Bacterial protease uses distinct thermodynamic signatures for substrate recognition

Bacterial protease uses distinct thermodynamic signatures for substrate recognition www.nature.com/scientificreports OPEN Bacterial protease uses distinct thermodynamic signatures for substrate recognition Received: 14 November 2016 1,6 2 3 4 Gustavo Arruda Bezerra , Yuko Ohara-Nemoto , Irina Cornaciu , Sofiya Fedosyuk , 3 3,7 3 2 Accepted: 2 May 2017 Guillaume Hoffmann , Adam Round , José A. Márquez , Takayuki K. Nemoto & Kristina 1,5 Published: xx xx xxxx Djinović-Carugo Porphyromonas gingivalis and Porphyromonas endodontalis are important bacteria related to periodontitis, the most common chronic inflammatory disease in humans worldwide. Its comorbidity with systemic diseases, such as type 2 diabetes, oral cancers and cardiovascular diseases, continues to generate considerable interest. Surprisingly, these two microorganisms do not ferment carbohydrates; rather they use proteinaceous substrates as carbon and energy sources. However, the underlying biochemical mechanisms of their energy metabolism remain unknown. Here, we show that dipeptidyl peptidase 11 (DPP11), a central metabolic enzyme in these bacteria, undergoes a conformational change upon peptide binding to distinguish substrates from end products. It binds substrates through an entropy-driven process and end products in an enthalpy-driven fashion. We show that increase in protein conformational entropy is the main-driving force for substrate binding via the unfolding of specific regions of the enzyme (“entropy reservoirs”). The relationship between our structural and thermodynamics data yields a distinct model for protein-protein interactions where protein conformational entropy modulates the binding free-energy. Further, our findings provide a framework for the structure-based design of specific DPP11 inhibitors. Periodontitis is the most common chronic inflammatory disease of humans worldwide, ae ff cting nearly half of 1, 2 adults in the United Kingdom and the United States of America . The condition is characterized by destruction of the connective tissue and alveolar bone surrounding the teeth and has many negative impacts in life quality , for instance, loss of permanent tooth. Porphyromonas gingivalis is the major causative agent in periodontitis and Porphyromonas endodontalis is another abundant bacterium in periodontal sites. Considerable attention has been drawn to these organisms due to recent reports associating periodontitis to systemic diseases like type II 7 8 9, 10 11 12 diabetes mellitus , rheumatoid arthritis , oral cancers , cardiovascular diseases , Alzheimer et al. and respira- tory diseases . In particular, P. gingivalis is a model pathogen for investigating microbial subversion in periodon- tal host immune response, which causes adverse impacts in systemic health . Both Porphyromonas species are Gram-negative black-pigmented anaerobes that do not ferment carbohy- drates; instead, they use proteinaceous substrates as carbon and energy source . Proteases with different spe- cificities reduce these extracellular proteins into di- and tri-peptides , which are further degraded via specific pathways, producing short-chain fatty acids, ammonia, acetate, propionate and butyrate . Together with other P. gingivalis elements such as the recently characterized pili , these metabolic end products are also virulence factors causing host tissue damage . In P. gingivalis, extracellular proteins are initially degraded to oligopep- tides by potent cysteine endopeptidases, i.e., gingipains R (Rgp, Arg-specific) and K (Kgp, Lys-specific) , mainly Department of Structural & Computational Biology, Max F. Perutz Laboratories, University of Vienna, Vienna Biocenter, Vienna Biocenter Campus 5, A-1030, Vienna, Austria. Department of Oral Molecular Biology, Course of Medical and Dental Sciences, Nagasaki University Graduate School of Biomedical Sciences, Nagasaki, 852-8588, Japan. European Molecular Biology Laboratory, Grenoble Outstation, 71 avenue des Martyrs, CS 90181, 38042, Grenoble, France. Max F. Perutz Laboratories, Medical University of Vienna, Vienna Biocenter, Dr. Bohr-Gasse 9/3, A-1030, Vienna, Austria. Department of Biochemistry, Faculty of Chemistry and Chemical Technology, University of Ljubljana, Večna pot 113, SI-1000, Ljubljana, Slovenia. Present address: Max F. Perutz Laboratories, Medical University of Vienna, Vienna Biocenter, Dr. Bohr-Gasse 9/3, A-1030, Vienna, Austria. Present address: European XFEL GmbH, Notkestraße 85, 22607, Hamburg, Germany. Correspondence and requests for materials should be addressed to G.A.B. (email: gustavo.bezerra@univie.ac.at) Scientific Repo R ts | 7: 2848 | DOI:10.1038/s41598-017-03220-y 1 www.nature.com/scientificreports/ localized in the outer membrane. Sequentially, the periplasmic enzymes, four dipeptidyl peptidases (DPPs), (i.e. DPP4, DPP5, DPP7 and DPP11), prolyl tripeptidyl peptidase-A and acylpeptidyl oligopeptidase convert the oli- 16 21 gopeptides to di- and tri-peptides , which are then incorporated via oligopeptide transporters . These enzymes’ die ff rent specificities and their concerted actions secure proper nutrient source and are essential for the bacteria metabolism. However, metabolic regulation for amino acid degradation is not well understood. Furthermore, these dipeptidases are widely distributed in the bacterial kingdom, including the two major phyla Bacteroidetes and Proteobacteria , thus it is of ample relevance to elucidate their mechanism of action. In P. gingivalis, the most utilized peptides contain Asp/Glu and are degraded by dipeptidyl peptidase 11 (DPP11), rendering it a central metabolic role in this microorganism . The metabolism of glutamate- and 23, 25 aspartate-containing peptides generates cytotoxic products , such as ammonia and butyrate, which may have a role in this bacterium to adversely impact systemic health. DPP11 is a dimeric 162 kDa (Supplementary Fig. S1) periplasmic serine protease (catalytic triad S652, D226 and H85) recently discovered in P. endodontalis and later identified in P. gingivalis by homology search (they share close to 58% identity). Due to its specificity for Asp/ Glu in the P1 position (second amino acid from the peptide N-terminus), DPP11 discovery is in line with the observation that aspartate and glutamate are the most intensively consumed amino acids in P. gingivalis . Indeed, P. gingivalis dpp11-knock-out strain shows growth impairment , suggesting its critical role in the bacterium energy metabolism. Its absence in mammals strengthens the enzyme’s potential as an attractive drug target. In this way, we aimed at elucidating the structural basis of peptide recognition by DPP11 in order to establish its mechanism of action. We determined the structures for the inactive constructs PgDPP11 S655A, PeDPP11 S652A and its 22-720 22-717 complexes with the dipeptides Arg-Asp and Arg-Glu, as well as the substrate Leu-Asp-Val-Trp, at 2.4, 2.85, 2.2, 2.1 and 2.6 Å resolution, referred to as PgDPP11, PeDPP11, PeDPP11:RD, PeDPP11:RE and PeDPP11:LDVWs, respectively (Table 1). DPP11 crystal structures in complex with peptides disclose a significant domain motion upon ligand binding and allow the elucidation of the enzyme’s specificity and selectivity. The distinct confor - mational states reported here oer o ff pportunities for the rational development of drugs and molecular tools for DPP11 studies, which are not possible to be fully exploited in the unbound form of the enzyme. Microcalorimetric analyse reveal a dual thermodynamic signature where DPP11 binds substrates through an endothermic/ entropy-driven process, and end products in an exothermic/enthalpy-driven fashion. We propose that increase in protein conformational entropy is the main-driving force for substrate recognition and that enzyme plasticity favours substrate promiscuity. Results and Discussion As previously reported , the overall fold of DPP11 comprises a bilobal architecture (Fig. 1a,b). The upper helical domain dictates the specificity of the enzyme and caps the catalytic domain, which has a typical chymotrypsin double β-barrel fold . PeDPP11 and PgDPP11 superposition yielded a root mean square deviation (r.m.s.d.) of 1.4 Å for 629 out of 685 superimposed Cα-atoms. A notable difference between the unbound PeDPP11 and its complexes with peptides is the conformational change bringing the helical and catalytic domains closer (Fig. 1b). This movement yields an approximate rotation of 22° of one domain relative to the other with a negligible transla- 29, 30 tional component . Notably, the helical domain undergoes larger structural changes reflected in higher r.m.s.d. and B-factor values, when compared to the catalytic domain, which behaves as a rigid body (Supplementary Table S1a,b). The active site of DPP11 lays in a wide cleft running through the middle of the protein between the cata- lytic and helical domains, which contributes to the formation of the substrate binding subsites (Supplementary Table S2a–c). The bound-peptide is anchored at its N-terminus primarily by N332 (N-anchor) located in the helical domain. It moves approximately 4.0 Å (Cα) towards the catalytic domain upon peptide binding (Supplementary Fig. S2a). The distance between the N-anchor and the catalytic S652 permits accommodation of only two amino acid residues, revealing how the enzyme acquires its dipeptidyl peptidase specificity (Figs.  1c,d). Evolutionary conserved R670 is responsible for the Asp/Glu specificity at subsite S1: its guanidinium group directly interacts with the substrate carboxyl group of Asp/Glu (Fig. 1c, Supplementary Fig. S2b). R670 and R336 confer a dominant positive charge to subsite S1 further explaining its P1 acidic specificity (Supplementary Fig. S2c). Indeed, the substitution R670D completely abolished PeDPP11 activity . In PeDPP11:LDVW, the third and fourth amino acids of the substrate (Val and Trp at positions P1′ and P2′, respectively) exhibit few interac- tions with the enzyme. For instance, Val (P1′) displays a weakly defined electron density with only 40% of its sol- vent accessible area buried by DPP11, while Trp (P2′) completely lacks electron density (Fig. 1d). This active site design renders the enzyme’s specificity more relaxed, with selectivity imposed mainly at P1 and P2 residues of the substrate. This promiscuous feature of DPP11 helps to provide nutrients for P. gingivalis and P. endodontalis given the scarce resources in the subgingival plaque . However, the strategy to increase enzyme promiscuity comes with a price: the affinities for substrates and end products are strikingly similar (Fig.  2). We performed a series of isothermal titration calorimetry experiments to further characterize peptide bind- ing to PeDPP11. Binding of LD and RD dipeptides/end products to PeDPP11 was largely exothermic (ΔH bind −1 of −22.0 and −15.5 kJ.mol , respectively) at 25 °C indicating an enthalpy-driven process [Fig. 2 (left panel), Supplementary Fig. S3a]. A favourable change in entropy due to water displacement caused by peptide binding and concomitant domain motion was observed (Fig. 1b). Analysis of PeDPP11:RD using Naccess revealed a large loss of solvent-accessible area upon peptide binding, approximately 1430 Å . In contrast, binding of the LDVW and LDL substrates was largely endothermic (ΔH of +23.8 and +17.0 kJ. bind −1 mol , respectively) at 25 °C indicating an entropy-driven process to overcome the unfavourable enthalpic con- tribution [Fig. 2 (right panel), Supplementary Fig. S3b]. The binding of peptides to PgDPP11 induces the dimer - ization of its monomeric population (Supplementary Fig. S4), masking the real thermodynamic contributions involved in the binding process. In this way, we focused our thermodynamic analysis solely on PeDPP11. Scientific Repo R ts | 7: 2848 | DOI:10.1038/s41598-017-03220-y 2 www.nature.com/scientificreports/ PgDPP11 PeDPP11 PeDPP11:RD PeDPP11:RE Data collection X-ray source BM14/ESRF ID30A-1/ESRF ID29/ESRF ID29/ESRF Space group P2 2 2 P2 2 2 P2 2 2 P2 2 2 1 1 1 1 1 1 1 1 1 1 1 1 Cell dimensions  a, b, c (Å) 103.18, 117.21, 148.35 76.75, 91.83, 229.91 111.81, 114.40, 147.82 111.44, 112.53, 148.26 Resolution (Å) 47.22–2.20 (2.32–2.20) 48.72–2.85 (3.00–2.85) 48.15–2.20 (2.32–2.20) 49.42–2.10 (2.21–2.10) R (%) 4.2 (57.9) 9.4 (46.8) 3.1 (37.9) 4.7 (38.3) pim R (%) 9.1 (122.4) 22.6 (111.8) 6.1 (74.4) 8.4 (69.6) merge CC1/2 (%) 99.8 (96.8) 98.9 (53.5) 99.9 (68.5) 99.6 (69.7) I / σ(I) 12.3 (0.9) 7.5 (1.7) 14.9 (2.0) 8.8 (2.0) Completeness (%) 99.7 (99.4) 100 (100) 99.6 (99.1) 99.0 (99.8) Redundancy 5.6 (5.4) 6.6 (6.6) 4.7 (4.6) 3.9 (4.0) Refinement Resolution (Å) 47.22–2.40 46.7–2.85 45.24–2.20 47.47–2.10 No. reflections 70637 38796 9612 107843 R / R (%) 20.7/25.9 24.0/27.4 18.1/22.6 18.9/22.9 work free No. atoms 11516 10832 11235 11093 Protein 11132 10773 10769 10549 Ligand/ion 23 3 40/5 42/2 Water 361 56 421 500 B–factors (A ) Protein 55.97 46.9 65.3 56.0/ Ligand/ion 72.93 20.78 47.6/62.6 44.6/51.326 Water 51.58 16.6 53.2 47.4 R.m.s. deviations Bond lengths (Å) 0.002 0.003 0.008 0.008 Bond angles (°) 0.680 0.763 1.141 1.094 Ramachandran analysis Favoured (%) 96 93.5 95.5 95.8 Allowed (%) 3.5 5.8 3.8 3.7 Outliers (%) 0.5 0.6 0.7 0.5 PeDPP11:LDVW PeDPP11:altconf FpDPP11:RD Data collection X–ray source ID30A–1/ESRF ID30A–1/ESRF ID23–1/ESRF Space group C2 C2 P2 Cell dimensions a, b, c (Å) 87.78, 113.33, 111.22 88.02, 103.99, 111.39 126.05, 70.68, 191.59 β =  106.2° β =  104.9º β =  97.3º Resolution (Å) 47.40–2.60 (2.74–2.60) 46.82–2.50 (2.64–2.50) 47.61-2.10 (2.21–2.10) R (%) 8.2 (75.4) 6.3 (43.8) 3.6 (43.2) pim R (%) 13.0 (121.1) 9.6 (67.4) 5.5 (66.6) merge CC1/2 (%) 99.2 (41.2) 99.5 (73.4) 99.9 (77.5) I / σ(I) 7.5 (1.0) 9.2 (1.5) 12.1 (1.7) Completeness (%) 99.2 (95.9) 99.0 (99.6) 98.9 (99.6) Redundancy 3.4 (3.5) 3.1 (3.0) 3.2 (3.2) Refinement Resolution (Å) 43.58–2.60 46.85–2.50 46.83–2.10 No. reflections 32005 33283 193404 R / R (%) 21.7/24.7 19.9/24.0 19.6/24.4 work free No. atoms 5490 5393 22036 Protein 5399 5240 21499 Ligand/ion 23/16 3 38/4 Water 52 150 495 B–factors (A ) Protein 51.98 48.5 65.5 Ligand/ion 43.29/90.86 58.5 70.3/58.7 Water 43.04 41.7 50.3 R.m.s. deviations Bond lengths (Å) 0.014 0.003 0.009 Bond angles (°) 1.119 0.705 1.149 Ramachandran analysis Favoured (%) 94 95.1 94.7 Allowed (%) 6 4.1 4.8 Outliers (%) 0 0.8 0.5 Table 1. Data collection and refinement statistics. Scientific Repo R ts | 7: 2848 | DOI:10.1038/s41598-017-03220-y 3 www.nature.com/scientificreports/ Figure 1. Structure of Porphyromonas endodontalis DPP11. (a) Domain architecture of PeDPP11. SP is signal peptide. The locations of catalytic triad amino acids are indicated by “red stars”. (b) Ribbon representation of PeDPP11 structure. Domains are coloured as in item (a) and helix α14 is shown in dark blue. Upper panel shows two perpendicular views of unbound PeDPP11. Lower panel shows two perpendicular views of PeDPP11 as in complex with peptides (binding pocket shown as yellow surface). (c) Active site of PeDPP11:RD (peptide RD shown in green). Catalytic triad is underlined. Note that S652 is mutated to alanine. (d) Active site of PeDPP11:LDVW (peptide LDVW shown in magenta), peptide omit map contoured at 3σ, shown in blue. Scientific Repo R ts | 7: 2848 | DOI:10.1038/s41598-017-03220-y 4 www.nature.com/scientificreports/ Figure 2. Microcalorimetric analysis. Isothermal titration calorimetry experiments performed by titrating LD (left panel) and LDVW (right panel) into PeDPP11. Upper panel shows time-dependent deflection of heat for each injection (top). Integrated calorimetric data for the respective interactions (bottom). The continuous curve represents the best fit using a one-site binding model. Lower panel shows the graphical representation of thermodynamics parameters. Next, we asked what governs the opposite thermodynamic signatures observed for DPP11 binding of end products and substrates. To address this question, we dissected the contributions of the three possible compo- nents influencing the binding energetics: solvent, ligand and the protein itself. The most apparent answer would point to hydrophobic effects, which is the release of well-ordered water molecules from interfaces to the bulk solvent, resulting in system’s entropy increase upon ligand binding . However, our crystal structures of DPP11 in complex with LDVW and dipeptide RD are both in closed conformation, excluding the possibility that solvent released from the protein’s cleft would explain the larger increase in entropy upon substrate binding. Then, we analysed the ligand’s contribution to the process. The presence of only one additional amino acid in the peptide −1 −1 LDL (ΔH of +17.0 kJ.mol ) compared to LD (ΔH of −22.0 kJ.mol ) results in the outstanding difference bind bind −1 of +39.0 kJ.mol in binding enthalpy. Due to the peptides similarity, energetic effects originating from the lig- ands alone do not suffice to explain the distinct thermodynamic binding forces reported. In light of the analyse Scientific Repo R ts | 7: 2848 | DOI:10.1038/s41598-017-03220-y 5 www.nature.com/scientificreports/ Figure 3. er Th modynamic analysis. (a) PeDPP11 binding to LD. (b) PeDPP11 binding to LDVW. Upper panels: Temperature dependence of ∆G, ∆H and −T∆S. Middle panel: Table with thermodynamic data derived from the ITC measurements at different temperatures. Lower panel: Entropy parameters estimations. Conformational entropy was calculated using the following equation: ∆S = ∆S − ∆S − ∆S . Where conf tot sol rt 65 −1 −1 66 ∆S = ∆Cp ln (298 K/385 K) and ∆S is estimated using the “cratic entropy” value of −33.3 J.mol. K . sol rt above, we concluded that the major contribution for the opposite thermodynamic signatures must arise from the protein itself, via changes in conformational entropy, as demonstrated below. In the free energy equation: ΔG = ΔH − TΔS , the total binding entropy (ΔS ) is deconvoluted into the tot tot tot tot sum of changes in ΔS (conformational entropy), ΔS (solvation entropy) and ΔS (rotational and transla- conf sol RT tional entropy) . Based on experimentally-measured heat capacity changes (ΔC ) for PeDPP11:LDVW (−1.6 kJ. −1 −1 −1 −1 mol .K ) and PeDPP11:LD (−3.3 kJ.mol .K ) interactions, we calculated a ΔS of +417.8 and +844.9 J. sol −1 −1 −1 −1 mol .K and a ΔS of −198.2 and −775.6 J.mol .K , respectively (Fig. 3). The data indicate that in both conf binding events the solvent provides a favourable contribution to the observed entropy and shows a 3.5-fold more prohibitive change in overall ΔS for PeDPP11 interaction with LD compared to LDVW. We propose that the conf −1 −1 +677.3 J.mol .K difference in Δ S is associated with the unfolding of DPP11 specific regions upon binding conf to LDVW. e h Th elical domain displays a high diversity of structural states across all solved structures in this work. When compared to unbound PeDPP11, the r.m.s.d. of the helical domain is 5-fold higher than that of the catalytic domain for PeDPP11:LDVW and 2-fold higher for PeDPP11:RD and PeDPP11:RE (Supplementary Table S1a). Particularly, the unfolding of helix α14 (residues 320–346) and loop F441-K451 upon LDVW binding corrobo- rates our hypothesis that protein conformational change is the determining factor for the opposed thermodynam- ics signatures observed upon peptide binding (Fig. 4a,b and Supplementary Fig. S5). We postulate that substrate binding leads to higher protein ΔS , which overcompensates for the unfavourable enthalpic contribution. conf Consistent with the measured endothermic binding, we propose that energy is absorbed from the solu- tion to break key interactions, such as those stabilizing loop F441-K451 and intra-main chain polar interac- tions that stabilize helix α14, but possibly in additional regions of the helical domain. These events permit the motion of the helical domain between different structural states leading to increased protein ΔS (Fig. 4c). conf 35 −1 −1 Usually, protein unfolding yields a positive ΔC , which explains the difference of + 1.7 kJ.mol .K in ΔC p p between PeDPP11:LDVW and PeDPP11:RD interactions. Upon DPP11-substrate binding, the increase in protein Scientific Repo R ts | 7: 2848 | DOI:10.1038/s41598-017-03220-y 6 www.nature.com/scientificreports/ Figure 4. PeDPP11 conformational changes. (a) Close-up view of the main PeDPP11 regions that unfold upon binding to LDVW, as observed in the crystal structures. (b) Loop F441-K451 region superposition of unbound PeDPP11 (blue), PeDPP11:LDVW (magenta, dashed line) and PeDPP11:RD (green). Unbound PeDPP11 is represented as ribbons and peptide binding pocket as yellow surface. (c) Cartoon representation depicting a DPP11 helix unfolding. Upon substrate binding, energy is absorbed from the solution to break polar contacts, which causes helix destabilization. In the disordered stage, the helix accesses different structural states, increasing system entropy. (d) Close-up view of the helix α14 missing region in PeDPP11 . Intra-main chain altconf polar contacts are indicated with orange dashed lines. conformational entropy counterbalances the overall entropic costs in protein-peptide interactions (including loss of protein and peptide degrees of freedom). Interestingly, we obtained an unbound PeDPP11 crystal form, called here PeDPP11 , which lacks electron density for helix α14, indicating its susceptibility to unfold (Fig. 4d). altconf Similar to PeDPP11 complexes, this structure is also closed (rotation angle of 27° of helical domain relative to the catalytic domain), illustrating the enzyme flexibility. Protein-peptide interactions oen o ft ccur in a way that minimizes the conformational changes of the protein partner, while maximizing their enthalpic potential via its packing and formation of hydrogen bonds (Fig. 5a). This strategy helps to decrease the entropic costs associated with the peptide loss of conformational entropy upon binding. The process can also be entropy-driven with the solvent providing the main driving-force, in this case, conformational flexibility may accompany peptide binding (Fig. 5b). Here, increased ΔS in DPP11 estab- conf lishes endothermic substrate binding via partial enzyme de-structuring associated with an increase in helical domain entropy, which acts as an “entropy reservoir” (Fig. 5c). DPP11 active site design displays stereochemical specificity only for P1 and P2 positions of the ligand. This arrangement favours substrate entropy-driven binding by limiting the enthalpic contributions of protein-peptide interaction (i.e. limiting the number of polar contacts) for only the two first amino acids of the incoming peptide. Additionally, our data also illustrate how conforma- tional plasticity enables enzyme promiscuity; for instance, by closing differently around different ligands . Due to experimental challenges, the role of conformational entropy in molecular recognition by proteins has begun to be elucidated only recently, mainly by nuclear magnetic resonance (NMR) relaxation methods . Using NMR techniques and molecular dynamics simulations, Veglia and colleagues observed in cAMP-dependent protein kinase A (PKA-C) a similar binding mode to that of DPP11. They showed that the substrate PLN 1-20 (phospholamban) binds to (PKA-C) in an entropically driven way, resulting in protein increased conformational dynamics. Conversely, binding of the inhibitor PKI (protein kinase inhibitor) to PKA-C is enthalpically driven 5-24 and stabilizes the protein, quenching the enzyme dynamics, which is important to prime the active for catalysis . e s Th tructural and thermodynamics data presented here provide a distinct model for protein-protein interac- tion, particularly in cases where increase in protein conformational entropy significantly contributes to the free energy of binding. Together with PKA-C, DPP11 binding mode may represent a general mechanism for biomo- lecular recognition, allowing the identification of proteins that share similar features and that have evolved to pro- miscuously bind numerous ligands. These findings further provide an innovative framework for structure-based Scientific Repo R ts | 7: 2848 | DOI:10.1038/s41598-017-03220-y 7 www.nature.com/scientificreports/ Figure 5. DPP11 conformational entropy in peptide binding. This cartoon illustrates two previously described models and DPP11 binding model reported in this work. (a) In the enthalpy-driven binding mode depicted, there are no major conformational changes and the active site is prearranged. The process is mainly governed by protein-peptide interactions, resulting in favourable enthalpy. (b) In this entropy-driven binding mode, the displacement of solvent