Dataset Information

Protein-protein docking with F(2)Dock 2.0 and GB-rerank.

ABSTRACT:

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.

SUBMITTER: Chowdhury R

PROVIDER: S-EPMC3590208 | biostudies-literature | 2013

REPOSITORIES: biostudies-literature

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Publications

Protein-protein docking with F(2)Dock 2.0 and GB-rerank.

Chowdhury Rezaul R Rasheed Muhibur M Keidel Donald D Moussalem Maysam M Olson Arthur A Sanner Michel M Bajaj Chandrajit C

PloS one 20130306 3

<h4>Motivation</h4>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 filte ...[more]

PMID: 23483883

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Project description:Over the past decade, protein-protein interactions have emerged as attractive but challenging targets for therapeutic intervention using small molecules. Due to the relatively flat surfaces that typify protein interaction sites, modern virtual screening tools developed for optimal performance against "traditional" protein targets perform less well when applied instead at protein interaction sites. Previously, we described a docking method specifically catered to the shallow binding modes characteristic of small-molecule inhibitors of protein interaction sites. This method, called DARC (Docking Approach using Ray Casting), operates by comparing the topography of the protein surface when "viewed" from a vantage point inside the protein against the topography of a bound ligand when "viewed" from the same vantage point. Here, we present five key enhancements to DARC. First, we use multiple vantage points to more accurately determine protein-ligand surface complementarity. Second, we describe a new scheme for rapidly determining optimal weights in the DARC scoring function. Third, we incorporate sampling of ligand conformers "on-the-fly" during docking. Fourth, we move beyond simple shape complementarity and introduce a term in the scoring function to capture electrostatic complementarity. Finally, we adjust the control flow in our GPU implementation of DARC to achieve greater speedup of these calculations. At each step of this study, we evaluate the performance of DARC in a "pose recapitulation" experiment: predicting the binding mode of 25 inhibitors each solved in complex with its distinct target protein (a protein interaction site). Whereas the previous version of DARC docked only one of these inhibitors to within 2 Å RMSD of its position in the crystal structure, the newer version achieves this level of accuracy for 12 of the 25 complexes, corresponding to a statistically significant performance improvement (p < 0.001). Collectively then, we find that the five enhancements described here--which together make up DARC 2.0--lead to dramatically improved speed and performance relative to the original DARC method.

Dataset Information

Protein-protein docking with F(2)Dock 2.0 and GB-rerank.

Motivation

Results

Availability

Publications

Protein-protein docking with F(2)Dock 2.0 and GB-rerank.

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