Knowledge-based fragment binding prediction.

Target-based drug discovery must assess many drug-like compounds for potential activity. Focusing on low-molecular-weight compounds (fragments) can dramatically reduce the chemical search space. However, approaches for determining protein-fragment interactions have limitations. Experimental assays a...

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Autores principales:	Grace W Tang, Russ B Altman
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2014
Materias:	Biology (General) QH301-705.5
Acceso en línea:	https://doaj.org/article/f5f13f86fd164876ab8053b1c99e4bff
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spelling	oai:doaj.org-article:f5f13f86fd164876ab8053b1c99e4bff2021-11-18T05:52:56ZKnowledge-based fragment binding prediction.1553-734X1553-735810.1371/journal.pcbi.1003589https://doaj.org/article/f5f13f86fd164876ab8053b1c99e4bff2014-04-01T00:00:00Zhttps://www.ncbi.nlm.nih.gov/pmc/articles/pmid/24762971/?tool=EBIhttps://doaj.org/toc/1553-734Xhttps://doaj.org/toc/1553-7358Target-based drug discovery must assess many drug-like compounds for potential activity. Focusing on low-molecular-weight compounds (fragments) can dramatically reduce the chemical search space. However, approaches for determining protein-fragment interactions have limitations. Experimental assays are time-consuming, expensive, and not always applicable. At the same time, computational approaches using physics-based methods have limited accuracy. With increasing high-resolution structural data for protein-ligand complexes, there is now an opportunity for data-driven approaches to fragment binding prediction. We present FragFEATURE, a machine learning approach to predict small molecule fragments preferred by a target protein structure. We first create a knowledge base of protein structural environments annotated with the small molecule substructures they bind. These substructures have low-molecular weight and serve as a proxy for fragments. FragFEATURE then compares the structural environments within a target protein to those in the knowledge base to retrieve statistically preferred fragments. It merges information across diverse ligands with shared substructures to generate predictions. Our results demonstrate FragFEATURE's ability to rediscover fragments corresponding to the ligand bound with 74% precision and 82% recall on average. For many protein targets, it identifies high scoring fragments that are substructures of known inhibitors. FragFEATURE thus predicts fragments that can serve as inputs to fragment-based drug design or serve as refinement criteria for creating target-specific compound libraries for experimental or computational screening.Grace W TangRuss B AltmanPublic Library of Science (PLoS)articleBiology (General)QH301-705.5ENPLoS Computational Biology, Vol 10, Iss 4, p e1003589 (2014)
institution	DOAJ
collection	DOAJ
language	EN
topic	Biology (General) QH301-705.5
spellingShingle	Biology (General) QH301-705.5 Grace W Tang Russ B Altman Knowledge-based fragment binding prediction.
description	Target-based drug discovery must assess many drug-like compounds for potential activity. Focusing on low-molecular-weight compounds (fragments) can dramatically reduce the chemical search space. However, approaches for determining protein-fragment interactions have limitations. Experimental assays are time-consuming, expensive, and not always applicable. At the same time, computational approaches using physics-based methods have limited accuracy. With increasing high-resolution structural data for protein-ligand complexes, there is now an opportunity for data-driven approaches to fragment binding prediction. We present FragFEATURE, a machine learning approach to predict small molecule fragments preferred by a target protein structure. We first create a knowledge base of protein structural environments annotated with the small molecule substructures they bind. These substructures have low-molecular weight and serve as a proxy for fragments. FragFEATURE then compares the structural environments within a target protein to those in the knowledge base to retrieve statistically preferred fragments. It merges information across diverse ligands with shared substructures to generate predictions. Our results demonstrate FragFEATURE's ability to rediscover fragments corresponding to the ligand bound with 74% precision and 82% recall on average. For many protein targets, it identifies high scoring fragments that are substructures of known inhibitors. FragFEATURE thus predicts fragments that can serve as inputs to fragment-based drug design or serve as refinement criteria for creating target-specific compound libraries for experimental or computational screening.
format	article
author	Grace W Tang Russ B Altman
author_facet	Grace W Tang Russ B Altman
author_sort	Grace W Tang
title	Knowledge-based fragment binding prediction.
title_short	Knowledge-based fragment binding prediction.
title_full	Knowledge-based fragment binding prediction.
title_fullStr	Knowledge-based fragment binding prediction.
title_full_unstemmed	Knowledge-based fragment binding prediction.
title_sort	knowledge-based fragment binding prediction.
publisher	Public Library of Science (PLoS)
publishDate	2014
url	https://doaj.org/article/f5f13f86fd164876ab8053b1c99e4bff
work_keys_str_mv	AT gracewtang knowledgebasedfragmentbindingprediction AT russbaltman knowledgebasedfragmentbindingprediction
_version_	1718424703546163200

Knowledge-based fragment binding prediction.

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