Access the full text.
Sign up today, get DeepDyve free for 14 days.
Moyocoyani Molina-Espíritu, R. Esquivel, M. Kohout, J. Angulo, J. Dobado, J. Dehesa, Sheila LópezRosa, C. Soriano-Correa (2014)
Insight into the informational-structure behavior of the Diels-Alder reaction of cyclopentadiene and maleic anhydrideJournal of Molecular Modeling, 20
Renxiao Wang, Xueliang Fang, Yipin Lu, Chao-Yie Yang, Shaomeng Wang (2005)
The PDBbind database: methodologies and updates.Journal of medicinal chemistry, 48 12
Zhiqiang Yan, Jin Wang (2012)
Specificity quantification of biomolecular recognition and its implication for drug discoveryScientific Reports, 2
Jin Wang, Gennady Verkhivker (2003)
Energy landscape theory, funnels, specificity, and optimal criterion of biomolecular binding.Physical review letters, 90 18
J. Havranek, P. Harbury (2003)
Automated design of specificity in molecular recognitionNature Structural Biology, 10
Ann Stock, R. Gao (2009)
Faculty Opinions recommendation of Design of protein-interaction specificity gives selective bZIP-binding peptides.
R. Levy, Emilio Gallicchio (1998)
Computer simulations with explicit solvent: recent progress in the thermodynamic decomposition of free energies and in modeling electrostatic effects.Annual review of physical chemistry, 49
J. Janin (1995)
Principles of protein-protein recognition from structure to thermodynamics.Biochimie, 77 7-8
D. Eisenberg, A. McLachlan (1986)
Solvation energy in protein folding and bindingNature, 319
D. Kitchen, H. Decornez, J. Furr, J. Bajorath (2004)
Docking and scoring in virtual screening for drug discovery: methods and applicationsNature Reviews Drug Discovery, 3
C. Kramer, P. Gedeck (2010)
Leave-Cluster-Out Cross-Validation Is Appropriate for Scoring Functions Derived from Diverse Protein Data SetsJournal of chemical information and modeling, 50 11
Jian Zhang, Fan Zheng, G. Grigoryan (2014)
Design and designability of protein-based assemblies.Current opinion in structural biology, 27
Lin Jiang, Yingduo Gao, Fenglou Mao, Zhijie Liu, L. Lai (2002)
Potential of mean force for protein–protein interaction studiesProteins: Structure, 46
T. Kortemme, L. Joachimiak, A. Bullock, A. Schuler, B. Stoddard, D. Baker (2004)
Computational redesign of protein-protein interaction specificityNature Structural &Molecular Biology, 11
Y. Levy, J. Onuchic (2006)
Water mediation in protein folding and molecular recognition.Annual review of biophysics and biomolecular structure, 35
Jin Wang, Xiliang Zheng, Yongliang Yang, D. Drueckhammer, Wei Yang, Gennardy Verkhivker, E. Wang (2007)
Quantifying intrinsic specificity: a potential complement to affinity in drug screening.Physical review letters, 99 19
Liyong Guo, Zhiqiang Yan, Xiliang Zheng, Liang Hu, Yongliang Yang, Jin Wang (2014)
A comparison of various optimization algorithms of protein–ligand docking programs by fitness accuracyJournal of Molecular Modeling, 20
M. Clark, R. Cramer, Nicole Opdenbosch (1989)
Validation of the general purpose tripos 5.2 force fieldJournal of Computational Chemistry, 10
Yu Su, Ao Zhou, Xuefeng Xia, Wen Li, Zhirong Sun (2009)
Quantitative prediction of protein–protein binding affinity with a potential of mean force considering volume correctionProtein Science, 18
Yan Li, Li Han, Zhihai Liu, Renxiao Wang (2014)
Comparative Assessment of Scoring Functions on an Updated Benchmark: 2. Evaluation Methods and General ResultsJournal of chemical information and modeling, 54 6
B. Shoichet (2004)
Virtual screening of chemical librariesNature, 432
David Zilian, C. Sotriffer (2013)
SFCscoreRF: A Random Forest-Based Scoring Function for Improved Affinity Prediction of Protein-Ligand ComplexesJournal of chemical information and modeling, 53 8
Zhiqiang Yan, Liyong Guo, Liang Hu, Jin Wang (2013)
Specificity and affinity quantification of protein-protein interactionsBioinformatics, 29 9
J. Dunbar, Richard Smith, Chao-Yie Yang, P. Ung, Katrina Lexa, Nickolay Khazanov, J. Stuckey, Shaomeng Wang, H. Carlson (2011)
CSAR Benchmark Exercise of 2010: Selection of the Protein–Ligand ComplexesJournal of Chemical Information and Modeling, 51
Daniel Bolon, R. Grant, T. Baker, R. Sauer (2005)
Specificity versus stability in computational protein design.Proceedings of the National Academy of Sciences of the United States of America, 102 36
Xiakun Chu, Linfeng Gan, E. Wang, Jin Wang (2013)
Quantifying the topography of the intrinsic energy landscape of flexible biomolecular recognitionProceedings of the National Academy of Sciences, 110
P. Ballester, John Mitchell (2010)
A machine learning approach to predicting protein-ligand binding affinity with applications to molecular dockingBioinformatics, 26 9
