Comprehensive detection and characterization of human druggable pockets through binding site descriptors

Druggable pockets are protein regions that have the ability to bind organic small molecules, and their characterization is essential in target-based drug discovery. However, deriving pocket descriptors is challenging and existing strategies are often limited in applicability. We introduce PocketVec, an approach to generate pocket descriptors via inverse virtual screening of lead-like molecules. PocketVec performs comparably to leading methodologies while addressing key limitations. Additionally, we systematically search for druggable pockets in the human proteome, using experimentally determined structures and AlphaFold2 models, identifying over 32,000 binding sites across 20,000 protein domains. We then generate PocketVec descriptors for each site and conduct an extensive similarity search, exploring over 1.2 billion pairwise comparisons. Our results reveal druggable pocket similarities not detected by structure- or sequence-based methods, uncovering clusters of similar pockets in proteins lacking crystallized inhibitors and opening the door to strategies for prioritizing chemical probe development to explore the druggable space.

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Publication Details

Journal
Nature Communications
Published
2024-09-10
DOI
https://doi.org/10.1038/s41467-024-52146-3
Citations
13
Primary Topic
Computational Drug Discovery Methods
Type
article
Field-Weighted Citation Impact
4.70

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article

Comprehensive detection and characterization of human druggable pockets through binding site descriptors

Patrick Aloy, Arnau Comajuncosa-Creus, Xavier Barril, Guillem Jorba
13 citations
Nature Communications
Computational Drug Discovery Methods
4.70
article

Comprehensive detection and characterization of human druggable pockets through binding site descriptors

Patrick Aloy, Arnau Comajuncosa-Creus, Xavier Barril, Guillem Jorba
article en
13 citations

Abstract

Druggable pockets are protein regions that have the ability to bind organic small molecules, and their characterization is essential in target-based drug discovery. However, deriving pocket descriptors is challenging and existing strategies are often limited in applicability. We introduce PocketVec, an approach to generate pocket descriptors via inverse virtual screening of lead-like molecules. PocketVec performs comparably to leading methodologies while addressing key limitations. Additionally, we systematically search for druggable pockets in the human proteome, using experimentally determined structures and AlphaFold2 models, identifying over 32,000 binding sites across 20,000 protein domains. We then generate PocketVec descriptors for each site and conduct an extensive similarity search, exploring over 1.2 billion pairwise comparisons. Our results reveal druggable pocket similarities not detected by structure- or sequence-based methods, uncovering clusters of similar pockets in proteins lacking crystallized inhibitors and opening the door to strategies for prioritizing chemical probe development to explore the druggable space.

Nature CommunicationsVol. 15(1)
Institució Catalana de Recerca i Estudis Avançats (ES), Institute for Research in Biomedicine (ES), Universitat de Barcelona (ES)
Ministerio de Ciencia, Innovación y Universidades, Centres de Recerca de Catalunya, European Commission, Generalitat de Catalunya, Ministerio de Ciencia e Innovación, Instituto de Salud Carlos III
Openalex Percentile: Top 4%
Computational Drug Discovery Methods
4.70
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Comprehensive detection and characterization of human druggable pockets through binding site descriptors — Patrick Aloy, Arnau Comajuncosa-Creus, et al. · Nature Communications (2024) | TGRS Research Map | TGRS