Improved cluster ranking in protein–protein docking using a regression approach

Computational and Structural Biotechnology Journal - Tập 19 - Trang 2269-2278 - 2021
Shahabeddin Sotudian1, Israel T. Desta2, Nasser Hashemi1, Shahrooz Zarbafian1, Dima Kozakov3, Pirooz Vakili1, Sandor Vajda2,4, Ioannis Ch. Paschalidis1,2,5
1Division of Systems Engineering, Boston University, Boston, USA
2Department of Biomedical Engineering, Boston University
3Laufer Center for Physical and Quantitative Biology, Institute for Advanced Computational Sciences, Stony Brook University, Stony Brook, USA
4Department of Chemistry, Boston University
5Department of Electrical & Computer Engineering, and Faculty for Computing & Data Sciences, Boston University

Tài liệu tham khảo

Jones, 1996, Principles of protein-protein interactions, Proc Natl Acad Sci USA, 93, 13, 10.1073/pnas.93.1.13 Alberts, 1998, The cell as a collection of protein machines: Preparing the next generation of molecular biologists, Cell, 92, 291, 10.1016/S0092-8674(00)80922-8 Petry, 2016, Mechanisms of mitotic spindle assembly, Annu Rev Biochem, 85, 659, 10.1146/annurev-biochem-060815-014528 Camacho, 2000, Scoring docked conformations generated by rigid-body protein-protein docking, Proteins: Structure, Funct Gen, 40, 525 Chuang, 2008, DARS (decoys as the reference state) potentials for protein-protein docking, Biophys J, 95, 4217, 10.1529/biophysj.108.135814 Gabb, 1997, Modelling protein docking using shape complementarity, electrostatics and biochemical information, J Mol Biol, 272, 106, 10.1006/jmbi.1997.1203 Chen, 2003, Zdock: an initial-stage protein-docking algorithm, Proteins: Structure, Funct Genet, 52, 80 Tovchigrechko, 2006, GRAMM-X public web server for protein–protein docking, Nucl Acids Res, 34, W310, 10.1093/nar/gkl206 Yan, 2020, The HDOCK server for integrated protein–protein docking, Nat Protocols, 15, 1829, 10.1038/s41596-020-0312-x Desta, 2020, Performance and its limits in rigid body protein-protein docking, Structure, 28, 1071, 10.1016/j.str.2020.06.006 Vreven, 2015, Updates to the integrated protein-protein interaction benchmarks: Docking benchmark version 5 and affinity benchmark version 2, J Mol Biol, 3031, 10.1016/j.jmb.2015.07.016 Moal, 2017, IRaPPA: information retrieval based integration of biophysical models for protein assembly selection, Bioinformatics, 33, 1806, 10.1093/bioinformatics/btx068 Conte, 1999, The atomic structure of protein-protein recognition sites, J Mol Biol, 285, 2177, 10.1006/jmbi.1998.2439 Kozakov, 2017, The ClusPro web server for protein-protein docking, Nat Protoc, 12, 255, 10.1038/nprot.2016.169 Brenke, 2012, Application of asymmetric statistical potentials to antibody–protein docking, Bioinformatics, 28, 2608, 10.1093/bioinformatics/bts493 Pfeiffenberger, 2017, A machine learning approach for ranking clusters of docked protein-protein complexes by pairwise cluster comparison, Proteins: Structure, Funct Bioinf, 85, 528, 10.1002/prot.25218 Geurts, 2006, Extremely randomized trees, Mach Learn, 63, 3, 10.1007/s10994-006-6226-1 Sankar, 2016, Finding correct protein–protein docking models using proqdock, Bioinformatics, 32, i262, 10.1093/bioinformatics/btw257 Pierce, 2007, Zrank: Reranking protein docking predictions with an optimized energy function, Proteins: Structure, Funct Gen, 67, 1078 Pierce, 2008, A combination of rescoring and refinement significantly improves protein docking performance, Proteins: Structure, Funct Genet, 72, 270 Eismann S, Townshend RJ, Thomas N, Jagota M, Jing B, Dror R. Hierarchical, rotation-equivariant neural networks to predict the structure of protein complexes, arXiv preprint arXiv:2006.09275 (2020). Kozakov, 2006, PIPER: An FFT-based protein docking program with pairwise potentials, Proteins, 65, 392, 10.1002/prot.21117 Dong, 2013, Optimized atomic statistical potentials: assessment of protein interfaces and loops, Bioinformatics, 29, 3158, 10.1093/bioinformatics/btt560 Alford, 2017, The rosetta all-atom energy function for macromolecular modeling and design, J Chem Theory Comput, 13, 3031, 10.1021/acs.jctc.7b00125 Park, 2016, Simultaneous optimization of biomolecular energy functions on features from small molecules and macromolecules, J Chem Theory Comput, 12, 6201, 10.1021/acs.jctc.6b00819 Brooks, 2009, CHARMM: the biomolecular simulation program, J Comput Chem, 30, 1545, 10.1002/jcc.21287 Mirzaei, 2012, Rigid body energy minimization on manifolds for molecular docking, J Chem Theory Comput, 8, 4374, 10.1021/ct300272j Mirzaei, 2015, Energy minimization on manifolds for docking flexible molecules, J Chem Theory Comput, 11, 1063, 10.1021/ct500155t Basu, 2016, DockQ: a quality measure for protein-protein docking models, PloS One, 11, 10.1371/journal.pone.0161879 Hwang, 2010, Protein–protein docking benchmark version 4.0, Proteins: Struct Funct Bioinf, 78, 3111, 10.1002/prot.22830 Chen, 2018, A robust learning approach for regression models based on distributionally robust optimization, J Mach Learn Res, 19 Chen, 2020, Distributionally robust learning, Found Trends Optim, 4, 1, 10.1561/2400000026 Bertsimas, 2016, Best subset selection via a modern optimization lens, Ann Stat, 44, 813, 10.1214/15-AOS1388 Leaver-Fay, 2011, ROSETTA3: an object-oriented software suite for the simulation and design of macromolecules, 487, 545, 10.1016/B978-0-12-381270-4.00019-6