molecules “entropy reservoir” provides the main driving-force for peptide-binding, and increases in system entropy outweighs the unfavourable enthalpy. In this case, peptide binding may be accompanied by protein conformational changes. (c) In DPP11 entropy-driven binding mode, protein conformational entropy is the main driving-force for substrate binding. De-structuring of parts of the helical domain “entropy reservoir” contributes to the increase in entropy necessary to compensate for the unfavourable enthalpy. drug design to develop compounds that target the “entropy reservoirs”. For instance, molecules able to prevent the unfolding of helix 14 and loop F441-K451 could display efficient inhibitor properties. Alternatively, it is also conceivable the identification of effectors that increase catalytic power by promoting protein dynamics. Methods Protein expression and purification. E. coli codon-optimized genes encoding for C-terminal 6xHis-tagged PeDPP11 S652A and PgDPP11 S655A in the pET-22b(+) vector (cloning sites 22-717 22-720 NdeI and XhoI) were purchased from GenScript (Piscataway, USA). The construct Flavobacterium p s y c hro p hi l u m DPP11 (called here FpDPP11 ) encoding the N-terminal fusion sequence 17-713 (MGGSHHHHHHGMASMTGGQQMGRDLYDDDDKDPTL) was cloned into the expression vector pTricHis. All plasmids were transformed in BL21(DE3)pLysS. The cells were grown in LB-medium containing −1 −1 100 μg ml ampicillin and 34 μg ml chloramphenicol. Aer 3 ft h at 37 °C, temperature was reduced to 30 °C and protein expression was induced by the addition of 0.5 mM isopropyl-1-thio-D-galactopyranoside (IPTG). Cells were then allowed to grow for 4 h and were harvested by centrifugation at 4,000 g for 10 minutes. For protein purification, cells were resuspended in 50 mM Hepes-NaOH pH 8.0, 150 mM NaCl. Cell debris was removed by centrifugation at 25,000 g for 45 minutes at 4 °C, and the supernatant was subjected to affinity chromatography on TM 5 ml HisTrap (GE Healthcare) equilibrated with lysis buffer. Bound protein was eluted in lysis buffer containing 500 mM imidazole. Further purification was performed by size exclusion chromatography (SEC) on a HiLoad 26/60 Superdex 200 (GE Healthcare) column previously equilibrated with 10 mM Hepes-NaOH pH 7.4, 100 mM NaCl. Purified protein was concentrated using 20 ml concentrators with an appropriate molecular weight cut-off (Vivaspin 50,000 MWCO, Sartorius). Crystallization. To enhance the crystallizability of PgDPP11 and PeDPP11, truncated forms of the enzymes were designed lacking the first 21 amino acid residues (called here PgDPP11 and PeDPP11 ) which 22-720 22-717 were predicted to be signal peptides . The following crystallization trials used the nanodrop-dispensing robot Scientific Repo R ts | 7: 2848 | DOI:10.1038/s41598-017-03220-y 8 www.nature.com/scientificreports/ (Phoenix RE; Rigaku Europe) employing the sitting drop vapour diffusion technique by mixing equal volumes of protein (200 nl) and reservoir solutions (200 nl) at 20 °C in a 96-well Intelli-Plate (ArtRobbins Instruments ). All crystals were cryoprotected in a solution consisting of reservoir solution supplemented with 20% glycerol before flash-cooling in liquid nitrogen. X-ray diffraction data were collected at 100 K. −1 42 • PgDPP11 S655A (PgDPP11) was crystallized at 10 mg ml using the Morpheus screen condition 22-720 D11: 0.12 M alcohols, buffer system 3 pH 8.5, 40% v/v glycerol, 20% w/v PEG 4000. −1 • PeDPP11 S652A (PeDPP11:RD) at 10 mg ml was incubated with 1.2 mM dipeptide Arg-Asp on ice 22-717 for 15 minutes. Crystals were obtained in the Morpheus screen condition E10: 0.12 M ethylene glycols, 0.1 M buffer system 3 pH 8.5, 40% v/v ethylene glycol, 20% w/v PEG 8000. −1 • PeDPP11 S652A (PeDPP11:RE) at 10 mg ml was incubated with 1.2 mM dipeptide Arg-Glu on ice 22-717 for 15 minutes. Crystals were obtained in the Morpheus screen condition F12: 0.12 M monosacharides, 0.1 M buffer system 3 pH 8.5, 25% v/v MPD, 25% PEG 1000, 25% w/v PEG 3350. −1 • FpDPP11 was crystallized at 10 mg ml in the PACT Premier screen (Molecular Dimensions ) condi- 17-713 tion G2: 0.2 M NaBr, 0.1 M bistris propane, pH 7.5, 20% PEG 3350. e Th following crystallization trials used the sitting-drop vapour diffusion method and were conducted at the High Throughput Crystallization Laboratory of the EMBL Grenoble Outstation (https://embl.fr/htxlab) . Drops of 100 nl sample and 100 nl crystallization solution were set up in CrystalDirect plates (MiTeGen, Ithaca, USA) with a Cartesian PixSys robot (Cartesian Technologies, Irvine, USA). e Th experiments were incubated at 20 °C in a RockImager system (Formulatrix Inc., Bedford, USA). Automated high-throughput crystal cryo-cooling and TM 44 harvesting were performed with CrystalDirect Technology as described by Zander et al., 2016 . Crystals were stored in liquid nitrogen for data collection. Data collection was done in a fully automated fashion at MASSIF-1, ESRF . X-ray diffraction data were collected at 100 K. −1 • PeDPP11 S652A (unbound) was crystallized at 10 mg ml initially in the condition D11 of ProComplex 22-717 screen (Qiagen ). The condition was further optimized to 0.1 M Tris-HCl pH 7.5, 15% PEG 6000. • PeDPP11 S652A in the alternate conformation (PeDPP11 ) was crystallized initially in the con- 22-717 altconf dition B2 of ProComplex screen (Qiagen ): 0.1 M calcium acetate, 10% w/v PEG 4000, 0.1 M sodium acetate pH 4.5. The condition was further optimized to: 0.1 M calcium acetate, 15% w/v PEG 4000, 0.1 M sodium acetate pH 5.0. −1 • PeDPP11 S652A (PeDPP11:LDVW) at 22 mg ml was incubated for 30 minutes on ice with 1.0 mM 22-717 Leu-Asp-Val-Trp. Crystals were initially obtained in condition D11 of ProComplex screen (Qiagen ). The condition was further optimized to 0.1 M Tris-HCl pH 7.5, 15% PEG 6000. Structure determination. Our initial attempts to solve the structure by molecular replacement using the coordinates of dipeptidyl aminopeptidase BII (DAP BII) from Pseudoxanthomonas mexicana WO24, the closest homologue (37% identity) with known 3D structure, were ineffective. We suspected that different conformations adopted by the protein in the crystal could be rendering molecular replacement trials unsuccessful. So, a DPP11 homologue (37% identical to PgDPP11) from FpDPP11 was employed to grow monoclinic crystals (space 17-713 group P2 ). FpDPP11 structure was then determined by molecular replacement using the coordinates of 1 17-713 DAP BII (PDB code: 3WOJ, 27% identity). Subsequently, FpDPP11 structure was successfully used as tem- 17-713 plate to solve the structures of PgDPP11 S655A and PeDPP11 S652A (Supplementary Fig. S6). Two 22-720 22-717 out of four subunits in the crystal asymmetric unit of FpDPP11 were in complex with Arg-Asp, a dipeptide 17-713 co-purified from E. coli . This finding led us to grow co-crystals of PeDPP11 S652A in complex with dipep- 22-717 tides Arg-Asp and Arg-Glu. 47 48 49 Data were processed with XDS , Scala and Pointless . All structures were solved by molecular replacement 50 51 52 53 with PHASER , refined with PHENIX and manually adjusted in COOT . R -values were computed from free 5% randomly chosen reflections not used for the refinement. The structure stereochemistry was checked using Molprobity . Details of data collection and refinement statistics are provided in Table  1. Peptide omit maps are depicted in Supplementary Fig. S7. All figures were prepared using the program PyMOL (http://www.pymol.org). 55, 56 Poisson-Boltzmann calculations were performed using the software APBS . Isothermal Titration Calorimetry. All experiments were carried out in 10 mM Hepes-NaOH pH 7.4, 100 mM NaCl. Both the enzymes and the peptides were dissolved in the same buffer. e Th bindings were analysed TM with a MicroCal iTC microcalorimeter (GE Healthcare, Life Sciences) equilibrated at the respective tempera- ture. Typically, a total of one aliquot of 0.4 μl and 19 aliquots of 2.0 μl of the peptide solution were injected at a rate of 0.5 μl/s into 200 μl of the protein solution under constant stirring at 750 rpm at the specified concentrations. The following titrations were performed: 600 μM LDVW to 60 μM PeDPP11, 810 μM LD to 75 μM PeDPP11 (at 10 °C, 1.28 mM LD to 120 μM PeDPP11 was employed), 1 mM of LDL to 75 μM PeDPP11, 1.0 mM of RD to 80 μM M PeDPP11. As a control to exclude buffer-dependent effects, we additionally performed the binding of 600 μM LDVW to 60 μM PeDPP11 and 600 μM LD to 75 μM PeDPP11 in 50 mM sodium phosphate pH 7.4 with 100 mM NaCl (Supplementary Fig. S8). Every injection was carried out over a period of 4 s with a spacing of 110 s between the injections. The corresponding heats of binding were determined by integrating the observed peaks after correcting for the heat dilution of the peptide determined in a reference measurement (peptide injected into buffer). These corrected values were plotted against the ratio peptide vs. protein concentration in the cell to generate the binding isotherm. Nonlinear least-squares fitting using Origin version 7.0 (Microcal) was used to obtain the association constants (K ), heats of binding (ΔH) and stoichiometries. K and Gibbs free energy (ΔG) a d were calculated according to: K = 1/K and ΔG = −RT ln K = RT ln K . The reported values are averages of at d a a d Scientific Repo R ts | 7: 2848 | DOI:10.1038/s41598-017-03220-y 9 www.nature.com/scientificreports/ least two independent measurements. The stoichiometry obtained in all experiments is within the range 0.7–1.1, which is in agreement with the crystal structures (stoichiometry 1). Small-angle X-ray scattering (SAXS) data collection and analysis. SAXS experiments were per- formed at 0.9918 Å wavelength ESRF at BioSAXS beamline BM29 (Grenoble, France) equipped with PILATUS 57 −1 −1 1 M . The detector distance was set at 2.864 m. The range of scattering vector 0.03 nm < q < 4.5 nm was cov- −1 ered. For PeDPP11 S652A, the data were collected using the following protein concentrations: 1.1 mg ml , 22-717 −1 −1 −1 1.9 mg ml , 9.62 mg ml and 16.37 mg ml . e s Th amples were in a buffer containing 10 mM Hepes-NaOH pH 7.4, 100 mM NaCl, and the measurements were performed at 20 °C. The automated sample changer was employed to load the samples and constantly remove the irradiated sample. Twenty successive exposures of 1 second were collected and compared to detect and discard possible radiation damage ee ff cts. For PgDPP11 S655A, on-line HPLC-mode was used with a 22-720 TM Superdex 200 10/300 GL column (GE Healthcare), and SAXS data was recorded directly on the sample eluted. −1 −1 A 0.5 ml min flow was used. The protein concentration applied onto the column was 50 mg ml . The data were processed and analyzed using the online analysis pipeline . Subsequent manual processing was done with the ATSAS 2.6 program package . The forward scattering I(0) and the radius of gyration Rg were 61, 62 extracted from the Guinier approximation calculated with the AutoRG function within PRIMUS . e m Th axi- mum particle dimension Dmax and P(r) function were evaluated using the program GNOM . For PeDPP11 22-717 S652A, the analysis of SAXS data by Guinier approximation showed no concentration dependence effect, indi- cating the samples were homogeneous and free of aggregation. For this construct, SAXS analyses were performed by merging data from all concentrations measured. For both PeDPP11 S652A and PgDPP11 S655A, 22-717 22-720 the theoretical scattering from the crystallographic structures was calculated using the program CRYSOL and compared with the respective scattering profiles. Size exclusion chromatography followed by Multiangle Laser Light Scattering. To assess the TM oligomeric state and molecular weight, the samples were applied onto a Superdex 200 10/300 GL column (GE Healthcare) at the respective concentrations, using a flow of 0.5 ml/min. The column was connected to a miniDAWN Tristar light scattering instrument (Wyatt Technologies, Santa Barbara, CA) and pre-equilibrated with 10 mM Hepes-NaOH pH 7.4, 100 mM NaCl. Data analysis was performed using the manufacturer’s software ASTRA. Hydrolyzing activity toward MCA-dipeptides. Purification of recombinant active forms of PgDPP11 and PeDPP11 was performed according to Ohara-Nemoto et al. . PgDPP11 and PeDPP11 (2-20 ng) were used for measurement of dipeptidyl peptidase activity in 200 μl of reaction solution composed of 50 mM sodium phosphate pH 7.0 and 5 mM EDTA. The reaction was started with an addition of 20 μM Leu-Asp-, Arg-Asp or Leu-Glu-MCA and continued at 37 °C for 30 min (Supplementary Fig. S9). Fluorescence intensity was measured with excitation at 380 nm and emission at 460 nm with a Fluorescence Photometer F-4000 (Hitachi). References 1. Eke, P. I. et al. Update on Prevalence of Periodontitis in Adults in the United States: NHANES 2009 to 2012. J. Periodontol. 