Fast Docking on Graphics Processing Units via Ray-Casting

Khar, Karen R.; Goldschmidt, Lukasz; Karanicolas, John

doi:10.1371/journal.pone.0070661

Cited by 8 publications

(12 citation statements)

References 48 publications

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“…[37] Another VS application implemented in the cloud is BINDSURF (Table 4), [178,179] an efficient and fast blind method for the determination of protein binding sites, which uses the massively parallel architecture of GPUs for unbiased prescreening of large ligand databases. Other algorithms taking advantage of the speedup offered by GPUs were developed by Khar et al [180] and Heinzerling et al [181] Several recently designed workflows were particularly aimed at bringing together structure-based approaches with ligandbased in silico methods (Table 5). Screening databases and decoy sets for virtual screening benchmarking ZINC remains the key resource for VS. [182] At the time of writing, the ZINC website contains >35 million purchasable compounds in downloadable ready-to-dock 3D formats.…”

Section: Automation/integration/parallelization/distributed Computingmentioning

confidence: 99%

Improvements, trends, and new ideas in molecular docking: 2012–2013 in review

Yuriev

Holien

Ramsland

2015

J of Molecular Recognition

240

165

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Molecular docking is a computational method for predicting the placement of ligands in the binding sites of their receptor(s). In this review, we discuss the methodological developments that occurred in the docking field in 2012 and 2013, with a particular focus on the more difficult aspects of this computational discipline. The main challenges and therefore focal points for developments in docking, covered in this review, are receptor flexibility, solvation, scoring, and virtual screening. We specifically deal with such aspects of molecular docking and its applications as selection criteria for constructing receptor ensembles, target dependence of scoring functions, integration of higher-level theory into scoring, implicit and explicit handling of solvation in the binding process, and comparison and evaluation of docking and scoring methods.

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Section: Automation/integration/parallelization/distributed Computingmentioning

confidence: 99%

Improvements, trends, and new ideas in molecular docking: 2012–2013 in review

Yuriev

Holien

Ramsland

2015

J of Molecular Recognition

240

165

View full text Add to dashboard Cite

show abstract

“…(B) For each protein-ligand complex in our set ( S1 Table ), we timed DARC when docking the ligand back into its cognate receptor, using either GPU+CPU or CPU alone. We find an average speedup of 90-fold when using the GPU (red line), an improvement over the 27-fold speedup we achieved in our original GPU implementation of DARC [ 26 ]. (C) The GPU led to an even greater speedup over the analogous calculation on the CPU when electrostatic complementarity was included in both calculations (190-fold speedup).…”

Section: Resultsmentioning

confidence: 88%

“…Previously, we adapted DARC such that the ray-casting step would be carried out on the GPU; meanwhile, the central processing unit (CPU) would be responsible for updating the ligand coordinates and repeatedly passing these to the GPU. This GPU implementation proved extremely useful, because it led to a speedup of about 27-fold in typical-use cases, as compared to the time required to carry out the analogous calculations using the CPU alone [ 26 ].…”

Section: Resultsmentioning

confidence: 99%

“…(4) We incorporated electrostatics into the DARC scoring function, allowing simultaneous optimization of both shape- and electrostatic-complementarity. (5) We identified the computational performance bottleneck in our previous implementation of GPU-DARC [ 26 ], and adjusted the control flow by transferring additional calculations onto the GPU to resolve this bottleneck.…”

Section: Introductionmentioning

confidence: 99%

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DARC 2.0: Improved Docking and Virtual Screening at Protein Interaction Sites

2015

Self Cite

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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.

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“…Zhang and Lange [28] also tackle sampling, finding that a replica exchange approach greatly improves conformational sampling during the low resolution stage of RosettaDock. Khar et al [29] have recently developed a ray-casting method (DARC) for small molecule docking and now demonstrate that its speed can be increased 25-fold via GPU-based computing, thereby enabling virtual screening of large compound libraries.…”

Section: Summary Of Papersmentioning

confidence: 99%