Abstract Motivation Studying the transport paths of ligands, solvents, or ions in transmembrane proteins and proteins with buried binding sites is fundamental to the understanding of their biological function. A detailed analysis of the structural features influencing the transport paths is also important for engineering proteins for biomedical and biotechnological applications. Results CAVER Analyst 2.0 is a software tool for quantitative analysis and real-time visualization of tunnels and channels in static and dynamic structures. This version provides the users with many new functions, including advanced techniques for intuitive visual inspection of the spatiotemporal behavior of tunnels and channels. Novel integrated algorithms allow an efficient analysis and data reduction in large protein structures and molecular dynamic simulations. Availability and implementation CAVER Analyst 2.0 is a multi-platform standalone Java-based application. Binaries and documentation are freely available at www.caver.cz. Contact firstname.lastname@example.org or email@example.com Supplementary information Supplementary data are available at Bioinformatics online. 1 Introduction The importance of access tunnels in proteins has been demonstrated by many studies in the last decade (Kingsley et al., 2015; Marques et al., 2017). Their examination in dynamical protein ensembles became a standard technique for studying important biochemical phenomena, designing new biocatalysts, materials or drugs (Brezovsky et al., 2016; Gora et al., 2013; Koudelakova et al., 2013; Liskova et al., 2015; Yu et al., 2013). With the current computational capacity, it becomes affordable to obtain molecular dynamics (MD) trajectories up to the microsecond time scales. This trend requires new approaches to explore the large datasets, as it becomes impracticable to observe such simulations in a frame-by-frame manner. Feature extraction and aggregation techniques, giving a guidance and overview of interesting sites and properties of tunnels over time, are therefore necessary. To follow this trend, we are introducing CAVER Analyst 2.0, which enables visual exploration of protein tunnels and channels even in microsecond-long MD simulations. This was achieved by introducing novel visualization approaches and other advanced functions, which enhance the manipulation of such simulation data. CAVER Analyst 2.0 introduces significant changes and improvements, focusing especially on large data processing, but also on providing the users with a complete description of the structural and biophysical features of protein tunnels and channels. 2 Features Tunnel, channel and cavity calculation: CAVER Analyst 2.0 integrates the most up-to-date CAVER tool with the set of algorithms for: (i) identification of tunnels and channels in proteins, (ii) analysis of tunnels and channels in large MD simulations and (iii) identification of protein pockets and inner cavities. The algorithms are being continuously developed to provide the most accurate and computationally efficient description of these specific structural features. The tunnel calculation can be launched directly from the CAVER Analyst interface, which offers the basic and advanced calculation settings modes. For compatibility reasons, we keep the user interface of the Tunnel Computation window consistent with the version 1.0 (Kozlikova et al., 2014). We have also improved the algorithm for the cavity detection (Manak et al., 2017). Visual analysis of tunnels: New visualization techniques present an important contribution to CAVER Analyst 2.0. They were mostly designed with the purpose of tunnel exploration in long MD trajectories (in AMBER, GROMACS, CHARMM formats), focusing on the changes of the tunnel properties and its surrounding residues over the time. Both techniques aggregate the spatial information to a single overview image so the user can get the information about the main trends in the tunnel behavior, regardless the MD simulation length. The first technique (Byska et al., 2015) focuses on the visual representation of the shape of tunnel cross-cut at a specific site, e.g. its bottleneck. It shows its changes over time and physico-chemical properties of the amino acids lining that section (Fig. 1 and Supplementary Fig. S2). The central part is formed by the contour, which is defined by the cross-cut through a given tunnel. Each time step generates one contour and their overlay shows the shape of the cross-cut over the time. The rectangular bars surrounding the contours represent the respective lining amino acids colored by their physico-chemical properties. The second technique (Byska et al., 2016) shows the width profile of a selected tunnel along the tunnel centerline (Fig. 1 and Supplementary Fig. S1). The amino acids forming the tunnel boundary are presented below the profile using a set of lines. The length of these lines illustrates the portion of the tunnel influenced by a particular amino acid. When dealing with dynamic ensembles, the lines represent the residues and their relative influence averaged over the entire simulation. Using a vertical slider, the user can specify a given section of the tunnel, for