86, 611–622 (2015). 2. Batchelor, P. Is periodontal disease a public health problem? Br. Dent. J. 217, 405–409 (2014). 3. Chee, B., Park, B. & Bartold, P. M. Periodontitis and type II diabetes: a two-way relationship. Int. J. Evid. Based Healthc. 11, 317–329 (2013). 4. Hajishengallis, G., Darveau, R. P. & Curtis, M. A. The keystone-pathogen hypothesis. Nat. Rev. Microbiol. 10, 717–725 (2012). 5. Lombardo Bedran, T. B. et al. Porphyromonas endodontalis in chronic periodontitis: a clinical and microbiological cross-sectional study. J. Oral Microbiol. 4, 10123 (2012). 6. Wang, J. & Jia, H. Metagenome-wide association studies: fine-mining the microbiome. Nat. Rev. Microbiol. 14, 508–522 (2016). 7. Lalla, E. & Papapanou, P. N. Diabetes mellitus and periodontitis: a tale of two common interrelated diseases. Nat. Rev. Endocrinol. 7, 738–748 (2011). 8. Lundberg, K., Wegner, N., Yucel-Lindberg, T. & Venables, P. J. Periodontitis in RA-the citrullinated enolase connection. Nat. Rev. Rheumatol. 6, 727–730 (2010). 9. Javed, F. & Warnakulasuriya, S. Is there a relationship between periodontal disease and oral cancer? A systematic review of currently available evidence. Crit. Rev. Oncol. Hematol. 97, 197–205 (2015). 10. Ha, N. H. et al. Prolonged and repetitive exposure to Porphyromonas gingivalis increases aggressiveness of oral cancer cells by promoting acquisition of cancer stem cell properties. Tumour Biol. 36, 9947–9960 (2015). 11. Ruiz, I. F. Risk factors: Periodontitis increases risk of a first MI. Nat. Rev. Cardiol. 13, 124 (2016). 12. Farhad, S. Z. et al. The effect of chronic periodontitis on serum levels of tumor necrosis factor-alpha in Alzheimer disease. Dent. Res. J. (Isfahan) 11, 549–552 (2014). 13. Paju, S. & Scannapieco, F. A. Oral biofilms, periodontitis, and pulmonary infections. Oral Dis. 13, 508–512 (2007). 14. Hajishengallis, G. Periodontitis: from microbial immune subversion to systemic inflammation. Nat. Rev. Immunol. 15, 30–44 (2015). 15. Rouf, S. M. et al. Phenylalanine 664 of dipeptidyl peptidase (DPP) 7 and Phenylalanine 671 of DPP11 mediate preference for P2- position hydrophobic residues of a substrate. FEBS Open Bio 3, 177–183 (2013). 16. Nemoto, T. K. & Ohara-Nemoto, Y. Exopeptidases and gingipains in Porphyromonas gingivalis as prerequisites for its amino acid metabolism. Jpn. Dent. Sci. Rev. 52, 22–29 (2016). 17. Takahashi, N. Oral Microbiome Metabolism: From “Who Are They?” to “What Are They Doing?”. J. Dent. Res. 94, (1628–1637 (2015). 18. Xu, Q. et al. A Distinct Type of Pilus from the Human Microbiome. Cell 165, 690–703 (2016). 19. Holt, S. C., Kesavalu, L., Walker, S. & Genco, C. A. Virulence factors of Porphyromonas gingivalis. Periodontol. 2000 20, 168–238 (1999). 20. de Diego, I. et al. Structure and mechanism of cysteine peptidase gingipain K (Kgp), a major virulence factor of Porphyromonas gingivalis in periodontitis. J. Biol. Chem. 289, 32291–32302 (2014). 21. Nelson, K. E. et al. Complete genome sequence of the oral pathogenic Bacterium porphyromonas gingivalis strain W83. J. Bacteriol. 185, 5591–5601 (2003). Scientific Repo R ts | 7: 2848 | DOI:10.1038/s41598-017-03220-y 10 www.nature.com/scientificreports/ 22. Ohara-Nemoto, Y. et al. Identification and characterization of prokaryotic dipeptidyl-peptidase 5 from Porphyromonas gingivalis. J. Biol. Chem. 289, 5436–5448 (2014). 23. Takahashi, N., Sato, T. & Yamada, T. Metabolic pathways for cytotoxic end product formation from glutamate- and aspartate- containing peptides by Porphyromonas gingivalis. J. Bacteriol. 182, 4704–4710 (2000). 24. Ohara-Nemoto, Y. et al. Asp- and Glu-specific novel dipeptidyl peptidase 11 of Porphyromonas gingivalis ensures utilization of proteinaceous energy sources. J. Biol. Chem. 286, 38115–38127 (2011). 25. Kurita-Ochiai, T. et al. Butyric acid induces apoptosis in inflamed fibroblasts. J. Dent. Res. 87, 51–55 (2008). 26. Rouf, S. M. et al. Discrimination based on Gly and Arg/Ser at position 673 between dipeptidyl-peptidase (DPP) 7 and DPP11, widely distributed DPPs in pathogenic and environmental gram-negative bacteria. Biochimie 95, 824–832 (2013). 27. Sakamoto, Y. et al. Structural and mutational analyses of dipeptidyl peptidase 11 from Porphyromonas gingivalis reveal the molecular basis for strict substrate specificity. Sci. Rep. 5, 11151 (2015). 28. Polgar, L. The catalytic triad of serine peptidases. Cell. Mol. Life Sci. 62, 2161–2172 (2005). 29. Hayward, S. & Berendsen, H. J. Systematic analysis of domain motions in proteins from conformational change: new results on citrate synthase and T4 lysozyme. Proteins 30, 144–154 (1998). 30. Hayward, S. & Lee, R. A. Improvements in the analysis of domain motions in proteins from conformational change: DynDom version 1.50. J. Mol. Graph. Model. 21, 181–183 (2002). 31. Kuboniwa, M. & Lamont, R. J. Subgingival biofilm formation. Periodontol. 2000 52, 38–52 (2010). 32. Naccess 2.1.1 v. 2.1.1 (Department of Biochemistry and Molecular Biology, University College, London., 1996). 33. Biela, A. et al. Ligand bindin g stepwise disrupts water network in thrombin: enthalpic and entropic changes reveal classical hydrophobic effect. J. Med. Chem. 55, 6094–6110 (2012). 34. Marlow, M. S., Dogan, J., Frederick, K. K., Valentine, K. G. & Wand, A. J. The role of conformational entropy in molecular recognition by calmodulin. Nat. Chem. Biol. 6, 352–358 (2010). 35. Prabhu, N. V. & Sharp, K. A. Heat capacity in proteins. Annu. Rev. Phys. Chem. 56, 521–548 (2005). 36. London, N., Movshovitz-Attias, D. & Schueler-Furman, O. The structural basis of peptide-protein binding strategies. Structure 18, 188–199 (2010). 37. Bezerra, G. A. et al. Entropy-driven binding of opioid peptides induces a large domain motion in human dipeptidyl peptidase III. Proc. Natl. Acad. Sci. USA 109, 6525–6530 (2012). 38. Nobeli, I., Favia, A. D. & Thornton, J. M. Protein promiscuity and its implications for biotechnology. Nat. Biotechnol. 27, 157–167 (2009). 39. Frederick, K. K., Marlow, M. S., Valentine, K. G. & Wand, A. J. Conformational entropy in molecular recognition by proteins. Nature 448, 325–329 (2007). 40. Masterson, L. R. et al. Dynamically committed, uncommitted, and quenched states encoded in protein kinase A revealed by NMR spectroscopy. Proc. Natl. Acad. Sci. USA 108, 6969–6974 (2011). 41. Slabinski, L. et al. XtalPred: a web server for prediction of protein crystallizability. Bioinformatics 23, 3403–3405 (2007). 42. Gorrec, F. The MORPHEUS protein crystallization screen. J. App. Cryst. 42, 1035–1042 (2009). 43. Dimasi, N., Flot, D., Dupeux, F. & Marquez, J. A. Expression, crystallization and X-ray data collection from microcrystals of the extracellular domain of the human inhibitory receptor expressed on myeloid cells IREM-1. Acta Crystallogr. Sect. F Struct. Biol. Cryst. Commun. 63, 204–208 (2007). 44. Zander, U. et al. Automated harvesting and processing of protein crystals through laser photoablation. Acta Cryst. D 72, 454–466 (2016). 45. Svensson, O., Malbet-Monaco, S., Popov, A., Nurizzo, D. & Bowler, M. W. Fully automatic characterization and data collection from crystals of biological macromolecules. Acta Cryst. D 71, 1757–1767 (2015). 46. Sakamoto, Y. et al. S46 peptidases are the first exopeptidases to be members of clan PA. Sci. Rep. 4, 4977 (2014). 47. Kabsch, W. X. Acta Cryst. D 66, 125–132 (2010). 48. Evans, P. Scaling and assessment of data quality. Acta Cryst. D 62, 72–82 (2006). 49. Evans, P. R. An introduction to data reduction: space-group determination, scaling and intensity statistics. Acta Cryst. D 67, 282–292 (2011). 50. McCoy, A. J. et al. Phaser crystallographic software. J. App. Cryst. 40, 658–674 (2007). 51. Adams, P. D. et al. The Phenix software for automated determination of macromolecular structures. Methods 55, 94–106 (2011). 52. Emsley, P. & Cowtan, K. Coot: model-building tools for molecular graphics. Acta Cryst. D 60, 2126–2132 (2004). 53. Kleywegt, G. J. & Brunger, A. T. Checking your imagination: applications of the free R value. Structure 4, 897–904 (1996). 54. Chen, V. B. et al. MolProbity: all-atom structure validation for macromolecular crystallography. Acta Cryst. D 66, 12–21 (2010). 55. Baker, N. A., Sept, D., Joseph, S., Holst, M. J. & McCammon, J. A. Electrostatics of nanosystems: application to microtubules and the ribosome. Proc. Natl. Acad. Sci. USA 98, 10037–10041 (2001). 56. Dolinsky, T. J., Nielsen, J. E., McCammon, J. A. & Baker, N. A. PDB2PQR: an automated pipeline for the setup of Poisson-Boltzmann electrostatics calculations. Nucleic Acids Res. 32, W665–667 (2004). 57. Pernot, P. et al. Upgraded ESRF BM29 beamline for SAXS on macromolecules in solution. J. Synchrotron Rad. 20, 660–664 (2013). 58. Round, A. et al. BioSAXS Sample Changer: a robotic sample changer for rapid and reliable high-throughput X-ray solution scattering experiments. Acta Cryst. D 71, 67–75 (2015). 59. Brennich, M. E. et al. Online data analysis at the ESRF bioSAXS beamline, BM29. J. App. Cryst. 49, 203–212 (2016). 60. Petoukhov, M. V. et al. New developments in the program package for small-angle scattering data analysis. J. App. Cryst. 45, 342–350 (2012). 61. Petoukhov, M. V., Konarev, P. V., Kikhney, A. G. & Svergun, D. I. ATSAS 2.1 - towards automated and web-supported small-angle scattering data analysis. J. App. Cryst. 40, s223–s228 (2007). 62. Konarev, P. V., Volkov, V. V., Sokolova, A. V., Koch, M. H. J. & Svergun, D. I. PRIMUS: a Windows PC-based system for small-angle scattering data analysis. J. App. Cryst. 36, 1277–1282 (2003). 63. Svergun, D. Determination of the regularization parameter in indirect-transform methods using perceptual criteria. J. App. Cryst. 25, 495–503 (1992). 64. Svergun, D., Barberato, C. & Koch, M. H. J. CRYSOL - a Program to Evaluate X-ray Solution Scattering of Biological Macromolecules from Atomic Coordinates. J. App. Cryst. 28, 768–773 (1995). 65. Baldwin, R. L. Temperature dependence of the hydrophobic interaction in protein folding. Proc. Natl. Acad. Sci. USA 83, 8069–8072 (1986). 66. Murphy, K. P., Xie, D., o Th mpson, K. S., Amzel, L. M. & Freire, E. Entropy in biological binding processes: estimation of translational entropy loss. Proteins 18, 63–67 (1994). Acknowledgements GAB was supported by the Vienna International Postdoctoral Program (VIPS). KDC research was supported by a research grant of the Austrian Academy of Sciences (#P25353-B21). This study was supported by JSPS KAKENHI, Grant Nos 15K11047 to T.K.N., 16K11481 to Y.O.-N.). We are grateful to the beam line staff at European Synchrotron Radiation Facility (ESRF) for support during diffraction data collection. Scientific Repo R ts | 7: 2848 | DOI:10.1038/s41598-017-03220-y 11 www.nature.com/scientificreports/ Author Contributions G.A.B., Y.O.N., S.F., I.C., G.H. and T.K.N. conceived and performed experiments; G.A.B. performed structure determination and ITC measurements; G.A.B., Y.O.N., S.F., A.R., I.C., J.A.M., T.K.N., K.D.C. analysed the data; G.A.B. wrote the manuscript with comments from all authors. Additional Information Supplementary information accompanies this paper at doi:10.1038/s41598-017-03220-y Competing Interests: The authors declare that they have no competing interests. Accession codes: The model coordinates and structure factors have been deposited in the Protein Data Bank (PDB) under accession numbers 5JWF(PgDPP11), 5JXK(PeDPP11), 5JWG(PeDPP11:RD), 5JWI(PeDPP11:RE), 5JY0(PeDPP11:LDVW), 5JXP(PeDPP11:altconf ) and 5JXF(FpDPP11:RD). Publisher's note: Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Cre- ative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not per- mitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/. © The Author(s) 2017 Scientific Repo R ts | 7: 2848 | DOI:10.1038/s41598-017-03220-y 12 http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Scientific Reports Springer Journals

