Predicting protein-ligand affinity with a random matrix framework

Lee Alpha A.; Brenner Michael P.; Colwell Lucy J.

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Predicting protein-ligand affinity with a random matrix framework

机译：Predicting protein-ligand affinity with a random matrix framework

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摘要

Rapid determination of whether a candidate compound will bind to a particular target receptor remains a stumbling block in drug discovery. We use an approach inspired by random matrix theory to decompose the known ligand set of a target in terms of orthogonal "signals" of salient chemical features, and distinguish these from the much larger set of ligand chemical features that are not relevant for binding to that particular target receptor. After removing the noise caused by finite sampling, we show that the similarity of an unknown ligand to the remaining, cleaned chemical features is a robust predictor of ligand-target affinity, performing as well or better than any algorithm in the published literature. We interpret our algorithm as deriving a model for the binding energy between a target receptor and the set of known ligands, where the underlying binding energy model is related to the classic Ising model in statistical physics.

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《Proceedings of the National Academy of Sciences of the United States of America.》 |2016年第48期|13564-13569|共6页
作者
Lee Alpha A.; Brenner Michael P.; Colwell Lucy J.;
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作者单位

Harvard Univ, Sch Engn & Appl Sci, Cambridge, MA 02138 USA;

Univ Cambridge, Dept Chem, Cambridge CB2 1EW, England;

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收录信息美国《科学引文索引》(SCI);美国《生物学医学文摘》(MEDLINE);美国《化学文摘》(CA);
原文格式 PDF
正文语种英语
中图分类自然科学总论;
关键词
drug discovery; random matrix theory; protein-ligand affinity; computational pharmacology; statistical physics;

Predicting protein-ligand affinity with a random matrix framework

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