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Pharmacophore-based similarity scoring for DOCK.

Jiang L, Rizzo RC - J Phys Chem B (2014)

Bottom Line: When used alone, FMS scoring dramatically improves pose reproduction success to 93.5% (∼20% increase) and reduces sampling failures to 3.7% (∼6% drop) compared to the standard energy score (SGE) across 1043 protein-ligand complexes.The combined FMS+SGE function further improves success to 98.3%.For enrichment, incorporating pharmacophores during sampling and scoring, in most cases, also yield improved outcomes when docking and rank-ordering libraries of known actives and decoys to 15 systems.

View Article: PubMed Central - PubMed

Affiliation: Department of Applied Mathematics & Statistics, ‡Institute of Chemical Biology & Drug Discovery, §Laufer Center for Physical & Quantitative Biology, Stony Brook University , Stony Brook, New York 11794-3600, United States.

ABSTRACT
Pharmacophore modeling incorporates geometric and chemical features of known inhibitors and/or targeted binding sites to rationally identify and design new drug leads. In this study, we have encoded a three-dimensional pharmacophore matching similarity (FMS) scoring function into the structure-based design program DOCK. Validation and characterization of the method are presented through pose reproduction, crossdocking, and enrichment studies. When used alone, FMS scoring dramatically improves pose reproduction success to 93.5% (∼20% increase) and reduces sampling failures to 3.7% (∼6% drop) compared to the standard energy score (SGE) across 1043 protein-ligand complexes. The combined FMS+SGE function further improves success to 98.3%. Crossdocking experiments using FMS and FMS+SGE scoring, for six diverse protein families, similarly showed improvements in success, provided proper pharmacophore references are employed. For enrichment, incorporating pharmacophores during sampling and scoring, in most cases, also yield improved outcomes when docking and rank-ordering libraries of known actives and decoys to 15 systems. Retrospective analyses of virtual screenings to three clinical drug targets (EGFR, IGF-1R, and HIVgp41) using X-ray structures of known inhibitors as pharmacophore references are also reported, including a customized FMS scoring protocol to bias on selected regions in the reference. Overall, the results and fundamental insights gained from this study should benefit the docking community in general, particularly researchers using the new FMS method to guide computational drug discovery with DOCK.

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Crossdocking heatmaps using SGE, FMS, and FMS+SGE protocolsforcarbonic anhydrase (29 × 29 = 841 combinations).
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fig13: Crossdocking heatmaps using SGE, FMS, and FMS+SGE protocolsforcarbonic anhydrase (29 × 29 = 841 combinations).

Mentions: As expected, for more challengingcrossdocking experiments, matrixsuccess (Figure 12b) using any of the scoringfunctions are in general significantly lower than their diagonal counterparts(Figure 12a). As a baseline, use of SGE yieldsan averaged matrix success of 36.0% compared to the diagonal at 54.6%.In contrast to the diagonal results, interestingly, use of FMS alonefor crossdocking shows improvement over SGE in only two cases (CAand CPA). However, in all cases, the combined FMS+SGE function alwaysyields a better matrix success than does SGE. Analogous to the diagonalresults, the matrix outcomes (Figure 12b) similarlyreveal that carbonic anhydrase has the lowest overall matrix SGE successrate (17.8%) which increased the most among all systems tested whenusing FMS (48.8%) or FMS+SGE (52.1%). Figure 13 compares the heatmaps for carbonic anhydrase, derived from threeindependent docking sets of size 29 × 29 = 841 combinations,using SGE, FMS, and FMS+SGE methods. The maps visually highlight thatSGE failures are primarily due to scoring (green squares), pinpointwhich specific systems are involved, and indicate that FMS and FMS+SGEprotocols significantly improve docking outcomes (more blue squares).


Pharmacophore-based similarity scoring for DOCK.

Jiang L, Rizzo RC - J Phys Chem B (2014)

Crossdocking heatmaps using SGE, FMS, and FMS+SGE protocolsforcarbonic anhydrase (29 × 29 = 841 combinations).
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4306494&req=5

fig13: Crossdocking heatmaps using SGE, FMS, and FMS+SGE protocolsforcarbonic anhydrase (29 × 29 = 841 combinations).
Mentions: As expected, for more challengingcrossdocking experiments, matrixsuccess (Figure 12b) using any of the scoringfunctions are in general significantly lower than their diagonal counterparts(Figure 12a). As a baseline, use of SGE yieldsan averaged matrix success of 36.0% compared to the diagonal at 54.6%.In contrast to the diagonal results, interestingly, use of FMS alonefor crossdocking shows improvement over SGE in only two cases (CAand CPA). However, in all cases, the combined FMS+SGE function alwaysyields a better matrix success than does SGE. Analogous to the diagonalresults, the matrix outcomes (Figure 12b) similarlyreveal that carbonic anhydrase has the lowest overall matrix SGE successrate (17.8%) which increased the most among all systems tested whenusing FMS (48.8%) or FMS+SGE (52.1%). Figure 13 compares the heatmaps for carbonic anhydrase, derived from threeindependent docking sets of size 29 × 29 = 841 combinations,using SGE, FMS, and FMS+SGE methods. The maps visually highlight thatSGE failures are primarily due to scoring (green squares), pinpointwhich specific systems are involved, and indicate that FMS and FMS+SGEprotocols significantly improve docking outcomes (more blue squares).

Bottom Line: When used alone, FMS scoring dramatically improves pose reproduction success to 93.5% (∼20% increase) and reduces sampling failures to 3.7% (∼6% drop) compared to the standard energy score (SGE) across 1043 protein-ligand complexes.The combined FMS+SGE function further improves success to 98.3%.For enrichment, incorporating pharmacophores during sampling and scoring, in most cases, also yield improved outcomes when docking and rank-ordering libraries of known actives and decoys to 15 systems.

View Article: PubMed Central - PubMed

Affiliation: Department of Applied Mathematics & Statistics, ‡Institute of Chemical Biology & Drug Discovery, §Laufer Center for Physical & Quantitative Biology, Stony Brook University , Stony Brook, New York 11794-3600, United States.

ABSTRACT
Pharmacophore modeling incorporates geometric and chemical features of known inhibitors and/or targeted binding sites to rationally identify and design new drug leads. In this study, we have encoded a three-dimensional pharmacophore matching similarity (FMS) scoring function into the structure-based design program DOCK. Validation and characterization of the method are presented through pose reproduction, crossdocking, and enrichment studies. When used alone, FMS scoring dramatically improves pose reproduction success to 93.5% (∼20% increase) and reduces sampling failures to 3.7% (∼6% drop) compared to the standard energy score (SGE) across 1043 protein-ligand complexes. The combined FMS+SGE function further improves success to 98.3%. Crossdocking experiments using FMS and FMS+SGE scoring, for six diverse protein families, similarly showed improvements in success, provided proper pharmacophore references are employed. For enrichment, incorporating pharmacophores during sampling and scoring, in most cases, also yield improved outcomes when docking and rank-ordering libraries of known actives and decoys to 15 systems. Retrospective analyses of virtual screenings to three clinical drug targets (EGFR, IGF-1R, and HIVgp41) using X-ray structures of known inhibitors as pharmacophore references are also reported, including a customized FMS scoring protocol to bias on selected regions in the reference. Overall, the results and fundamental insights gained from this study should benefit the docking community in general, particularly researchers using the new FMS method to guide computational drug discovery with DOCK.

Show MeSH