Cargando…
Protein-Protein Docking with F(2)Dock 2.0 and GB-Rerank
MOTIVATION: Computational simulation of protein-protein docking can expedite the process of molecular modeling and drug discovery. This paper reports on our new F(2) Dock protocol which improves the state of the art in initial stage rigid body exhaustive docking search, scoring and ranking by introd...
Autores principales: | , , , , , , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Public Library of Science
2013
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3590208/ https://www.ncbi.nlm.nih.gov/pubmed/23483883 http://dx.doi.org/10.1371/journal.pone.0051307 |
_version_ | 1782261829254250496 |
---|---|
author | Chowdhury, Rezaul Rasheed, Muhibur Keidel, Donald Moussalem, Maysam Olson, Arthur Sanner, Michel Bajaj, Chandrajit |
author_facet | Chowdhury, Rezaul Rasheed, Muhibur Keidel, Donald Moussalem, Maysam Olson, Arthur Sanner, Michel Bajaj, Chandrajit |
author_sort | Chowdhury, Rezaul |
collection | PubMed |
description | MOTIVATION: Computational simulation of protein-protein docking can expedite the process of molecular modeling and drug discovery. This paper reports on our new F(2) Dock protocol which improves the state of the art in initial stage rigid body exhaustive docking search, scoring and ranking by introducing improvements in the shape-complementarity and electrostatics affinity functions, a new knowledge-based interface propensity term with FFT formulation, a set of novel knowledge-based filters and finally a solvation energy (GBSA) based reranking technique. Our algorithms are based on highly efficient data structures including the dynamic packing grids and octrees which significantly speed up the computations and also provide guaranteed bounds on approximation error. RESULTS: The improved affinity functions show superior performance compared to their traditional counterparts in finding correct docking poses at higher ranks. We found that the new filters and the GBSA based reranking individually and in combination significantly improve the accuracy of docking predictions with only minor increase in computation time. We compared F(2) Dock 2.0 with ZDock 3.0.2 and found improvements over it, specifically among 176 complexes in ZLab Benchmark 4.0, F(2) Dock 2.0 finds a near-native solution as the top prediction for 22 complexes; where ZDock 3.0.2 does so for 13 complexes. F(2) Dock 2.0 finds a near-native solution within the top 1000 predictions for 106 complexes as opposed to 104 complexes for ZDock 3.0.2. However, there are 17 and 15 complexes where F(2) Dock 2.0 finds a solution but ZDock 3.0.2 does not and vice versa; which indicates that the two docking protocols can also complement each other. AVAILABILITY: The docking protocol has been implemented as a server with a graphical client (TexMol) which allows the user to manage multiple docking jobs, and visualize the docked poses and interfaces. Both the server and client are available for download. Server: http://www.cs.utexas.edu/~bajaj/cvc/software/f2dock.shtml. Client: http://www.cs.utexas.edu/~bajaj/cvc/software/f2dockclient.shtml. |
format | Online Article Text |
id | pubmed-3590208 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2013 |
publisher | Public Library of Science |
record_format | MEDLINE/PubMed |
spelling | pubmed-35902082013-03-12 Protein-Protein Docking with F(2)Dock 2.0 and GB-Rerank Chowdhury, Rezaul Rasheed, Muhibur Keidel, Donald Moussalem, Maysam Olson, Arthur Sanner, Michel Bajaj, Chandrajit PLoS One Research Article MOTIVATION: Computational simulation of protein-protein docking can expedite the process of molecular modeling and drug discovery. This paper reports on our new F(2) Dock protocol which improves the state of the art in initial stage rigid body exhaustive docking search, scoring and ranking by introducing improvements in the shape-complementarity and electrostatics affinity functions, a new knowledge-based interface propensity term with FFT formulation, a set of novel knowledge-based filters and finally a solvation energy (GBSA) based reranking technique. Our algorithms are based on highly efficient data structures including the dynamic packing grids and octrees which significantly speed up the computations and also provide guaranteed bounds on approximation error. RESULTS: The improved affinity functions show superior performance compared to their traditional counterparts in finding correct docking poses at higher ranks. We found that the new filters and the GBSA based reranking individually and in combination significantly improve the accuracy of docking predictions with only minor increase in computation time. We compared F(2) Dock 2.0 with ZDock 3.0.2 and found improvements over it, specifically among 176 complexes in ZLab Benchmark 4.0, F(2) Dock 2.0 finds a near-native solution as the top prediction for 22 complexes; where ZDock 3.0.2 does so for 13 complexes. F(2) Dock 2.0 finds a near-native solution within the top 1000 predictions for 106 complexes as opposed to 104 complexes for ZDock 3.0.2. However, there are 17 and 15 complexes where F(2) Dock 2.0 finds a solution but ZDock 3.0.2 does not and vice versa; which indicates that the two docking protocols can also complement each other. AVAILABILITY: The docking protocol has been implemented as a server with a graphical client (TexMol) which allows the user to manage multiple docking jobs, and visualize the docked poses and interfaces. Both the server and client are available for download. Server: http://www.cs.utexas.edu/~bajaj/cvc/software/f2dock.shtml. Client: http://www.cs.utexas.edu/~bajaj/cvc/software/f2dockclient.shtml. Public Library of Science 2013-03-06 /pmc/articles/PMC3590208/ /pubmed/23483883 http://dx.doi.org/10.1371/journal.pone.0051307 Text en © 2013 Chowdhury et al http://creativecommons.org/licenses/by/4.0/ 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 properly credited. |
spellingShingle | Research Article Chowdhury, Rezaul Rasheed, Muhibur Keidel, Donald Moussalem, Maysam Olson, Arthur Sanner, Michel Bajaj, Chandrajit Protein-Protein Docking with F(2)Dock 2.0 and GB-Rerank |
title | Protein-Protein Docking with F(2)Dock 2.0 and GB-Rerank |
title_full | Protein-Protein Docking with F(2)Dock 2.0 and GB-Rerank |
title_fullStr | Protein-Protein Docking with F(2)Dock 2.0 and GB-Rerank |
title_full_unstemmed | Protein-Protein Docking with F(2)Dock 2.0 and GB-Rerank |
title_short | Protein-Protein Docking with F(2)Dock 2.0 and GB-Rerank |
title_sort | protein-protein docking with f(2)dock 2.0 and gb-rerank |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3590208/ https://www.ncbi.nlm.nih.gov/pubmed/23483883 http://dx.doi.org/10.1371/journal.pone.0051307 |
work_keys_str_mv | AT chowdhuryrezaul proteinproteindockingwithf2dock20andgbrerank AT rasheedmuhibur proteinproteindockingwithf2dock20andgbrerank AT keideldonald proteinproteindockingwithf2dock20andgbrerank AT moussalemmaysam proteinproteindockingwithf2dock20andgbrerank AT olsonarthur proteinproteindockingwithf2dock20andgbrerank AT sannermichel proteinproteindockingwithf2dock20andgbrerank AT bajajchandrajit proteinproteindockingwithf2dock20andgbrerank |