ALLO: a tool to discriminate and prioritize allosteric pockets

Abstract

Allosteric proteins make up a substantial proportion of human drug targets. Thus, rational design of small molecule binders that target these proteins requires the identification of putative allosteric pockets and an understanding of their potential activity. Here, we characterized allosteric pockets using a set of physicochemical descriptors and compared them to pockets that are found on the surface of a protein. Further, we trained predictive models capable of discriminating allosteric pockets from orthosteric pockets and models capable of prioritizing allosteric pockets in a set of pockets found on a given protein. Such models might be useful for identifying novel allosteric sites and in turn, potentially new allosteric drug targets. Datasets along with a Python program encapsulating the predictive models are available at github. com/fibonaccirabbits/allo.

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In this work, we characterized allosteric pockets using a set of physicochemical descriptors and compared them to pockets that are found on the surface of a protein. Further, we trained predictive models capable of discriminating allosteric pockets from orthosteric pockets and models capable of prioritizing allosteric pockets in a set of pockets found on a given protein.

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