Bacterial protease uses distinct thermodynamic signatures for substrate recognition

Free
12 pages
Loading next page...
 
/lp/springer_journal/bacterial-protease-uses-distinct-thermodynamic-signatures-for-lquXO8qpha
Publisher
Nature Publishing Group UK
Copyright
Copyright © 2017 by The Author(s)
Subject
Science, Humanities and Social Sciences, multidisciplinary; Science, Humanities and Social Sciences, multidisciplinary; Science, multidisciplinary
eISSN
2045-2322
D.O.I.
10.1038/s41598-017-03220-y
Publisher site
See Article on Publisher Site

Abstract

www.nature.com/scientificreports OPEN Bacterial protease uses distinct thermodynamic signatures for substrate recognition Received: 14 November 2016 1,6 2 3 4 Gustavo Arruda Bezerra , Yuko Ohara-Nemoto , Irina Cornaciu , Sofiya Fedosyuk , 3 3,7 3 2 Accepted: 2 May 2017 Guillaume Hoffmann , Adam Round , José A. Márquez , Takayuki K. Nemoto & Kristina 1,5 Published: xx xx xxxx Djinović-Carugo Porphyromonas gingivalis and Porphyromonas endodontalis are important bacteria related to periodontitis, the most common chronic inflammatory disease in humans worldwide. Its comorbidity with systemic diseases, such as type 2 diabetes, oral cancers and cardiovascular diseases, continues to generate considerable interest. Surprisingly, these two microorganisms do not ferment carbohydrates; rather they use proteinaceous substrates as carbon and energy sources. However, the underlying biochemical mechanisms of their energy metabolism remain unknown. Here, we show that dipeptidyl peptidase 11 (DPP11), a central metabolic enzyme in these bacteria, undergoes a conformational change upon peptide binding to distinguish substrates from end products. It binds substrates through an entropy-driven process and end products in an enthalpy-driven fashion. We show that increase in protein conformational entropy is the main-driving force for substrate binding via the unfolding of specific regions of the enzyme (“entropy reservoirs”). The relationship between our structural and thermodynamics data yields a distinct model for protein-protein interactions where protein conformational entropy modulates the binding free-energy. Further, our findings provide a framework for the structure-based design of specific DPP11 inhibitors. Periodontitis is the most common chronic inflammatory disease of humans worldwide, ae ff cting nearly half of 1, 2 adults in the United Kingdom and the United States of America . The condition is characterized by destruction of the connective tissue and alveolar bone surrounding the teeth and has many negative impacts in life quality , for instance, loss of permanent tooth. Porphyromonas gingivalis is the major causative agent in periodontitis and Porphyromonas endodontalis is another abundant bacterium in periodontal sites. Considerable attention has been drawn to these organisms due to recent reports associating periodontitis to systemic diseases like type II 7 8 9, 10 11 12 diabetes mellitus , rheumatoid arthritis , oral cancers , cardiovascular diseases , Alzheimer et al. and respira- tory diseases . In particular, P. gingivalis is a model pathogen for investigating microbial subversion in periodon- tal host immune response, which causes adverse impacts in systemic health . Both Porphyromonas species are Gram-negative black-pigmented anaerobes that do not ferment carbohy- drates; instead, they use proteinaceous substrates as carbon and energy source . Proteases with different spe- cificities reduce these extracellular proteins into di- and tri-peptides , which are further degraded via specific pathways, producing short-chain fatty acids, ammonia, acetate, propionate and butyrate . Together with other P. gingivalis elements such as the recently characterized pili , these metabolic end products are also virulence factors causing host tissue damage . In P. gingivalis, extracellular proteins are initially degraded to oligopep- tides by potent cysteine endopeptidases, i.e., gingipains R (Rgp, Arg-specific) and K (Kgp, Lys-specific) , mainly Department of Structural & Computational Biology, Max F. Perutz Laboratories, University of Vienna, Vienna Biocenter, Vienna Biocenter Campus 5, A-1030, Vienna, Austria. Department of Oral Molecular Biology, Course of Medical and Dental Sciences, Nagasaki University Graduate School of Biomedical Sciences, Nagasaki, 852-8588, Japan. European Molecular Biology Laboratory, Grenoble Outstation, 71 avenue des Martyrs, CS 90181, 38042, Grenoble, France. Max F. Perutz Laboratories, Medical University of Vienna, Vienna Biocenter, Dr. Bohr-Gasse 9/3, A-1030, Vienna, Austria. Department of Biochemistry, Faculty of Chemistry and Chemical Technology, University of Ljubljana, Večna pot 113, SI-1000, Ljubljana, Slovenia. Present address: Max F. Perutz Laboratories, Medical University of Vienna, Vienna Biocenter, Dr. Bohr-Gasse 9/3, A-1030, Vienna, Austria. Present address: European XFEL GmbH, Notkestraße 85, 22607, Hamburg, Germany. Correspondence and requests for materials should be addressed to G.A.B. (email: gustavo.bezerra@univie.ac.at) Scientific Repo R ts | 7: 2848 | DOI:10.1038/s41598-017-03220-y 1 www.nature.com/scientificreports/ localized in the outer membrane. Sequentially, the periplasmic enzymes, four dipeptidyl peptidases (DPPs), (i.e. DPP4, DPP5, DPP7 and DPP11), prolyl tripeptidyl peptidase-A and acylpeptidyl oligopeptidase convert the oli- 16 21 gopeptides to di- and tri-peptides , which are then incorporated via oligopeptide transporters . These enzymes’ die ff rent specificities and their concerted actions secure proper nutrient source and are essential for the bacteria metabolism. However, metabolic regulation for amino acid degradation is not well understood. Furthermore, these dipeptidases are widely distributed in the bacterial kingdom, including the two major phyla Bacteroidetes and Proteobacteria , thus it is of ample relevance to elucidate their mechanism of action. In P. gingivalis, the most utilized peptides contain Asp/Glu and are degraded by dipeptidyl peptidase 11 (DPP11), rendering it a central metabolic role in this microorganism . The metabolism of glutamate- and 23, 25 aspartate-containing peptides generates cytotoxic products , such as ammonia and butyrate, which may have a role in this bacterium to adversely impact systemic health. DPP11 is a dimeric 162 kDa (Supplementary Fig. S1) periplasmic serine protease (catalytic triad S652, D226 and H85) recently discovered in P. endodontalis and later identified in P. gingivalis by homology search (they share close to 58% identity). Due to its specificity for Asp/ Glu in the P1 position (second amino acid from the peptide N-terminus), DPP11 discovery is in line with the observation that aspartate and glutamate are the most intensively consumed amino acids in P. gingivalis . Indeed, P. gingivalis dpp11-knock-out strain shows growth impairment , suggesting its critical role in the bacterium energy metabolism. Its absence in mammals strengthens the enzyme’s potential as an attractive drug target. In this way, we aimed at elucidating the structural basis of peptide recognition by DPP11 in order to establish its mechanism of action. We determined the structures for the inactive constructs PgDPP11 S655A, PeDPP11 S652A and its 22-720 22-717 complexes with the dipeptides Arg-Asp and Arg-Glu, as well as the substrate Leu-Asp-Val-Trp, at 2.4, 2.85, 2.2, 2.1 and 2.6 Å resolution, referred to as PgDPP11, PeDPP11, PeDPP11:RD, PeDPP11:RE and PeDPP11:LDVWs, respectively (Table 1). DPP11 crystal structures in complex with peptides disclose a significant domain motion upon ligand binding and allow the elucidation of the enzyme’s specificity and selectivity. The distinct confor - mational states reported here oer o ff pportunities for the rational development of drugs and molecular tools for DPP11 studies, which are not possible to be fully exploited in the unbound form of the enzyme. Microcalorimetric analyse reveal a dual thermodynamic signature where DPP11 binds substrates through an endothermic/ entropy-driven process, and end products in an exothermic/enthalpy-driven fashion. We propose that increase in protein conformational entropy is the main-driving force for substrate recognition and that enzyme plasticity favours substrate promiscuity. Results and Discussion As previously reported , the overall fold of DPP11 comprises a bilobal architecture (Fig. 1a,b). The upper helical domain dictates the specificity of the enzyme and caps the catalytic domain, which has a typical chymotrypsin double β-barrel fold . PeDPP11 and PgDPP11 superposition yielded a root mean square deviation (r.m.s.d.) of 1.4 Å for 629 out of 685 superimposed Cα-atoms. A notable difference between the unbound PeDPP11 and its complexes with peptides is the conformational change bringing the helical and catalytic domains closer (Fig. 1b). This movement yields an approximate rotation of 22° of one domain relative to the other with a negligible transla- 29, 30 tional component . Notably, the helical domain undergoes larger structural changes reflected in higher r.m.s.d. and B-factor values, when compared to the catalytic domain, which behaves as a rigid body (Supplementary Table S1a,b). The active site of DPP11 lays in a wide cleft running through the middle of the protein between the cata- lytic and helical domains, which contributes to the formation of the substrate binding subsites (Supplementary Table S2a–c). The bound-peptide is anchored at its N-terminus primarily by N332 (N-anchor) located in the helical domain. It moves approximately 4.0 Å (Cα) towards the catalytic domain upon peptide binding (Supplementary Fig. S2a). The distance between the N-anchor and the catalytic S652 permits accommodation of only two amino acid residues, revealing how the enzyme acquires its dipeptidyl peptidase specificity (Figs.  1c,d). Evolutionary conserved R670 is responsible for the Asp/Glu specificity at subsite S1: its guanidinium group directly interacts with the substrate carboxyl group of Asp/Glu (Fig. 1c, Supplementary Fig. S2b). R670 and R336 confer a dominant positive charge to subsite S1 further explaining its P1 acidic specificity (Supplementary Fig. S2c). Indeed, the substitution R670D completely abolished PeDPP11 activity . In PeDPP11:LDVW, the third and fourth amino acids of the substrate (Val and Trp at positions P1′ and P2′, respectively) exhibit few interac- tions with the enzyme. For instance, Val (P1′) displays a weakly defined electron density with only 40% of its sol- vent accessible area buried by DPP11, while Trp (P2′) completely lacks electron density (Fig. 1d). This active site design renders the enzyme’s specificity more relaxed, with selectivity imposed mainly at P1 and P2 residues of the substrate. This promiscuous feature of DPP11 helps to provide nutrients for P. gingivalis and P. endodontalis given the scarce resources in the subgingival plaque . However, the strategy to increase enzyme promiscuity comes with a price: the affinities for substrates and end products are strikingly similar (Fig.  2). We performed a series of isothermal titration calorimetry experiments to further characterize peptide bind- ing to PeDPP11. Binding of LD and RD dipeptides/end products to PeDPP11 was largely exothermic (ΔH bind −1 of −22.0 and −15.5 kJ.mol , respectively) at 25 °C indicating an enthalpy-driven process [Fig. 2 (left panel), Supplementary Fig. S3a]. A favourable change in entropy due to water displacement caused by peptide binding and concomitant domain motion was observed (Fig. 1b). Analysis of PeDPP11:RD using Naccess revealed a large loss of solvent-accessible area upon peptide binding, approximately 1430 Å . In contrast, binding of the LDVW and LDL substrates was largely endothermic (ΔH of +23.8 and +17.0 kJ. bind −1 mol , respectively) at 25 °C indicating an entropy-driven process to overcome the unfavourable enthalpic con- tribution [Fig. 2 (right panel), Supplementary Fig. S3b]. The binding of peptides to PgDPP11 induces the dimer - ization of its monomeric population (Supplementary Fig. S4), masking the real thermodynamic contributions involved in the binding process. In this way, we focused our thermodynamic analysis solely on PeDPP11. Scientific Repo R ts | 7: 2848 | DOI:10.1038/s41598-017-03220-y 2 www.nature.com/scientificreports/ PgDPP11 PeDPP11 PeDPP11:RD PeDPP11:RE Data collection X-ray source BM14/ESRF ID30A-1/ESRF ID29/ESRF ID29/ESRF Space group P2 2 2 P2 2 2 P2 2 2 P2 2 2 1 1 1 1 1 1 1 1 1 1 1 1 Cell dimensions  a, b, c (Å) 103.18, 117.21, 148.35 76.75, 91.83, 229.91 111.81, 114.40, 147.82 111.44, 112.53, 148.26 Resolution (Å) 47.22–2.20 (2.32–2.20) 48.72–2.85 (3.00–2.85) 48.15–2.20 (2.32–2.20) 49.42–2.10 (2.21–2.10) R (%) 4.2 (57.9) 9.4 (46.8) 3.1 (37.9) 