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Pharmmaker: Pharmacophore modeling and hit identification based on druggability simulations

Recent years have seen progress in druggability simulations, that is, molecular dynamics simulations of target proteins in solutions containing drug‐like probe molecules to characterize their drug‐binding abilities, if any. An important consecutive step is to analyze the trajectories to construct ph...

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Autores principales: Lee, Ji Young, Krieger, James M., Li, Hongchun, Bahar, Ivet
Formato: Online Artículo Texto
Lenguaje:English
Publicado: John Wiley & Sons, Inc. 2019
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6933858/
https://www.ncbi.nlm.nih.gov/pubmed/31576621
http://dx.doi.org/10.1002/pro.3732
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author Lee, Ji Young
Krieger, James M.
Li, Hongchun
Bahar, Ivet
author_facet Lee, Ji Young
Krieger, James M.
Li, Hongchun
Bahar, Ivet
author_sort Lee, Ji Young
collection PubMed
description Recent years have seen progress in druggability simulations, that is, molecular dynamics simulations of target proteins in solutions containing drug‐like probe molecules to characterize their drug‐binding abilities, if any. An important consecutive step is to analyze the trajectories to construct pharmacophore models (PMs) to use for virtual screening of libraries of small molecules. While considerable success has been observed in this type of computer‐aided drug discovery, a systematic tool encompassing multiple steps from druggability simulations to pharmacophore modeling, to identifying hits by virtual screening of libraries of compounds, has been lacking. We address this need here by developing a new tool, Pharmmaker, building on the DruGUI module of our ProDy application programming interface. Pharmmaker is composed of a suite of steps: (Step 1) identification of high affinity residues for each probe molecule type; (Step 2) selecting high affinity residues and hot spots in the vicinity of sites identified by DruGUI; (Step 3) ranking of the interactions between high affinity residues and specific probes; (Step 4) obtaining probe binding poses and corresponding protein conformations by collecting top‐ranked snapshots; and (Step 5) using those snapshots for constructing PMs. The PMs are then used as filters for identifying hits in structure‐based virtual screening. Pharmmaker, accessible online at http://prody.csb.pitt.edu/pharmmaker/, can be used in conjunction with other tools available in ProDy.
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spelling pubmed-69338582019-12-30 Pharmmaker: Pharmacophore modeling and hit identification based on druggability simulations Lee, Ji Young Krieger, James M. Li, Hongchun Bahar, Ivet Protein Sci Tools for Protein Science Recent years have seen progress in druggability simulations, that is, molecular dynamics simulations of target proteins in solutions containing drug‐like probe molecules to characterize their drug‐binding abilities, if any. An important consecutive step is to analyze the trajectories to construct pharmacophore models (PMs) to use for virtual screening of libraries of small molecules. While considerable success has been observed in this type of computer‐aided drug discovery, a systematic tool encompassing multiple steps from druggability simulations to pharmacophore modeling, to identifying hits by virtual screening of libraries of compounds, has been lacking. We address this need here by developing a new tool, Pharmmaker, building on the DruGUI module of our ProDy application programming interface. Pharmmaker is composed of a suite of steps: (Step 1) identification of high affinity residues for each probe molecule type; (Step 2) selecting high affinity residues and hot spots in the vicinity of sites identified by DruGUI; (Step 3) ranking of the interactions between high affinity residues and specific probes; (Step 4) obtaining probe binding poses and corresponding protein conformations by collecting top‐ranked snapshots; and (Step 5) using those snapshots for constructing PMs. The PMs are then used as filters for identifying hits in structure‐based virtual screening. Pharmmaker, accessible online at http://prody.csb.pitt.edu/pharmmaker/, can be used in conjunction with other tools available in ProDy. John Wiley & Sons, Inc. 2019-12-04 2020-01 /pmc/articles/PMC6933858/ /pubmed/31576621 http://dx.doi.org/10.1002/pro.3732 Text en © 2019 The Authors. Protein Science published by Wiley Periodicals, Inc. on behalf of The Protein Society. This is an open access article under the terms of the http://creativecommons.org/licenses/by/4.0/ License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.
spellingShingle Tools for Protein Science
Lee, Ji Young
Krieger, James M.
Li, Hongchun
Bahar, Ivet
Pharmmaker: Pharmacophore modeling and hit identification based on druggability simulations
title Pharmmaker: Pharmacophore modeling and hit identification based on druggability simulations
title_full Pharmmaker: Pharmacophore modeling and hit identification based on druggability simulations
title_fullStr Pharmmaker: Pharmacophore modeling and hit identification based on druggability simulations
title_full_unstemmed Pharmmaker: Pharmacophore modeling and hit identification based on druggability simulations
title_short Pharmmaker: Pharmacophore modeling and hit identification based on druggability simulations
title_sort pharmmaker: pharmacophore modeling and hit identification based on druggability simulations
topic Tools for Protein Science
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6933858/
https://www.ncbi.nlm.nih.gov/pubmed/31576621
http://dx.doi.org/10.1002/pro.3732
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