E. Purisima, T. Sulea (2014)
Solvation models: theory and validation.Current pharmaceutical design, 20 20
B. Lee, F. Richards (1971)
The interpretation of protein structures: estimation of static accessibility.Journal of molecular biology, 55 3
Junmei Wang, Wei Wang, Shuanghong Huo, Andrew Lee, P. Kollman (2001)
Solvation Model Based on Weighted Solvent Accessible Surface AreaJournal of Physical Chemistry B, 105
Chi Zhang, Song Liu, Qianqian Zhu, Yaoqi Zhou (2005)
A knowledge-based energy function for protein-ligand, protein-protein, and protein-DNA complexes.Journal of medicinal chemistry, 48 7
Joffrey Gabel, Jérémy Desaphy, D. Rognan (2014)
Beware of Machine Learning-Based Scoring Functions - On the Danger of Developing Black BoxesJournal of chemical information and modeling, 54 10
(1993)
NACCESS. Computer program. London: Department of Biochemistry and Molecular Biology, University Col- lege
P. Ballester, A. Schreyer, T. Blundell (2014)
Does a More Precise Chemical Description of Protein–Ligand Complexes Lead to More Accurate Prediction of Binding Affinity?Journal of Chemical Information and Modeling, 54
P. Kollman, I. Massova, Carolina Reyes, B. Kuhn, Shuanghong Huo, L. Chong, Matthew Lee, Tai-Sung Lee, Y. Duan, Wei Wang, O. Donini, P. Cieplak, Jaysharee Srinivasan, D. Case, T. Cheatham (2000)
Calculating structures and free energies of complex molecules: combining molecular mechanics and continuum models.Accounts of chemical research, 33 12
J. Ashworth, J. Havranek, Carlos Duarte, D. Sussman, R. Monnat, B. Stoddard, D. Baker (2006)
Computational redesign of endonuclease DNA binding and cleavage specificity
K. Dill, Thomas Truskett, V. Vlachy, B. Hribar-Lee (2005)
Modeling water, the hydrophobic effect, and ion solvation.Annual review of biophysics and biomolecular structure, 34
S. Cosconati, Stefano Forli, A. Perryman, Rodney Harris, D. Goodsell, A. Olson (2010)
Virtual screening with AutoDock: theory and practiceExpert Opinion on Drug Discovery, 5
Tingjun Hou, Wei Zhang, Qin Huang, Xiaojie Xu (2005)
An extended aqueous solvation model based on atom-weighted solvent accessible surface areas: SAWSA v2.0 modelJournal of Molecular Modeling, 11
G. Papoian, J. Ulander, P. Wolynes (2003)
Role of water mediated interactions in protein-protein recognition landscapes.Journal of the American Chemical Society, 125 30
W. Koppensteiner, M. Sippl (1998)
Knowledge-based potentials--back to the roots.Biochemistry. Biokhimiia, 63 3
Jun Wang, Wei Wang (1999)
A computational approach to simplifying the protein folding alphabetNature Structural Biology, 6
M. Gilson, J. Given, B. Bush, J. McCammon (1997)
The statistical-thermodynamic basis for computation of binding affinities: a critical review.Biophysical journal, 72 3
Tingjun Hou, Xuebin Qiao, A. Zhang, Xiaojie Xu (2002)
Empirical Aqueous Solvation Models Based on Accessible Surface Areas with Implicit ElectrostaticsJournal of Physical Chemistry B, 106
Zhiqiang Yan, Jin Wang (2013)
Optimizing Scoring Function of Protein-Nucleic Acid Interactions with Both Affinity and SpecificityPLoS ONE, 8
M. Feig, C. Brooks (2004)
Recent advances in the development and application of implicit solvent models in biomolecule simulations.Current opinion in structural biology, 14 2
M. Bello, M. Martínez-Archundia, J. Correa-Basurto (2013)
Automated docking for novel drug discoveryExpert Opinion on Drug Discovery, 8
Jie Liu, Renxiao Wang (2015)
Classification of Current Scoring FunctionsJournal of chemical information and modeling, 55 3
Julia Shifman, S. Mayo (2003)
Exploring the origins of binding specificity through the computational redesign of calmodulinProceedings of the National Academy of Sciences of the United States of America, 100
M. Sippl (1990)
Calculation of conformational ensembles from potentials of mean force. An approach to the knowledge-based prediction of local structures in globular proteins.Journal of molecular biology, 213 4
X. Zou, and Sun, I. Kuntz (1999)
Inclusion of Solvation in Ligand Binding Free Energy Calculations Using the Generalized-Born ModelJournal of the American Chemical Society, 121
P. Block, C. Sotriffer, Ingo Dramburg, G. Klebe (2005)
AffinDB: a freely accessible database of affinities for protein–ligand complexes from the PDBNucleic Acids Research, 34
B. Berne, J. Weeks, R. Zhou (2009)
Dewetting and hydrophobic interaction in physical and biological systems.Annual review of physical chemistry, 60
A. Lindström, Lotta Edvinsson, A. Johansson, C. Andersson, Ida Andersson, F. Raubacher, A. Linusson (2011)
Postprocessing of Docked Protein-Ligand Complexes Using Implicit Solvation ModelsJournal of chemical information and modeling, 51 2