4.7 (38.3) pim R (%) 9.1 (122.4) 22.6 (111.8) 6.1 (74.4) 8.4 (69.6) merge CC1/2 (%) 99.8 (96.8) 98.9 (53.5) 99.9 (68.5) 99.6 (69.7) I / σ(I) 12.3 (0.9) 7.5 (1.7) 14.9 (2.0) 8.8 (2.0) Completeness (%) 99.7 (99.4) 100 (100) 99.6 (99.1) 99.0 (99.8) Redundancy 5.6 (5.4) 6.6 (6.6) 4.7 (4.6) 3.9 (4.0) Refinement Resolution (Å) 47.22–2.40 46.7–2.85 45.24–2.20 47.47–2.10 No. reflections 70637 38796 9612 107843 R / R (%) 20.7/25.9 24.0/27.4 18.1/22.6 18.9/22.9 work free No. atoms 11516 10832 11235 11093 Protein 11132 10773 10769 10549 Ligand/ion 23 3 40/5 42/2 Water 361 56 421 500 B–factors (A ) Protein 55.97 46.9 65.3 56.0/ Ligand/ion 72.93 20.78 47.6/62.6 44.6/51.326 Water 51.58 16.6 53.2 47.4 R.m.s. deviations Bond lengths (Å) 0.002 0.003 0.008 0.008 Bond angles (°) 0.680 0.763 1.141 1.094 Ramachandran analysis Favoured (%) 96 93.5 95.5 95.8 Allowed (%) 3.5 5.8 3.8 3.7 Outliers (%) 0.5 0.6 0.7 0.5 PeDPP11:LDVW PeDPP11:altconf FpDPP11:RD Data collection X–ray source ID30A–1/ESRF ID30A–1/ESRF ID23–1/ESRF Space group C2 C2 P2 Cell dimensions a, b, c (Å) 87.78, 113.33, 111.22 88.02, 103.99, 111.39 126.05, 70.68, 191.59 β =  106.2° β =  104.9º β =  97.3º Resolution (Å) 47.40–2.60 (2.74–2.60) 46.82–2.50 (2.64–2.50) 47.61-2.10 (2.21–2.10) R (%) 8.2 (75.4) 6.3 (43.8) 3.6 (43.2) pim R (%) 13.0 (121.1) 9.6 (67.4) 5.5 (66.6) merge CC1/2 (%) 99.2 (41.2) 99.5 (73.4) 99.9 (77.5) I / σ(I) 7.5 (1.0) 9.2 (1.5) 12.1 (1.7) Completeness (%) 99.2 (95.9) 99.0 (99.6) 98.9 (99.6) Redundancy 3.4 (3.5) 3.1 (3.0) 3.2 (3.2) Refinement Resolution (Å) 43.58–2.60 46.85–2.50 46.83–2.10 No. reflections 32005 33283 193404 R / R (%) 21.7/24.7 19.9/24.0 19.6/24.4 work free No. atoms 5490 5393 22036 Protein 5399 5240 21499 Ligand/ion 23/16 3 38/4 Water 52 150 495 B–factors (A ) Protein 51.98 48.5 65.5 Ligand/ion 43.29/90.86 58.5 70.3/58.7 Water 43.04 41.7 50.3 R.m.s. deviations Bond lengths (Å) 0.014 0.003 0.009 Bond angles (°) 1.119 0.705 1.149 Ramachandran analysis Favoured (%) 94 95.1 94.7 Allowed (%) 6 4.1 4.8 Outliers (%) 0 0.8 0.5 Table 1. Data collection and refinement statistics. Scientific Repo R ts | 7: 2848 | DOI:10.1038/s41598-017-03220-y 3 www.nature.com/scientificreports/ Figure 1. Structure of Porphyromonas endodontalis DPP11. (a) Domain architecture of PeDPP11. SP is signal peptide. The locations of catalytic triad amino acids are indicated by “red stars”. (b) Ribbon representation of PeDPP11 structure. Domains are coloured as in item (a) and helix α14 is shown in dark blue. Upper panel shows two perpendicular views of unbound PeDPP11. Lower panel shows two perpendicular views of PeDPP11 as in complex with peptides (binding pocket shown as yellow surface). (c) Active site of PeDPP11:RD (peptide RD shown in green). Catalytic triad is underlined. Note that S652 is mutated to alanine. (d) Active site of PeDPP11:LDVW (peptide LDVW shown in magenta), peptide omit map contoured at 3σ, shown in blue. Scientific Repo R ts | 7: 2848 | DOI:10.1038/s41598-017-03220-y 4 www.nature.com/scientificreports/ Figure 2. Microcalorimetric analysis. Isothermal titration calorimetry experiments performed by titrating LD (left panel) and LDVW (right panel) into PeDPP11. Upper panel shows time-dependent deflection of heat for each injection (top). Integrated calorimetric data for the respective interactions (bottom). The continuous curve represents the best fit using a one-site binding model. Lower panel shows the graphical representation of thermodynamics parameters. Next, we asked what governs the opposite thermodynamic signatures observed for DPP11 binding of end products and substrates. To address this question, we dissected the contributions of the three possible compo- nents influencing the binding energetics: solvent, ligand and the protein itself. The most apparent answer would point to hydrophobic effects, which is the release of well-ordered water molecules from interfaces to the bulk solvent, resulting in system’s entropy increase upon ligand binding . However, our crystal structures of DPP11 in complex with LDVW and dipeptide RD are both in closed conformation, excluding the possibility that solvent released from the protein’s cleft would explain the larger increase in entropy upon substrate binding. Then, we analysed the ligand’s contribution to the process. The presence of only one additional amino acid in the peptide −1 −1 LDL (ΔH of +17.0 kJ.mol ) compared to LD (ΔH of −22.0 kJ.mol ) results in the outstanding difference bind bind −1 of +39.0 kJ.mol in binding enthalpy. Due to the peptides similarity, energetic effects originating from the lig- ands alone do not suffice to explain the distinct thermodynamic binding forces reported. In light of the analyse Scientific Repo R ts | 7: 2848 | DOI:10.1038/s41598-017-03220-y 5 www.nature.com/scientificreports/ Figure 3. er Th modynamic analysis. (a) PeDPP11 binding to LD. (b) PeDPP11 binding to LDVW. Upper panels: Temperature dependence of ∆G, ∆H and −T∆S. Middle panel: Table with thermodynamic data derived from the ITC measurements at different temperatures. Lower panel: Entropy parameters estimations. Conformational entropy was calculated using the following equation: ∆S = ∆S − ∆S − ∆S . Where conf tot sol rt 65 −1 −1 66 ∆S = ∆Cp ln (298 K/385 K) and ∆S is estimated using the “cratic entropy” value of −33.3 J.mol. K . sol rt above, we concluded that the major contribution for the opposite thermodynamic signatures must arise from the protein itself, via changes in conformational entropy, as demonstrated below. In the free energy equation: ΔG = ΔH − TΔS , the total binding entropy (ΔS ) is deconvoluted into the tot tot tot tot sum of changes in ΔS (conformational entropy), ΔS (solvation entropy) and ΔS (rotational and transla- conf sol RT tional entropy) . Based on experimentally-measured heat capacity changes (ΔC ) for PeDPP11:LDVW (−1.6 kJ. −1 −1 −1 −1 mol .K ) and PeDPP11:LD (−3.3 kJ.mol .K ) interactions, we calculated a ΔS of +417.8 and +844.9 J. sol −1 −1 −1 −1 mol .K and a ΔS of −198.2 and −775.6 J.mol .K , respectively (Fig. 3). The data indicate that in both conf binding events the solvent provides a favourable contribution to the observed entropy and shows a 3.5-fold more prohibitive change in overall ΔS for PeDPP11 interaction with LD compared to LDVW. We propose that the conf −1 −1 +677.3 J.mol .K difference in Δ S is associated with the unfolding of DPP11 specific regions upon binding conf to LDVW. e h Th elical domain displays a high diversity of structural states across all solved structures in this work. When compared to unbound PeDPP11, the r.m.s.d. of the helical domain is 5-fold higher than that of the catalytic domain for PeDPP11:LDVW and 2-fold higher for PeDPP11:RD and PeDPP11:RE (Supplementary Table S1a). Particularly, the unfolding of helix α14 (residues 320–346) and loop F441-K451 upon LDVW binding corrobo- rates our hypothesis that protein conformational change is the determining factor for the opposed thermodynam- ics signatures observed upon peptide binding (Fig. 4a,b and Supplementary Fig. S5). We postulate that substrate binding leads to higher protein ΔS , which overcompensates for the unfavourable enthalpic contribution. conf Consistent with the measured endothermic binding, we propose that energy is absorbed from the solu- tion to break key interactions, such as those stabilizing loop F441-K451 and intra-main chain polar interac- tions that stabilize helix α14, but possibly in additional regions of the helical domain. These events permit the motion of the helical domain between different structural states leading to increased protein ΔS (Fig. 4c). conf 35 −1 −1 Usually, protein unfolding yields a positive ΔC , which explains the difference of + 1.7 kJ.mol .K in ΔC p p between PeDPP11:LDVW and PeDPP11:RD interactions. Upon DPP11-substrate binding, the increase in protein Scientific Repo R ts | 7: 2848 | DOI:10.1038/s41598-017-03220-y 6 www.nature.com/scientificreports/ Figure 4. PeDPP11 conformational changes. (a) Close-up view of the main PeDPP11 regions that unfold upon binding to LDVW, as observed in the crystal structures. (b) Loop F441-K451 region superposition of unbound PeDPP11 (blue), PeDPP11:LDVW (magenta, dashed line) and PeDPP11:RD (green). Unbound PeDPP11 is represented as ribbons and peptide binding pocket as yellow surface. (c) Cartoon representation depicting a DPP11 helix unfolding. Upon substrate binding, energy is absorbed from the solution to break polar contacts, which causes helix destabilization. In the disordered stage, the helix accesses different structural states, increasing system entropy. (d) Close-up view of the helix α14 missing region in PeDPP11 . Intra-main chain altconf polar contacts are indicated with orange dashed lines. conformational entropy counterbalances the overall entropic costs in protein-peptide interactions (including loss of protein and peptide degrees of freedom). Interestingly, we obtained an unbound PeDPP11 crystal form, called here PeDPP11 , which lacks electron density for helix α14, indicating its susceptibility to unfold (Fig. 4d). altconf Similar to PeDPP11 complexes, this structure is also closed (rotation angle of 27° of helical domain relative to the catalytic domain), illustrating the enzyme flexibility. Protein-peptide interactions oen o ft ccur in a way that minimizes the conformational changes of the protein partner, while maximizing their enthalpic potential via its packing and formation of hydrogen bonds (Fig. 5a). This strategy helps to decrease the entropic costs associated with the peptide loss of conformational entropy upon binding. The process can also be entropy-driven with the solvent providing the main driving-force, in this case, conformational flexibility may accompany peptide binding (Fig. 5b). Here, increased ΔS in DPP11 estab- conf lishes endothermic substrate binding via partial enzyme de-structuring associated with an increase in helical domain entropy, which acts as an “entropy reservoir” (Fig. 5c). DPP11 active site design displays stereochemical specificity only for P1 and P2 positions of the ligand. This arrangement favours substrate entropy-driven binding by limiting the enthalpic contributions of protein-peptide interaction (i.e. limiting the number of polar contacts) for only the two first amino acids of the incoming peptide. Additionally, our data also illustrate how conforma- tional plasticity enables enzyme promiscuity; for instance, by closing differently around different ligands . Due to experimental challenges, the role of conformational entropy in molecular recognition by proteins has begun to be elucidated only recently, mainly by nuclear magnetic resonance (NMR) relaxation methods . Using NMR techniques and molecular dynamics simulations, Veglia and colleagues observed in cAMP-dependent protein kinase A (PKA-C) a similar binding mode to that of DPP11. They showed that the substrate PLN 1-20 (phospholamban) binds to (PKA-C) in an entropically driven way, resulting in protein increased conformational dynamics. Conversely, binding of the inhibitor PKI (protein kinase inhibitor) to PKA-C is enthalpically driven 5-24 and stabilizes the protein, quenching the enzyme dynamics, which is important to prime the active for catalysis . e s Th tructural and thermodynamics data presented here provide a distinct model for protein-protein interac- tion, particularly in cases where increase in protein conformational entropy significantly contributes to the free energy of binding. Together with PKA-C, DPP11 binding mode may represent a general mechanism for biomo- lecular recognition, allowing the identification of proteins that share similar features and that have evolved to pro- miscuously bind numerous ligands. These findings further provide an innovative framework for structure-based Scientific Repo R ts | 7: 2848 | DOI:10.1038/s41598-017-03220-y 7 www.nature.com/scientificreports/ Figure 5. DPP11 conformational entropy in peptide binding. This cartoon illustrates two previously described models and DPP11 binding model reported in this work. (a) In the enthalpy-driven binding mode depicted, there are no major conformational changes and the active site is prearranged. The process is mainly governed by protein-peptide interactions, resulting in favourable enthalpy. (b) In this entropy-driven binding mode, the displacement of solvent molecules “entropy reservoir” provides the main driving-force for peptide-binding, and increases in system entropy outweighs the unfavourable enthalpy. In this case, peptide binding may be accompanied by protein conformational changes. (c) In DPP11 entropy-driven binding mode, protein conformational entropy is the main driving-force for substrate binding. De-structuring of parts of the helical domain “entropy reservoir” contributes