期刊名称：Proceedings of the National Academy of Sciences
印刷版ISSN：0027-8424
电子版ISSN：1091-6490
出版年度：2016
卷号：113
期号：48
页码：13564-13569
DOI：10.1073/pnas.1611138113
语种：English
出版社：The National Academy of Sciences of the United States of America
摘要：SignificanceDeveloping computational methods to screen ligands against protein targets is a major challenge for drug discovery. We present a robust mathematical framework, inspired by random matrix theory, which predicts ligand binding to a target given the known ligand set of that target. Our method considers binding prediction as a denoising problem, recognizing that only some of the chemically important features associated with each ligand contribute to binding to a particular receptor. We use correlations among chemical features in the known ligand set, combined with random matrix theory, to eliminate statistically insignificant correlations. Our method outperforms existing algorithms in the literature. We show that our algorithm has the physical interpretation of estimating the ligand-target binding energy. 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.
关键词：drug discovery ; random matrix theory ; protein–ligand affinity ; computational pharmacology ; statistical physics
