
==== Front
bioRxiv
BIORXIV
bioRxiv
2692-8205
Cold Spring Harbor Laboratory

38464121
10.1101/2024.02.25.581968
preprint
4
Article
Efficient Generation of Protein Pockets with PocketGen
Zhang Zaixi
Shen Wanxiang
Liu Qi
Zitnik Marinka http://orcid.org/0000-0001-8530-7228

23 9 2024
2024.02.25.581968https://creativecommons.org/licenses/by-nc-nd/4.0/ This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License, which allows reusers to copy and distribute the material in any medium or format in unadapted form only, for noncommercial purposes only, and only so long as attribution is given to the creator.
http://biorxiv.org/lookup/doi/10.1101/2024.02.25.581968
nihpp-2024.02.25.581968.pdf
Designing small-molecule-binding proteins, such as enzymes and biosensors, is crucial in protein biology and bioengineering. Generating high-fidelity protein pockets areas where proteins interact with ligand molecules, is challenging due to complex interactions between ligand molecules and proteins, flexibility of ligand molecules and amino acid side chains, and complex sequence-structure dependencies. Here, we introduce PocketGen, a deep generative method for generating the residue sequence and the full-atom structure within the protein pocket region that leverages sequence structure consistency. PocketGen consists of a bilevel graph transformer for structural encoding and a sequence refinement module that uses a protein language model (pLM) for sequence prediction. The bilevel graph transformer captures interactions at multiple granularities (atom-level and residue/ligand-level) and aspects (intra-protein and protein-ligand) with bilevel attention mechanisms. For sequence refinement, a structural adapter using cross-attention is integrated into a pLM to ensure structure-sequence consistency. During training, only the adapter is fine-tuned, while the other layers of the pLM remain unchanged. Experiments show that PocketGen can efficiently generate protein pockets with higher binding affinity and validity than state-of-the-art methods. PocketGen is ten times faster than physics-based methods and achieves a 95% success rate (percentage of generated pockets with higher binding affinity than reference pockets) with over 64% amino acid recovery rate.
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pmc