to the increase in entropy necessary to compensate for the unfavourable enthalpy. drug design to develop compounds that target the “entropy reservoirs”. For instance, molecules able to prevent the unfolding of helix 14 and loop F441-K451 could display efficient inhibitor properties. Alternatively, it is also conceivable the identification of effectors that increase catalytic power by promoting protein dynamics. Methods Protein expression and purification. E. coli codon-optimized genes encoding for C-terminal 6xHis-tagged PeDPP11 S652A and PgDPP11 S655A in the pET-22b(+) vector (cloning sites 22-717 22-720 NdeI and XhoI) were purchased from GenScript (Piscataway, USA). The construct Flavobacterium p s y c hro p hi l u m DPP11 (called here FpDPP11 ) encoding the N-terminal fusion sequence 17-713 (MGGSHHHHHHGMASMTGGQQMGRDLYDDDDKDPTL) was cloned into the expression vector pTricHis. All plasmids were transformed in BL21(DE3)pLysS. The cells were grown in LB-medium containing −1 −1 100 μg ml ampicillin and 34 μg ml chloramphenicol. Aer 3 ft h at 37 °C, temperature was reduced to 30 °C and protein expression was induced by the addition of 0.5 mM isopropyl-1-thio-D-galactopyranoside (IPTG). Cells were then allowed to grow for 4 h and were harvested by centrifugation at 4,000 g for 10 minutes. For protein purification, cells were resuspended in 50 mM Hepes-NaOH pH 8.0, 150 mM NaCl. Cell debris was removed by centrifugation at 25,000 g for 45 minutes at 4 °C, and the supernatant was subjected to affinity chromatography on TM 5 ml HisTrap (GE Healthcare) equilibrated with lysis buffer. Bound protein was eluted in lysis buffer containing 500 mM imidazole. Further purification was performed by size exclusion chromatography (SEC) on a HiLoad 26/60 Superdex 200 (GE Healthcare) column previously equilibrated with 10 mM Hepes-NaOH pH 7.4, 100 mM NaCl. Purified protein was concentrated using 20 ml concentrators with an appropriate molecular weight cut-off (Vivaspin 50,000 MWCO, Sartorius). Crystallization. To enhance the crystallizability of PgDPP11 and PeDPP11, truncated forms of the enzymes were designed lacking the first 21 amino acid residues (called here PgDPP11 and PeDPP11 ) which 22-720 22-717 were predicted to be signal peptides . The following crystallization trials used the nanodrop-dispensing robot Scientific Repo R ts | 7: 2848 | DOI:10.1038/s41598-017-03220-y 8 www.nature.com/scientificreports/ (Phoenix RE; Rigaku Europe) employing the sitting drop vapour diffusion technique by mixing equal volumes of protein (200 nl) and reservoir solutions (200 nl) at 20 °C in a 96-well Intelli-Plate (ArtRobbins Instruments ). All crystals were cryoprotected in a solution consisting of reservoir solution supplemented with 20% glycerol before flash-cooling in liquid nitrogen. X-ray diffraction data were collected at 100 K. −1 42 • PgDPP11 S655A (PgDPP11) was crystallized at 10 mg ml using the Morpheus screen condition 22-720 D11: 0.12 M alcohols, buffer system 3 pH 8.5, 40% v/v glycerol, 20% w/v PEG 4000. −1 • PeDPP11 S652A (PeDPP11:RD) at 10 mg ml was incubated with 1.2 mM dipeptide Arg-Asp on ice 22-717 for 15 minutes. Crystals were obtained in the Morpheus screen condition E10: 0.12 M ethylene glycols, 0.1 M buffer system 3 pH 8.5, 40% v/v ethylene glycol, 20% w/v PEG 8000. −1 • PeDPP11 S652A (PeDPP11:RE) at 10 mg ml was incubated with 1.2 mM dipeptide Arg-Glu on ice 22-717 for 15 minutes. Crystals were obtained in the Morpheus screen condition F12: 0.12 M monosacharides, 0.1 M buffer system 3 pH 8.5, 25% v/v MPD, 25% PEG 1000, 25% w/v PEG 3350. −1 • FpDPP11 was crystallized at 10 mg ml in the PACT Premier screen (Molecular Dimensions ) condi- 17-713 tion G2: 0.2 M NaBr, 0.1 M bistris propane, pH 7.5, 20% PEG 3350. e Th following crystallization trials used the sitting-drop vapour diffusion method and were conducted at the High Throughput Crystallization Laboratory of the EMBL Grenoble Outstation (https://embl.fr/htxlab) . Drops of 100 nl sample and 100 nl crystallization solution were set up in CrystalDirect plates (MiTeGen, Ithaca, USA) with a Cartesian PixSys robot (Cartesian Technologies, Irvine, USA). e Th experiments were incubated at 20 °C in a RockImager system (Formulatrix Inc., Bedford, USA). Automated high-throughput crystal cryo-cooling and TM 44 harvesting were performed with CrystalDirect Technology as described by Zander et al., 2016 . Crystals were stored in liquid nitrogen for data collection. Data collection was done in a fully automated fashion at MASSIF-1, ESRF . X-ray diffraction data were collected at 100 K. −1 • PeDPP11 S652A (unbound) was crystallized at 10 mg ml initially in the condition D11 of ProComplex 22-717 screen (Qiagen ). The condition was further optimized to 0.1 M Tris-HCl pH 7.5, 15% PEG 6000. • PeDPP11 S652A in the alternate conformation (PeDPP11 ) was crystallized initially in the con- 22-717 altconf dition B2 of ProComplex screen (Qiagen ): 0.1 M calcium acetate, 10% w/v PEG 4000, 0.1 M sodium acetate pH 4.5. The condition was further optimized to: 0.1 M calcium acetate, 15% w/v PEG 4000, 0.1 M sodium acetate pH 5.0. −1 • PeDPP11 S652A (PeDPP11:LDVW) at 22 mg ml was incubated for 30 minutes on ice with 1.0 mM 22-717 Leu-Asp-Val-Trp. Crystals were initially obtained in condition D11 of ProComplex screen (Qiagen ). The condition was further optimized to 0.1 M Tris-HCl pH 7.5, 15% PEG 6000. Structure determination. Our initial attempts to solve the structure by molecular replacement using the coordinates of dipeptidyl aminopeptidase BII (DAP BII) from Pseudoxanthomonas mexicana WO24, the closest homologue (37% identity) with known 3D structure, were ineffective. We suspected that different conformations adopted by the protein in the crystal could be rendering molecular replacement trials unsuccessful. So, a DPP11 homologue (37% identical to PgDPP11) from FpDPP11 was employed to grow monoclinic crystals (space 17-713 group P2 ). FpDPP11 structure was then determined by molecular replacement using the coordinates of 1 17-713 DAP BII (PDB code: 3WOJ, 27% identity). Subsequently, FpDPP11 structure was successfully used as tem- 17-713 plate to solve the structures of PgDPP11 S655A and PeDPP11 S652A (Supplementary Fig. S6). Two 22-720 22-717 out of four subunits in the crystal asymmetric unit of FpDPP11 were in complex with Arg-Asp, a dipeptide 17-713 co-purified from E. coli . This finding led us to grow co-crystals of PeDPP11 S652A in complex with dipep- 22-717 tides Arg-Asp and Arg-Glu. 47 48 49 Data were processed with XDS , Scala and Pointless . All structures were solved by molecular replacement 50 51 52 53 with PHASER , refined with PHENIX and manually adjusted in COOT . R -values were computed from free 5% randomly chosen reflections not used for the refinement. The structure stereochemistry was checked using Molprobity . Details of data collection and refinement statistics are provided in Table  1. Peptide omit maps are depicted in Supplementary Fig. S7. All figures were prepared using the program PyMOL (http://www.pymol.org). 55, 56 Poisson-Boltzmann calculations were performed using the software APBS . Isothermal Titration Calorimetry. All experiments were carried out in 10 mM Hepes-NaOH pH 7.4, 100 mM NaCl. Both the enzymes and the peptides were dissolved in the same buffer. e Th bindings were analysed TM with a MicroCal iTC microcalorimeter (GE Healthcare, Life Sciences) equilibrated at the respective tempera- ture. Typically, a total of one aliquot of 0.4 μl and 19 aliquots of 2.0 μl of the peptide solution were injected at a rate of 0.5 μl/s into 200 μl of the protein solution under constant stirring at 750 rpm at the specified concentrations. The following titrations were performed: 600 μM LDVW to 60 μM PeDPP11, 810 μM LD to 75 μM PeDPP11 (at 10 °C, 1.28 mM LD to 120 μM PeDPP11 was employed), 1 mM of LDL to 75 μM PeDPP11, 1.0 mM of RD to 80 μM M PeDPP11. As a control to exclude buffer-dependent effects, we additionally performed the binding of 600 μM LDVW to 60 μM PeDPP11 and 600 μM LD to 75 μM PeDPP11 in 50 mM sodium phosphate pH 7.4 with 100 mM NaCl (Supplementary Fig. S8). Every injection was carried out over a period of 4 s with a spacing of 110 s between the injections. The corresponding heats of binding were determined by integrating the observed peaks after correcting for the heat dilution of the peptide determined in a reference measurement (peptide injected into buffer). These corrected values were plotted against the ratio peptide vs. protein concentration in the cell to generate the binding isotherm. Nonlinear least-squares fitting using Origin version 7.0 (Microcal) was used to obtain the association constants (K ), heats of binding (ΔH) and stoichiometries. K and Gibbs free energy (ΔG) a d were calculated according to: K = 1/K and ΔG = −RT ln K = RT ln K . The reported values are averages of at d a a d Scientific Repo R ts | 7: 2848 | DOI:10.1038/s41598-017-03220-y 9 www.nature.com/scientificreports/ least two independent measurements. The stoichiometry obtained in all experiments is within the range 0.7–1.1, which is in agreement with the crystal structures (stoichiometry 1). Small-angle X-ray scattering (SAXS) data collection and analysis. SAXS experiments were per- formed at 0.9918 Å wavelength ESRF at BioSAXS beamline BM29 (Grenoble, France) equipped with PILATUS 57 −1 −1 1 M . The detector distance was set at 2.864 m. The range of scattering vector 0.03 nm < q < 4.5 nm was cov- −1 ered. For PeDPP11 S652A, the data were collected using the following protein concentrations: 1.1 mg ml , 22-717 −1 −1 −1 1.9 mg ml , 9.62 mg ml and 16.37 mg ml . e s Th amples were in a buffer containing 10 mM Hepes-NaOH pH 7.4, 100 mM NaCl, and the measurements were performed at 20 °C. The automated sample changer was employed to load the samples and constantly remove the irradiated sample. Twenty successive exposures of 1 second were collected and compared to detect and discard possible radiation damage ee ff cts. For PgDPP11 S655A, on-line HPLC-mode was used with a 22-720 TM Superdex 200 10/300 GL column (GE Healthcare), and SAXS data was recorded directly on the sample eluted. −1 −1 A 0.5 ml min flow was used. The protein concentration applied onto the column was 50 mg ml . The data were processed and analyzed using the online analysis pipeline . Subsequent manual processing was done with the ATSAS 2.6 program package . The forward scattering I(0) and the radius of gyration Rg were 61, 62 extracted from the Guinier approximation calculated with the AutoRG function within PRIMUS . e m Th axi- mum particle dimension Dmax and P(r) function were evaluated using the program GNOM . For PeDPP11 22-717 S652A, the analysis of SAXS data by Guinier approximation showed no concentration dependence effect, indi- cating the samples were homogeneous and free of aggregation. For this construct, SAXS analyses were performed by merging data from all concentrations measured. For both PeDPP11 S652A and PgDPP11 S655A, 22-717 22-720 the theoretical scattering from the crystallographic structures was calculated using the program CRYSOL and compared with the respective scattering profiles. Size exclusion chromatography followed by Multiangle Laser Light Scattering. To assess the TM oligomeric state and molecular weight, the samples were applied onto a Superdex 200 10/300 GL column (GE Healthcare) at the respective concentrations, using a flow of 0.5 ml/min. The column was connected to a miniDAWN Tristar light scattering instrument (Wyatt Technologies, Santa Barbara, CA) and pre-equilibrated with 10 mM Hepes-NaOH pH 7.4, 100 mM NaCl. Data analysis was performed using the manufacturer’s software ASTRA. Hydrolyzing activity toward MCA-dipeptides. Purification of recombinant active forms of PgDPP11 and PeDPP11 was performed according to Ohara-Nemoto et al. . PgDPP11 and PeDPP11 (2-20 ng) were used for measurement of dipeptidyl peptidase activity in 200 μl of reaction solution composed of 50 mM sodium phosphate pH 7.0 and 5 mM EDTA. The reaction was started with an addition of 20 μM Leu-Asp-, Arg-Asp or Leu-Glu-MCA and continued at 37 °C for 30 min (Supplementary Fig. S9). Fluorescence intensity was measured with excitation at 380 nm and emission at 460 nm with a Fluorescence Photometer F-4000 (Hitachi). References 1. Eke, P. I. et al. Update on Prevalence of Periodontitis in Adults in the United States: NHANES 2009 to 2012. J. Periodontol. 86, 611–622 (2015). 2. Batchelor, P. Is periodontal disease a public health problem? Br. Dent. J. 217, 405–409 (2014). 3. Chee, B., Park, B. & Bartold, P. M. Periodontitis and type II diabetes: a two-way relationship. Int. J. Evid. Based Healthc. 11, 317–329 (2013). 4. Hajishengallis, G., Darveau, R. P. & Curtis, M. A. The keystone-pathogen hypothesis. Nat. Rev. Microbiol. 