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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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Ten out of twenty-six FN poses derived from FMS-guideddockingwith the largest FMS scores. Crystal poses in orange, best scoredposes in magenta. RMSD in angstroms and FMS scores in parentheses.
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fig11: Ten out of twenty-six FN poses derived from FMS-guideddockingwith the largest FMS scores. Crystal poses in orange, best scoredposes in magenta. RMSD in angstroms and FMS scores in parentheses.

Mentions: In terms of theFN examples (RMSD < 2 Å, FMS > 2), Figure 11 presents the ten out of twenty-six poses with the highestFMS scores. Immediately obvious compared to the FP examples is thatthe molecules here contain fewer aromatic rings, for the most partare larger and more extended, and have a higher number of more looselymatched hydrogen-bonding functional groups (most polar atoms in theFP cases are either tightly matched or not matched at all). This latterpoint is particularly important as relatively small changes in positionof a hydrogen-bonding functional group can lead to relatively largechanges in FMS overlap but minor effects on RMSD which is computedusing only heavy (non-hydrogen) atoms. Although our standard preparationprotocol for FMS scoring employs an energy minimization step to relaxany hydrogen atoms added to the system, the positions adopted as aresult of ligand sampling during growth may result in the candidateand reference poses having different hydrogen directions. This resulthighlights the need for care when preparing a molecule to be usedas a “reference” for scoring candidate compounds. Despitebeing a distinctly different type of function, a similar conclusionwas reached by employing the DOCK footprint function.7 Despite this sensitivity, however, most of the FN caseshave scores close to 2 that could easily be rescued by a minor increasein FMS cutoff to 2.5.


Pharmacophore-based similarity scoring for DOCK.

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

Ten out of twenty-six FN poses derived from FMS-guideddockingwith the largest FMS scores. Crystal poses in orange, best scoredposes in magenta. RMSD in angstroms and FMS scores in parentheses.
© Copyright Policy
Related In: Results  -  Collection

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

fig11: Ten out of twenty-six FN poses derived from FMS-guideddockingwith the largest FMS scores. Crystal poses in orange, best scoredposes in magenta. RMSD in angstroms and FMS scores in parentheses.
Mentions: In terms of theFN examples (RMSD < 2 Å, FMS > 2), Figure 11 presents the ten out of twenty-six poses with the highestFMS scores. Immediately obvious compared to the FP examples is thatthe molecules here contain fewer aromatic rings, for the most partare larger and more extended, and have a higher number of more looselymatched hydrogen-bonding functional groups (most polar atoms in theFP cases are either tightly matched or not matched at all). This latterpoint is particularly important as relatively small changes in positionof a hydrogen-bonding functional group can lead to relatively largechanges in FMS overlap but minor effects on RMSD which is computedusing only heavy (non-hydrogen) atoms. Although our standard preparationprotocol for FMS scoring employs an energy minimization step to relaxany hydrogen atoms added to the system, the positions adopted as aresult of ligand sampling during growth may result in the candidateand reference poses having different hydrogen directions. This resulthighlights the need for care when preparing a molecule to be usedas a “reference” for scoring candidate compounds. Despitebeing a distinctly different type of function, a similar conclusionwas reached by employing the DOCK footprint function.7 Despite this sensitivity, however, most of the FN caseshave scores close to 2 that could easily be rescued by a minor increasein FMS cutoff to 2.5.

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