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      AnkPlex: algorithmic structure for refinement of near-native ankyrin-protein docking

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          Abstract

          Background

          Computational analysis of protein-protein interaction provided the crucial information to increase the binding affinity without a change in basic conformation. Several docking programs were used to predict the near-native poses of the protein-protein complex in 10 top-rankings. The universal criteria for discriminating the near-native pose are not available since there are several classes of recognition protein. Currently, the explicit criteria for identifying the near-native pose of ankyrin-protein complexes (APKs) have not been reported yet.

          Results

          In this study, we established an ensemble computational model for discriminating the near-native docking pose of APKs named “AnkPlex”. A dataset of APKs was generated from seven X-ray APKs, which consisted of 3 internal domains, using the reliable docking tool ZDOCK. The dataset was composed of 669 and 44,334 near-native and non-near-native poses, respectively, and it was used to generate eleven informative features. Subsequently, a re-scoring rank was generated by AnkPlex using a combination of a decision tree algorithm and logistic regression. AnkPlex achieved superior efficiency with ≥1 near-native complexes in the 10 top-rankings for nine X-ray complexes compared to ZDOCK, which only obtained six X-ray complexes. In addition, feature analysis demonstrated that the van der Waals feature was the dominant near-native pose out of the potential ankyrin-protein docking poses.

          Conclusion

          The AnkPlex model achieved a success at predicting near-native docking poses and led to the discovery of informative characteristics that could further improve our understanding of the ankyrin-protein complex. Our computational study could be useful for predicting the near-native poses of binding proteins and desired targets, especially for ankyrin-protein complexes. The AnkPlex web server is freely accessible at http://ankplex.ams.cmu.ac.th.

          Electronic supplementary material

          The online version of this article (doi:10.1186/s12859-017-1628-6) contains supplementary material, which is available to authorized users.

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          Most cited references29

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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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            Ridge Estimators in Logistic Regression

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              ClusPro: an automated docking and discrimination method for the prediction of protein complexes.

              Predicting protein interactions is one of the most challenging problems in functional genomics. Given two proteins known to interact, current docking methods evaluate billions of docked conformations by simple scoring functions, and in addition to near-native structures yield many false positives, i.e. structures with good surface complementarity but far from the native. We have developed a fast algorithm for filtering docked conformations with good surface complementarity, and ranking them based on their clustering properties. The free energy filters select complexes with lowest desolvation and electrostatic energies. Clustering is then used to smooth the local minima and to select the ones with the broadest energy wells-a property associated with the free energy at the binding site. The robustness of the method was tested on sets of 2000 docked conformations generated for 48 pairs of interacting proteins. In 31 of these cases, the top 10 predictions include at least one near-native complex, with an average RMSD of 5 A from the native structure. The docking and discrimination method also provides good results for a number of complexes that were used as targets in the Critical Assessment of PRedictions of Interactions experiment. The fully automated docking and discrimination server ClusPro can be found at http://structure.bu.edu
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                Author and article information

                Contributors
                miisuii@hotmail.com
                watshara.sho@mahidol.ac.th
                vannajan@gmail.com
                kitidee_010@hotmail.com
                asimi002@hotmail.com
                Journal
                BMC Bioinformatics
                BMC Bioinformatics
                BMC Bioinformatics
                BioMed Central (London )
                1471-2105
                19 April 2017
                19 April 2017
                2017
                : 18
                : 220
                Affiliations
                [1 ]ISNI 0000 0000 9039 7662, GRID grid.7132.7, Division of Clinical Immunology, Department of Medical Technology, , Faculty of Associated Medical Sciences, Chiang Mai University, ; Chiang Mai, 50200 Thailand
                [2 ]ISNI 0000 0000 9039 7662, GRID grid.7132.7, , Center of Biomolecular Therapy and Diagnostic, Faculty of Associated Medical Sciences, Chiang Mai University, ; Chiang Mai, 50200 Thailand
                [3 ]ISNI 0000 0004 1937 0490, GRID grid.10223.32, , Center of Data Mining and Biomedical Informatics, Faculty of Medical Technology, Mahidol University, ; Bangkok, 10700 Thailand
                [4 ]GRID grid.450348.e, , Thailand Center of Excellence in Physics, Commission on Higher Education, ; Bangkok, 10400 Thailand
                [5 ]ISNI 0000 0001 2308 5949, GRID grid.10347.31, Department of Chemistry, , Faculty of Science, University of Malaya, ; Kuala Lumpur, 50603 Malaysia
                [6 ]ISNI 0000 0004 1937 0490, GRID grid.10223.32, , Center for Research and Innovation, Faculty of Medical Technology, Mahidol University, ; Bangkok, 10700 Thailand
                Article
                1628
                10.1186/s12859-017-1628-6
                5395911
                28424069
                4a0ab3ca-b608-4b8d-b712-a3b38bbe95ec
                © The Author(s). 2017

                Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License ( http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver ( http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.

                History
                : 30 June 2016
                : 7 April 2017
                Funding
                Funded by: The Cluster and Program Management Office (CPMO)
                Funded by: The National Science and Technology Development Agency (NSTDA)
                Funded by: Thailand Research Fund (TRF)
                Funded by: The National Research Council of Thailand (NRCT)
                Funded by: The Health Systems Research Institute (HSRI)
                Funded by: The National Research University project under the Thailand’s Office of the Commission on Higher Education (NRU)
                Funded by: The Center of Biomolecular Therapy and Diagnostic (CBTD)
                Funded by: The Mahidol University Talent Management Program to WS
                Funded by: The Mahidol University Talent Management Program to KK
                Categories
                Research Article
                Custom metadata
                © The Author(s) 2017

                Bioinformatics & Computational biology
                ankyrin-protein complexes,near-native docking pose,machine learning methods,decision tree,logistic regression model,ankplex

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