Predicting protein-ligand affinity with a random matrix framework
Proceedings of the National Academy of Sciences
Proceedings of the National Academy of Sciences of the United States of America
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Lee, A., Brenner, M., & Colwell, L. (2016). Predicting protein-ligand affinity with a random matrix framework. Proceedings of the National Academy of Sciences, 113 (48), 13564-13569. https://doi.org/10.1073/pnas.1611138113
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.
drug discovery, random matrix theory, protein–ligand affinity, computational pharmacology, statistical physics
This research was funded by a grant from Roche Pharmaceuticals. A.A.L. acknowledges the support of a Fulbright Fellowship. L.J.C. was supported by a Next Generation Fellowship, and a Marie Curie Career Integration Grant (Evo-Couplings, Grant 631609). M.P.B. is an investigator of the Simons Foundation, and also acknowledges support from the National Science Foundation through DMS-1411694.
European Commission (631609)
External DOI: https://doi.org/10.1073/pnas.1611138113
This record's URL: https://www.repository.cam.ac.uk/handle/1810/261426
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