Efficient generation of protein pockets with PocketGen
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Abstract
Designing protein-binding proteins is critical for drug discovery. However, artificial-intelligence-based design of such proteins is challenging due to the complexity of protein–ligand interactions, the flexibility of ligand molecules and amino acid side chains, and sequence–structure dependencies. We introduce PocketGen, a deep generative model that produces residue sequence and atomic structure of the protein regions in which ligand interactions occur. PocketGen promotes consistency between protein sequence and structure by using a graph transformer for structural encoding and a sequence refinement module based on a protein language model. The graph transformer captures interactions at multiple scales, including atom, residue and ligand levels. For sequence refinement, PocketGen integrates a structural adapter into the protein language model, ensuring that structure-based predictions align with sequence-based predictions. PocketGen can generate high-fidelity protein pockets with enhanced binding affinity and structural validity. It operates ten times faster than physics-based methods and achieves a 97% success rate, defined as the percentage of generated pockets with higher binding affinity than reference pockets. Additionally, it attains an amino acid recovery rate exceeding 63%.
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Flagged first, then by citations
- Authors
- Zhang, Z., Shen, W. X., Liu, Q., & Zitnik, M.
- Affiliations
- University of Science and Technology of China (CN)
- Harvard University (US)
- Corresponding author
- Liu, Qi
- Venue
- Nature Machine Intelligence
- Published
- 2024-11-15
- Volume
- 6
- Issue
- 11
- Pages
- 1382-1395
- ISSN
- 2522-5839 (ISSN-L)
- PMC
- PMC10925136
- OpenAlex
- W4404391482
- Cited by
- 32
- References
- 15
- Citations by year
- 2026: 18 · 2025: 13 · 2024: 1
- FWCI
- 6.78
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Locations
- Nature Machine Intelligence (publishedVersion, cc-by)
- bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion, cc-by-nc-nd)
- PubMed (publishedVersion)
- PubMed Central (submittedVersion, cc-by-nc-nd)
- Funders
- National Science Foundation (2339524, CAREER)
- Pfizer
- AstraZeneca
- Sanofi
- Harvard University
- National Natural Science Foundation of China (62337001)
- University of Science and Technology of China (2021YFF0901003)
- National Institutes of Health (-0001)
- Harvard Data Science Initiative, Harvard University
- National Key Research and Development Program of China (2021YFF0901003)
- Fundamental Research Funds for the Central Universities
- Eunice Kennedy Shriver National Institute of Child Health and Human Development (R01HD108794)
- Topics
- Physical Sciences › Computer Science › Computational Theory and Mathematics › Computational Drug Discovery Methods
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Protein Structure and Dynamics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Machine Learning in Bioinformatics
- Keywords
- deep generative models, graph transformer, protein language models, protein-ligand interactions, binding affinity, protein structure generation
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