which the contour representation is calculated and visualized. The Supplementary Material demonstrates the applicability of these visualizations with two case studies focused on the engineering of tunnels aimed at improving protein stability and catalytic activity. Fig. 1. View largeDownload slide CAVER Analyst 2.0 user interface Fig. 1. View largeDownload slide CAVER Analyst 2.0 user interface Mutagenesis: Engineering proteins typically requires the design and modeling of mutations. CAVER Analyst 2.0 supports this task by the new Mutagenesis Window (Supplementary Fig. S3). It offers the possibility to design one or more mutations in selected positions of a static molecule structure, which can be further used to recalculate the tunnels and visually compare the differences with the template. The newly designed molecule can be exported, upon which additional modeling studies, such as MD simulations, can be performed. The obtained trajectories can be loaded again to CAVER Analyst 2.0 and visually explored. The mutagenesis may use two different libraries of residue rotamers (Dunbrack et al., 2011; http://bioserv.rpbs.univ-paris-diderot.fr/software.html). Buffering: CAVER Analyst 2.0 enables to manipulate MD simulations of arbitrary length instantly, which ensures that the tool will be usable with simulations containing orders of magnitude higher number of time steps than now. Other features: CAVER Analyst 2.0 offers advanced Measurement Window, the Clip Plane Window enabling to operate several independent clip planes and slices at once (Supplementary Fig. S4), improved manipulation of the protein structure, e.g. removing selected atoms, exporting structures from selected objects, video recording, high-resolution screenshots and the accessibility to common actions via the command line. 3 Implementation CAVER Analyst 2.0 is a multi-platform JAVA-based software. It can run on both 32- and 64-bit system architectures with JAVA 1.8 (see Supplementary Material for implementation details). Funding Development of the software has been supported by the Czech Science Foundation (17-07690S and 16-06096S); by the Ministry of Education (LO1214, LO1506, LQ1605, LM2015047 and LM2015055); by PhysioIllustration research project 218023 funded by the Norwegian Research Council; and by National Science Centre, Poland 2017/25/B/NZ1/01307. Computational resources for simulations were supplied by the Ministry of Education, Youth and Sports of the Czech Republic under the Projects CESNET (LM2015042) and CERIT-Scientific Cloud (LM2015085). Conflict of Interest: none declared. References Brezovsky J. et al. ( 2016) Engineering a de Novo Transport Tunnel. ACS Catalysis , 6, 7597– 7610. Google Scholar CrossRef Search ADS Byska J. et al. ( 2015) MoleCollar and Tunnel Heat Map Visualization for Conveying Spatio-Temporo-Chemical Properties Across and Along Protein Voids. Computer Graphics Forum , 34, 1– 10. Google Scholar CrossRef Search ADS Byska J. et al. ( 2016) AnimoAminoMiner: exploration of Protein Tunnels and their Properties in Molecular Dynamics. IEEE Transactions on Visualization and Computer Graphics , 22, 747– 756. Google Scholar CrossRef Search ADS PubMed Dunbrack R.L. et al. ( 2011) A Smoothed Backbone-Dependent Rotamer Library for Proteins Derived from Adaptive Kernel Density Estimates and Regressions. Structure , 19, 844– 858. Google Scholar CrossRef Search ADS PubMed Gora A. et al. ( 2013) Gates of Enzymes. Chemical Reviews , 113, 5871– 5923. Google Scholar CrossRef Search ADS PubMed Kingsley L.J. et al. ( 2015) Substrate Tunnels in Enzymes: structure-Function Relationships and Computational Methodology. Proteins , 83, 599– 611. Google Scholar CrossRef Search ADS PubMed Koudelakova T. et al. ( 2013) Engineering Enzyme Stability and Resistance to an Organic Cosolvent by Modification of Residues in the Access Tunnel. Angewandte Chemie International Edition , 52, 1959– 1963. Google Scholar CrossRef Search ADS PubMed Kozlikova B. et al. ( 2014) CAVER Analyst 1.0: graphic Tool for Interactive Visualization and Analysis of Tunnels and Channels in Protein Structures. Bioinformatics , 30, 2684– 2685. Google Scholar CrossRef Search ADS PubMed Liskova V. et al. ( 2015) Balancing the Stability-Activity Trade-Off by Fine-Tuning Dehalogenase Access Tunnels. ChemCatChem , 7, 648– 659. Google Scholar CrossRef Search ADS Manak M. et al. ( 2017) Interactive Analysis of Connoly Surfaces for Various Probes. Computer Graphics Forum , 36, 160– 172. Google Scholar CrossRef Search ADS Marques S. et al. ( 2017) Enzyme Tunnels and Gates as Relevant Targets in Drug Design. Medicinal Research Reviews , 37, 1095– 1139. Google Scholar CrossRef Search ADS PubMed Yu X. et al. ( 2013) Conformational Diversity and Ligand Tunnels of Mammalian Cytochrome P450s. Biotechnology and Applied Biochemistry , 60, 134– 145. Google Scholar CrossRef Search ADS PubMed © The Author(s) 2018. Published by Oxford University Press. This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited. For commercial re-use, please contact firstname.lastname@example.org
Bioinformatics – Oxford University Press
Published: May 8, 2018
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