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Ligity: A Non-Superpositional, Knowledge-Based Approach to Virtual Screening
[Image: see text] We present Ligity, a hybrid ligand-structure-based, non-superpositional method for virtual screening of large databases of small molecules. Ligity uses the relative spatial distribution of pharmacophoric interaction points (PIPs) derived from the conformations of small molecules. T...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
American Chemical
Society
2019
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Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7007185/ https://www.ncbi.nlm.nih.gov/pubmed/31117509 http://dx.doi.org/10.1021/acs.jcim.8b00779 |
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author | Ebejer, Jean-Paul Finn, Paul W. Wong, Wing Ki Deane, Charlotte M. Morris, Garrett M. |
author_facet | Ebejer, Jean-Paul Finn, Paul W. Wong, Wing Ki Deane, Charlotte M. Morris, Garrett M. |
author_sort | Ebejer, Jean-Paul |
collection | PubMed |
description | [Image: see text] We present Ligity, a hybrid ligand-structure-based, non-superpositional method for virtual screening of large databases of small molecules. Ligity uses the relative spatial distribution of pharmacophoric interaction points (PIPs) derived from the conformations of small molecules. These are compared with the PIPs derived from key interaction features found in protein–ligand complexes and are used to prioritize likely binders. We investigated the effect of generating PIPs using the single lowest energy conformer versus an ensemble of conformers for each screened ligand, using different bin sizes for the distance between two features, utilizing triangular sets of pharmacophoric features (3-PIPs) versus chiral tetrahedral sets (4-PIPs), fusing data for targets with multiple protein–ligand complex structures, and applying different similarity measures. Ligity was benchmarked using the Directory of Useful Decoys-Enhanced (DUD-E). Optimal results were obtained using the tetrahedral PIPs derived from an ensemble of bound ligand conformers and a bin size of 1.5 Å, which are used as the default settings for Ligity. The high-throughput screening mode of Ligity, using only the lowest-energy conformer of each ligand, was used for benchmarking against the whole of the DUD-E, and a more resource-intensive, “information-rich” mode of Ligity, using a conformational ensemble of each ligand, were used for a representative subset of 10 targets. Against the full DUD-E database, mean area under the receiver operating characteristic curve (AUC) values ranged from 0.44 to 0.99, while for the representative subset they ranged from 0.61 to 0.86. Data fusion further improved Ligity’s performance, with mean AUC values ranging from 0.64 to 0.95. Ligity is very efficient compared to a protein–ligand docking method such as AutoDock Vina: if the time taken for the precalculation of Ligity descriptors is included in the comparason, then Ligity is about 20 times faster than docking. A direct comparison of the virtual screening steps shows Ligity to be over 5000 times faster. Ligity highly ranks the lowest-energy conformers of DUD-E actives, in a statistically significant manner, behavior that is not observed for DUD-E decoys. Thus, our results suggest that active compounds tend to bind in relatively low-energy conformations compared to decoys. This may be because actives—and thus their lowest-energy conformations—have been optimized for conformational complementarity with their cognate binding sites. |
format | Online Article Text |
id | pubmed-7007185 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2019 |
publisher | American Chemical
Society |
record_format | MEDLINE/PubMed |
spelling | pubmed-70071852020-02-10 Ligity: A Non-Superpositional, Knowledge-Based Approach to Virtual Screening Ebejer, Jean-Paul Finn, Paul W. Wong, Wing Ki Deane, Charlotte M. Morris, Garrett M. J Chem Inf Model [Image: see text] We present Ligity, a hybrid ligand-structure-based, non-superpositional method for virtual screening of large databases of small molecules. Ligity uses the relative spatial distribution of pharmacophoric interaction points (PIPs) derived from the conformations of small molecules. These are compared with the PIPs derived from key interaction features found in protein–ligand complexes and are used to prioritize likely binders. We investigated the effect of generating PIPs using the single lowest energy conformer versus an ensemble of conformers for each screened ligand, using different bin sizes for the distance between two features, utilizing triangular sets of pharmacophoric features (3-PIPs) versus chiral tetrahedral sets (4-PIPs), fusing data for targets with multiple protein–ligand complex structures, and applying different similarity measures. Ligity was benchmarked using the Directory of Useful Decoys-Enhanced (DUD-E). Optimal results were obtained using the tetrahedral PIPs derived from an ensemble of bound ligand conformers and a bin size of 1.5 Å, which are used as the default settings for Ligity. The high-throughput screening mode of Ligity, using only the lowest-energy conformer of each ligand, was used for benchmarking against the whole of the DUD-E, and a more resource-intensive, “information-rich” mode of Ligity, using a conformational ensemble of each ligand, were used for a representative subset of 10 targets. Against the full DUD-E database, mean area under the receiver operating characteristic curve (AUC) values ranged from 0.44 to 0.99, while for the representative subset they ranged from 0.61 to 0.86. Data fusion further improved Ligity’s performance, with mean AUC values ranging from 0.64 to 0.95. Ligity is very efficient compared to a protein–ligand docking method such as AutoDock Vina: if the time taken for the precalculation of Ligity descriptors is included in the comparason, then Ligity is about 20 times faster than docking. A direct comparison of the virtual screening steps shows Ligity to be over 5000 times faster. Ligity highly ranks the lowest-energy conformers of DUD-E actives, in a statistically significant manner, behavior that is not observed for DUD-E decoys. Thus, our results suggest that active compounds tend to bind in relatively low-energy conformations compared to decoys. This may be because actives—and thus their lowest-energy conformations—have been optimized for conformational complementarity with their cognate binding sites. American Chemical Society 2019-05-22 2019-06-24 /pmc/articles/PMC7007185/ /pubmed/31117509 http://dx.doi.org/10.1021/acs.jcim.8b00779 Text en Copyright © 2019 American Chemical Society This is an open access article published under a Creative Commons Attribution (CC-BY) License (http://pubs.acs.org/page/policy/authorchoice_ccby_termsofuse.html) , which permits unrestricted use, distribution and reproduction in any medium, provided the author and source are cited. |
spellingShingle | Ebejer, Jean-Paul Finn, Paul W. Wong, Wing Ki Deane, Charlotte M. Morris, Garrett M. Ligity: A Non-Superpositional, Knowledge-Based Approach to Virtual Screening |
title | Ligity: A Non-Superpositional, Knowledge-Based Approach
to Virtual Screening |
title_full | Ligity: A Non-Superpositional, Knowledge-Based Approach
to Virtual Screening |
title_fullStr | Ligity: A Non-Superpositional, Knowledge-Based Approach
to Virtual Screening |
title_full_unstemmed | Ligity: A Non-Superpositional, Knowledge-Based Approach
to Virtual Screening |
title_short | Ligity: A Non-Superpositional, Knowledge-Based Approach
to Virtual Screening |
title_sort | ligity: a non-superpositional, knowledge-based approach
to virtual screening |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7007185/ https://www.ncbi.nlm.nih.gov/pubmed/31117509 http://dx.doi.org/10.1021/acs.jcim.8b00779 |
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