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      Re-Docking Scheme for Generating Near-Native Protein Complexes by Assembling Residue Interaction Fingerprints

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          Abstract

          Interaction profile method is a useful method for processing rigid-body docking. After the docking process, the resulting set of docking poses could be classified by calculating similarities among them using these interaction profiles to search for near-native poses. However, there are some cases where the near-native poses are not included in this set of docking poses even when the bound-state structures are used. Therefore, we have developed a method for generating near-native docking poses by introducing a re-docking process. We devised a method for calculating the profile of interaction fingerprints by assembling protein complexes after determining certain core-protein complexes. For our analysis, we used 44 bound-state protein complexes selected from the ZDOCK benchmark dataset ver. 2.0, including some protein pairs none of which generated near-native poses in the docking process. Consequently, after the re-docking process we obtained profiles of interaction fingerprints, some of which yielded near-native poses. The re-docking process involved searching for possible docking poses in a restricted area using the profile of interaction fingerprints. If the profile includes interactions identical to those in the native complex, we obtained near-native docking poses. Accordingly, near-native poses were obtained for all bound-state protein complexes examined here. Application of interaction fingerprints to the re-docking process yielded structures with more native interactions, even when a docking pose, obtained following the initial docking process, contained only a small number of native amino acid interactions. Thus, utilization of the profile of interaction fingerprints in the re-docking process yielded more near-native poses.

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          LIGPLOT: a program to generate schematic diagrams of protein-ligand interactions.

          The LIGPLOT program automatically generates schematic 2-D representations of protein-ligand complexes from standard Protein Data Bank file input. The output is a colour, or black-and-white, PostScript file giving a simple and informative representation of the intermolecular interactions and their strengths, including hydrogen bonds, hydrophobic interactions and atom accessibilities. The program is completely general for any ligand and can also be used to show other types of interaction in proteins and nucleic acids. It was designed to facilitate the rapid inspection of many enzyme complexes, but has found many other applications.
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            ZDOCK: an initial-stage protein-docking algorithm.

            The development of scoring functions is of great importance to protein docking. Here we present a new scoring function for the initial stage of unbound docking. It combines our recently developed pairwise shape complementarity with desolvation and electrostatics. We compare this scoring function with three other functions on a large benchmark of 49 nonredundant test cases and show its superior performance, especially for the antibody-antigen category of test cases. For 44 test cases (90% of the benchmark), we can retain at least one near-native structure within the top 2000 predictions at the 6 degrees rotational sampling density, with an average of 52 near-native structures per test case. The remaining five difficult test cases can be explained by a combination of poor binding affinity, large backbone conformational changes, and our algorithm's strong tendency for identifying large concave binding pockets. All four scoring functions have been integrated into our Fast Fourier Transform based docking algorithm ZDOCK, which is freely available to academic users at http://zlab.bu.edu/~ rong/dock. Copyright 2003 Wiley-Liss, Inc.
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              Accelerating Protein Docking in ZDOCK Using an Advanced 3D Convolution Library

              Computational prediction of the 3D structures of molecular interactions is a challenging area, often requiring significant computational resources to produce structural predictions with atomic-level accuracy. This can be particularly burdensome when modeling large sets of interactions, macromolecular assemblies, or interactions between flexible proteins. We previously developed a protein docking program, ZDOCK, which uses a fast Fourier transform to perform a 3D search of the spatial degrees of freedom between two molecules. By utilizing a pairwise statistical potential in the ZDOCK scoring function, there were notable gains in docking accuracy over previous versions, but this improvement in accuracy came at a substantial computational cost. In this study, we incorporated a recently developed 3D convolution library into ZDOCK, and additionally modified ZDOCK to dynamically orient the input proteins for more efficient convolution. These modifications resulted in an average of over 8.5-fold improvement in running time when tested on 176 cases in a newly released protein docking benchmark, as well as substantially less memory usage, with no loss in docking accuracy. We also applied these improvements to a previous version of ZDOCK that uses a simpler non-pairwise atomic potential, yielding an average speed improvement of over 5-fold on the docking benchmark, while maintaining predictive success. This permits the utilization of ZDOCK for more intensive tasks such as docking flexible molecules and modeling of interactomes, and can be run more readily by those with limited computational resources.
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                Author and article information

                Contributors
                Role: Editor
                Journal
                PLoS One
                PLoS ONE
                plos
                plosone
                PLoS ONE
                Public Library of Science (San Francisco, USA )
                1932-6203
                2013
                16 July 2013
                : 8
                : 7
                : e69365
                Affiliations
                [1 ]Department of Physics, Chuo University, Bunkyo-ku, Tokyo, Japan
                [2 ]Grand Challenge Applications Project for Life Sciences, Next-Generation Integrated Simulation of Living Matter, Computational Science Research Program, Riken, Wako, Saitama, Japan
                [3 ]Department of Computer Science, Graduate School of Information Science and Engineering, Tokyo Institute of Technology, Meguro-ku, Tokyo, Japan
                [4 ]Japan Society for the Promotion of Science, Tokyo, Japan
                [5 ]Education Academy of Computational Life Sciences, Tokyo Institute of Technology, Meguro-ku, Tokyo, Japan
                [6 ]Computational Bioinformatics Research Center, AIST, Koto-ku, Tokyo, Japan
                Russian Academy of Sciences, Institute for Biological Instrumentation, Russian Federation
                Author notes

                Competing Interests: The authors have declared that no competing interests exist.

                Conceived and designed the experiments: NU TH. Performed the experiments: YM MO YA. Analyzed the data: NU. Contributed reagents/materials/analysis tools: NU YM MO. Wrote the paper: NU TH.

                Article
                PONE-D-13-12342
                10.1371/journal.pone.0069365
                3712918
                23874954
                52b9a010-bd88-4595-9b33-116c957782c3
                Copyright @ 2013

                This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

                History
                : 25 March 2013
                : 8 June 2013
                Page count
                Pages: 10
                Funding
                Funded by Next-Generation Integrated Simulation of Living Matter ( http://www.csrp.riken.jp/index_e.html). Part of the results were obtained by using the K computer at the RIKEN Advanced Institute for Computational Science (research proposal number hp120131). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
                Categories
                Research Article
                Biology
                Biochemistry
                Proteins
                Globular Proteins
                Protein Interactions
                Protein Structure
                Biophysics
                Biophysics Simulations
                Biophysics Theory
                Computational Biology
                Macromolecular Structure Analysis
                Protein Structure
                Biophysic Al Simulations
                Proteomics
                Protein Interactions

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                Uncategorized

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