Wenfei Li, Jian Zhang, Jun Wang, Wei Wang (2008)
Metal-coupled folding of Cys2His2 zinc-finger.Journal of the American Chemical Society, 130 3
Yan Li, Zhihai Liu, Jie Li, Li Han, Jie Liu, Zhixiong Zhao, Renxiao Wang (2014)
Comparative Assessment of Scoring Functions on an Updated Benchmark: 1. Compilation of the Test SetJournal of chemical information and modeling, 54 6
Grigoryan (2009)
Design of protein-interaction specificity gives selective bZIP-binding peptidesNature, 458
P. Ballester, John Mitchell (2011)
Comments on "Leave-Cluster-Out Cross-Validation Is Appropriate for Scoring Functions Derived from Diverse Protein Data Sets": Significance for the Validation of Scoring FunctionsJournal of chemical information and modeling, 51 8
Jin Wang, R. Oliveira, Xiakun Chu, P. Whitford, J. Chahine, Wei Han, E. Wang, J. Onuchic, V. Leite (2012)
Topography of funneled landscapes determines the thermodynamics and kinetics of protein foldingProceedings of the National Academy of Sciences, 109
Zhihai Liu, Yan Li, Li Han, Jie Li, Jie Liu, Zhixiong Zhao, Wei Nie, Yuchen Liu, Renxiao Wang (2015)
PDB-wide collection of binding data: current status of the PDBbind databaseBioinformatics, 31 3
Shengyou Huang, X. Zou (2014)
A knowledge-based scoring function for protein-RNA interactions derived from a statistical mechanics-based iterative methodNucleic Acids Research, 42
Yu Liu, Lei-feng Zhao, Wentao Li, Dongyu Zhao, Miao Song, Yongliang Yang (2013)
FIPSDock: A new molecular docking technique driven by fully informed swarm optimization algorithmJournal of Computational Chemistry, 34
Olgun Guvench, C. Brooks (2004)
Efficient approximate all‐atom solvent accessible surface area method parameterized for folded and denatured protein conformationsJournal of Computational Chemistry, 25
Zhijie Liu, Fenglou Mao, Jun-tao Guo, Bo Yan, Pengju Wang, Youxing Qu, Ying Xu (2005)
Quantitative evaluation of protein–DNA interactions using an optimized knowledge-based potentialNucleic Acids Research, 33
Zhiqiang Yan, Xiliang Zheng, E. Wang, Jin Wang (2013)
Thermodynamic and kinetic specificities of ligand bindingChemical Science, 4
Noel O'Boyle, M. Banck, Craig James, Chris Morley, T. Vandermeersch, G. Hutchison (2011)
Open Babel: An open chemical toolboxJournal of Cheminformatics, 3
Renxiao Wang, Xueliang Fang, Yipin Lu, Shaomeng Wang (2004)
The PDBbind database: collection of binding affinities for protein-ligand complexes with known three-dimensional structures.Journal of medicinal chemistry, 47 12
ABSTRACT Solvation effect is an important factor for protein–ligand binding in aqueous water. Previous scoring function of protein–ligand interactions rarely incorporates the solvation model into the quantification of protein–ligand interactions, mainly due to the immense computational cost, especially in the structure‐based virtual screening, and nontransferable application of independently optimized atomic solvation parameters. In order to overcome these barriers, we effectively combine knowledge‐based atom–pair potentials and the atomic solvation energy of charge‐independent implicit solvent model in the optimization of binding affinity and specificity. The resulting scoring functions with optimized atomic solvation parameters is named as specificity and affinity with solvation effect (SPA‐SE). The performance of SPA‐SE is evaluated and compared to 20 other scoring functions, as well as SPA. The comparative results show that SPA‐SE outperforms all other scoring functions in binding affinity prediction and “native” pose identification. Our optimization validates that solvation effect is an important regulator to the stability and specificity of protein–ligand binding. The development strategy of SPA‐SE sets an example for other scoring function to account for the solvation effect in biomolecular recognitions. Proteins 2015; 83:1632–1642. © 2015 Wiley Periodicals, Inc.
Proteins: Structure Function and Bioinformatics – Wiley
Published: Sep 1, 2015
Read and print from thousands of top scholarly journals.
Already have an account? Log in
Bookmark this article. You can see your Bookmarks on your DeepDyve Library.
To save an article, log in first, or sign up for a DeepDyve account if you don’t already have one.
Copy and paste the desired citation format or use the link below to download a file formatted for EndNote
Access the full text.
Sign up today, get DeepDyve free for 14 days.
All DeepDyve websites use cookies to improve your online experience. They were placed on your computer when you launched this website. You can change your cookie settings through your browser.