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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...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
John Wiley & Sons, Inc.
2019
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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. |
format | Online Article Text |
id | pubmed-6933858 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2019 |
publisher | John Wiley & Sons, Inc. |
record_format | MEDLINE/PubMed |
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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