10, 717–725 (2012). 5. Lombardo Bedran, T. B. et al. Porphyromonas endodontalis in chronic periodontitis: a clinical and microbiological cross-sectional study. J. Oral Microbiol. 4, 10123 (2012). 6. Wang, J. & Jia, H. Metagenome-wide association studies: fine-mining the microbiome. Nat. Rev. Microbiol. 14, 508–522 (2016). 7. Lalla, E. & Papapanou, P. N. Diabetes mellitus and periodontitis: a tale of two common interrelated diseases. Nat. Rev. Endocrinol. 7, 738–748 (2011). 8. Lundberg, K., Wegner, N., Yucel-Lindberg, T. & Venables, P. J. Periodontitis in RA-the citrullinated enolase connection. Nat. Rev. Rheumatol. 6, 727–730 (2010). 9. Javed, F. & Warnakulasuriya, S. Is there a relationship between periodontal disease and oral cancer? A systematic review of currently available evidence. Crit. Rev. Oncol. Hematol. 97, 197–205 (2015). 10. Ha, N. H. et al. Prolonged and repetitive exposure to Porphyromonas gingivalis increases aggressiveness of oral cancer cells by promoting acquisition of cancer stem cell properties. Tumour Biol. 36, 9947–9960 (2015). 11. Ruiz, I. F. Risk factors: Periodontitis increases risk of a first MI. Nat. Rev. Cardiol. 13, 124 (2016). 12. Farhad, S. Z. et al. The effect of chronic periodontitis on serum levels of tumor necrosis factor-alpha in Alzheimer disease. Dent. Res. J. (Isfahan) 11, 549–552 (2014). 13. Paju, S. & Scannapieco, F. A. Oral biofilms, periodontitis, and pulmonary infections. Oral Dis. 13, 508–512 (2007). 14. Hajishengallis, G. Periodontitis: from microbial immune subversion to systemic inflammation. Nat. Rev. Immunol. 15, 30–44 (2015). 15. Rouf, S. M. et al. Phenylalanine 664 of dipeptidyl peptidase (DPP) 7 and Phenylalanine 671 of DPP11 mediate preference for P2- position hydrophobic residues of a substrate. FEBS Open Bio 3, 177–183 (2013). 16. Nemoto, T. K. & Ohara-Nemoto, Y. Exopeptidases and gingipains in Porphyromonas gingivalis as prerequisites for its amino acid metabolism. Jpn. Dent. Sci. Rev. 52, 22–29 (2016). 17. Takahashi, N. Oral Microbiome Metabolism: From “Who Are They?” to “What Are They Doing?”. J. Dent. Res. 94, (1628–1637 (2015). 18. Xu, Q. et al. A Distinct Type of Pilus from the Human Microbiome. Cell 165, 690–703 (2016). 19. Holt, S. C., Kesavalu, L., Walker, S. & Genco, C. A. Virulence factors of Porphyromonas gingivalis. Periodontol. 2000 20, 168–238 (1999). 20. de Diego, I. et al. Structure and mechanism of cysteine peptidase gingipain K (Kgp), a major virulence factor of Porphyromonas gingivalis in periodontitis. J. Biol. Chem. 289, 32291–32302 (2014). 21. Nelson, K. E. et al. Complete genome sequence of the oral pathogenic Bacterium porphyromonas gingivalis strain W83. J. Bacteriol. 185, 5591–5601 (2003). Scientific Repo R ts | 7: 2848 | DOI:10.1038/s41598-017-03220-y 10 www.nature.com/scientificreports/ 22. Ohara-Nemoto, Y. et al. Identification and characterization of prokaryotic dipeptidyl-peptidase 5 from Porphyromonas gingivalis. J. Biol. Chem. 289, 5436–5448 (2014). 23. Takahashi, N., Sato, T. & Yamada, T. Metabolic pathways for cytotoxic end product formation from glutamate- and aspartate- containing peptides by Porphyromonas gingivalis. J. Bacteriol. 182, 4704–4710 (2000). 24. Ohara-Nemoto, Y. et al. Asp- and Glu-specific novel dipeptidyl peptidase 11 of Porphyromonas gingivalis ensures utilization of proteinaceous energy sources. J. Biol. Chem. 286, 38115–38127 (2011). 25. Kurita-Ochiai, T. et al. Butyric acid induces apoptosis in inflamed fibroblasts. J. Dent. Res. 87, 51–55 (2008). 26. Rouf, S. M. et al. Discrimination based on Gly and Arg/Ser at position 673 between dipeptidyl-peptidase (DPP) 7 and DPP11, widely distributed DPPs in pathogenic and environmental gram-negative bacteria. Biochimie 95, 824–832 (2013). 27. Sakamoto, Y. et al. Structural and mutational analyses of dipeptidyl peptidase 11 from Porphyromonas gingivalis reveal the molecular basis for strict substrate specificity. Sci. Rep. 5, 11151 (2015). 28. Polgar, L. The catalytic triad of serine peptidases. Cell. Mol. Life Sci. 62, 2161–2172 (2005). 29. Hayward, S. & Berendsen, H. J. Systematic analysis of domain motions in proteins from conformational change: new results on citrate synthase and T4 lysozyme. Proteins 30, 144–154 (1998). 30. Hayward, S. & Lee, R. A. Improvements in the analysis of domain motions in proteins from conformational change: DynDom version 1.50. J. Mol. Graph. Model. 21, 181–183 (2002). 31. Kuboniwa, M. & Lamont, R. J. Subgingival biofilm formation. Periodontol. 2000 52, 38–52 (2010). 32. Naccess 2.1.1 v. 2.1.1 (Department of Biochemistry and Molecular Biology, University College, London., 1996). 33. Biela, A. et al. Ligand bindin g stepwise disrupts water network in thrombin: enthalpic and entropic changes reveal classical hydrophobic effect. J. Med. Chem. 55, 6094–6110 (2012). 34. Marlow, M. S., Dogan, J., Frederick, K. K., Valentine, K. G. & Wand, A. J. The role of conformational entropy in molecular recognition by calmodulin. Nat. Chem. Biol. 6, 352–358 (2010). 35. Prabhu, N. V. & Sharp, K. A. Heat capacity in proteins. Annu. Rev. Phys. Chem. 56, 521–548 (2005). 36. London, N., Movshovitz-Attias, D. & Schueler-Furman, O. The structural basis of peptide-protein binding strategies. Structure 18, 188–199 (2010). 37. Bezerra, G. A. et al. Entropy-driven binding of opioid peptides induces a large domain motion in human dipeptidyl peptidase III. Proc. Natl. Acad. Sci. USA 109, 6525–6530 (2012). 38. Nobeli, I., Favia, A. D. & Thornton, J. M. Protein promiscuity and its implications for biotechnology. Nat. Biotechnol. 27, 157–167 (2009). 39. Frederick, K. K., Marlow, M. S., Valentine, K. G. & Wand, A. J. Conformational entropy in molecular recognition by proteins. Nature 448, 325–329 (2007). 40. Masterson, L. R. et al. Dynamically committed, uncommitted, and quenched states encoded in protein kinase A revealed by NMR spectroscopy. Proc. Natl. Acad. Sci. USA 108, 6969–6974 (2011). 41. Slabinski, L. et al. XtalPred: a web server for prediction of protein crystallizability. Bioinformatics 23, 3403–3405 (2007). 42. Gorrec, F. The MORPHEUS protein crystallization screen. J. App. Cryst. 42, 1035–1042 (2009). 43. Dimasi, N., Flot, D., Dupeux, F. & Marquez, J. A. Expression, crystallization and X-ray data collection from microcrystals of the extracellular domain of the human inhibitory receptor expressed on myeloid cells IREM-1. Acta Crystallogr. Sect. F Struct. Biol. Cryst. Commun. 63, 204–208 (2007). 44. Zander, U. et al. Automated harvesting and processing of protein crystals through laser photoablation. Acta Cryst. D 72, 454–466 (2016). 45. Svensson, O., Malbet-Monaco, S., Popov, A., Nurizzo, D. & Bowler, M. W. Fully automatic characterization and data collection from crystals of biological macromolecules. Acta Cryst. D 71, 1757–1767 (2015). 46. Sakamoto, Y. et al. S46 peptidases are the first exopeptidases to be members of clan PA. Sci. Rep. 4, 4977 (2014). 47. Kabsch, W. X. Acta Cryst. D 66, 125–132 (2010). 48. Evans, P. Scaling and assessment of data quality. Acta Cryst. D 62, 72–82 (2006). 49. Evans, P. R. An introduction to data reduction: space-group determination, scaling and intensity statistics. Acta Cryst. D 67, 282–292 (2011). 50. McCoy, A. J. et al. Phaser crystallographic software. J. App. Cryst. 40, 658–674 (2007). 51. Adams, P. D. et al. The Phenix software for automated determination of macromolecular structures. Methods 55, 94–106 (2011). 52. Emsley, P. & Cowtan, K. Coot: model-building tools for molecular graphics. Acta Cryst. D 60, 2126–2132 (2004). 53. Kleywegt, G. J. & Brunger, A. T. Checking your imagination: applications of the free R value. Structure 4, 897–904 (1996). 54. Chen, V. B. et al. MolProbity: all-atom structure validation for macromolecular crystallography. Acta Cryst. D 66, 12–21 (2010). 55. Baker, N. A., Sept, D., Joseph, S., Holst, M. J. & McCammon, J. A. Electrostatics of nanosystems: application to microtubules and the ribosome. Proc. Natl. Acad. Sci. USA 98, 10037–10041 (2001). 56. Dolinsky, T. J., Nielsen, J. E., McCammon, J. A. & Baker, N. A. PDB2PQR: an automated pipeline for the setup of Poisson-Boltzmann electrostatics calculations. Nucleic Acids Res. 32, W665–667 (2004). 57. Pernot, P. et al. Upgraded ESRF BM29 beamline for SAXS on macromolecules in solution. J. Synchrotron Rad. 20, 660–664 (2013). 58. Round, A. et al. BioSAXS Sample Changer: a robotic sample changer for rapid and reliable high-throughput X-ray solution scattering experiments. Acta Cryst. D 71, 67–75 (2015). 59. Brennich, M. E. et al. Online data analysis at the ESRF bioSAXS beamline, BM29. J. App. Cryst. 49, 203–212 (2016). 60. Petoukhov, M. V. et al. New developments in the program package for small-angle scattering data analysis. J. App. Cryst. 45, 342–350 (2012). 61. Petoukhov, M. V., Konarev, P. V., Kikhney, A. G. & Svergun, D. I. ATSAS 2.1 - towards automated and web-supported small-angle scattering data analysis. J. App. Cryst. 40, s223–s228 (2007). 62. Konarev, P. V., Volkov, V. V., Sokolova, A. V., Koch, M. H. J. & Svergun, D. I. PRIMUS: a Windows PC-based system for small-angle scattering data analysis. J. App. Cryst. 36, 1277–1282 (2003). 63. Svergun, D. Determination of the regularization parameter in indirect-transform methods using perceptual criteria. J. App. Cryst. 25, 495–503 (1992). 64. Svergun, D., Barberato, C. & Koch, M. H. J. CRYSOL - a Program to Evaluate X-ray Solution Scattering of Biological Macromolecules from Atomic Coordinates. J. App. Cryst. 28, 768–773 (1995). 65. Baldwin, R. L. Temperature dependence of the hydrophobic interaction in protein folding. Proc. Natl. Acad. Sci. USA 83, 8069–8072 (1986). 66. Murphy, K. P., Xie, D., o Th mpson, K. S., Amzel, L. M. & Freire, E. Entropy in biological binding processes: estimation of translational entropy loss. Proteins 18, 63–67 (1994). Acknowledgements GAB was supported by the Vienna International Postdoctoral Program (VIPS). KDC research was supported by a research grant of the Austrian Academy of Sciences (#P25353-B21). This study was supported by JSPS KAKENHI, Grant Nos 15K11047 to T.K.N., 16K11481 to Y.O.-N.). We are grateful to the beam line staff at European Synchrotron Radiation Facility (ESRF) for support during diffraction data collection. Scientific Repo R ts | 7: 2848 | DOI:10.1038/s41598-017-03220-y 11 www.nature.com/scientificreports/ Author Contributions G.A.B., Y.O.N., S.F., I.C., G.H. and T.K.N. conceived and performed experiments; G.A.B. performed structure determination and ITC measurements; G.A.B., Y.O.N., S.F., A.R., I.C., J.A.M., T.K.N., K.D.C. analysed the data; G.A.B. wrote the manuscript with comments from all authors. Additional Information Supplementary information accompanies this paper at doi:10.1038/s41598-017-03220-y Competing Interests: The authors declare that they have no competing interests. Accession codes: The model coordinates and structure factors have been deposited in the Protein Data Bank (PDB) under accession numbers 5JWF(PgDPP11), 5JXK(PeDPP11), 5JWG(PeDPP11:RD), 5JWI(PeDPP11:RE), 5JY0(PeDPP11:LDVW), 5JXP(PeDPP11:altconf ) and 5JXF(FpDPP11:RD). Publisher's note: Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Cre- ative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not per- mitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/. © The Author(s) 2017 Scientific Repo R ts | 7: 2848 | DOI:10.1038/s41598-017-03220-y 12

Journal

Scientific ReportsSpringer Journals

Published: Jun 6, 2017

References

You’re reading a free preview. Subscribe to read the entire article.


DeepDyve is your
personal research library

It’s your single place to instantly
discover and read the research
that matters to you.

Enjoy affordable access to
over 18 million articles from more than
15,000 peer-reviewed journals.

All for just $49/month

Explore the DeepDyve Library

Search

Query the DeepDyve database, plus search all of PubMed and Google Scholar seamlessly

Organize

Save any article or search result from DeepDyve, PubMed, and Google Scholar... all in one place.

Access

Get unlimited, online access to over 18 million full-text articles from more than 15,000 scientific journals.

Your journals are on DeepDyve

Read from thousands of the leading scholarly journals from SpringerNature, Elsevier, Wiley-Blackwell, Oxford University Press and more.

All the latest content is available, no embargo periods.

See the journals in your area

DeepDyve

Freelancer

DeepDyve

Pro

Price

FREE

$49/month
$360/year

Save searches from
Google Scholar,
PubMed

Create lists to
organize your research

Export lists, citations

Read DeepDyve articles

Abstract access only

Unlimited access to over
18 million full-text articles

Print

20 pages / month

PDF Discount

20% off