The Human Omnibus of Targetable Pockets

Abstract

Hundreds of computational methods for predicting ligand binding pockets exist, but the problem of finding druggable pockets throughout the human proteome persists. Different strategies for pocket-finding excel in different use cases. Ensemble models that leverage multiple different pocket-finding strategies can best capture diverse pockets at scale. Despite this, no publicly available human-proteome-wide datasets of pocket predictions from multiple pocket-finding methods exist. We present the Human Omnibus of Targetable Pockets (HOTPocket), a dataset of over 2.4 million predicted pockets over the entire human proteome that utilizes both experimentally-determined and computationally-predicted protein structures. We assembled this dataset by running seven diverse, established pocket-finding methods over all PDB and AlphaFold2 structures of the canonical human proteome. We created a novel pocket scoring method, hotpocketNN, which we used to filter candidate pockets and assemble the final proteome-wide dataset. Our hotpocketNN method is able to recover known ligand binding pockets, including those which are dissimilar from any pocket seen in its training set. The hotpocketNN method outperforms all constituent methods, including P2Rank and Fpocket, when assessing the DCCcriterion on the Astex Diverse Set and PoseBusters dataset. Additionally, hotpocketNN was able to identify recently-discovered druggable pockets on KRAS and the mu opioid receptor. We make both the HOTPocket dataset and the hotpocketNN method freely available. Scientific ContributionWe introduce HOTPocket, a human proteome-wide dataset of known and predicted binding pockets from a diverse assortment of pocket-finding algorithms. We also introduce hotpocketNN, a machine learning model that scores candidate pockets, which we used to filter and harmonize the HOTPocket dataset. Both the data and the model are made publicly available for scientific use.