AlphaMut: A Deep Reinforcement Learning Model to Suggest Helix-Disrupting Mutations
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Abstract
Helices are important secondary structural motifs within proteins and are pivotal in numerous physiological processes. While amino acids (AA) such as alanine and leucine are known to promote helix formation, proline and glycine disfavor it. Helical structure formation, however, also depends on its environment, and hence, prior prediction of a mutational effect on a helical structure is difficult. Here, we employ a reinforcement learning algorithm to develop a predictive model for helix-disrupting mutations. We start with a model to disrupt helices independent of their protein environment. Our results show that only a few mutations lead to a drastic disruption of the target helix. We further extend our approach to helices in proteins and validate the results using rigorous free energy calculations. Our strategy identifies amino acids crucial for maintaining structural integrity and predicts key mutations that could alter protein structure. Through our work, we present a new use case for reinforcement learning in protein structure disruption.
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Special Topics: Developments of Theoretical and Computational Chemistry Methods in Asia
Cui, Q., Gao, Y. Q., Kurashige, Y., & Tiwary, P. (2026) · Journal of Chemical Theory and Computation · doi:10.1021/acs.jctc.6c01244
Cheng, Y., Dai, X.-Y., Zhang, S.-W., & Zhang, Y.-Y. (2026) · Structural Chemistry · doi:10.1007/s11224-026-02905-3
- Authors
- Bhargav, P., & Mukherjee, A.
- Affiliations
- Indian Institute of Science Education and Research Pune (IN)
- Corresponding author
- Mukherjee, Arnab
- Venue
- Journal of Chemical Theory and Computation
- Publisher
- American Chemical Society (ACS)
- Published
- 2024-12-20
- Volume
- 21
- Issue
- 1
- Pages
- 463-473
- ISSN
- 1549-9618 (ISSN-L)
- 1549-9626
- OpenAlex
- W4405649028
- Cited by
- 2
- References
- 75
- Citations by year
- 2026: 2
- FWCI
- 0.27
- Type
- journal article
- Language
- en
- Open access
- green
- doi.org
- Licence
- Locations
- Journal of Chemical Theory and Computation (publishedVersion)
- bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion)
- PubMed (publishedVersion)
- Funders
- Department of Biotechnology, Ministry of Science and Technology, India (BT/PR34215/AI/133/22/2019)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Protein Structure and Dynamics
- Physical Sciences › Computer Science › Computational Theory and Mathematics › Computational Drug Discovery Methods
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Machine Learning in Bioinformatics
- Keywords
- protein secondary structure, helix formation, deep reinforcement learning, mutation effect prediction, free energy calculation, protein structure prediction
- MeSH terms
- Protein Conformation, alpha-Helical, Deep Learning, Reinforcement Machine Learning, Algorithms, Models, Molecular, Mutation, Proteins, Thermodynamics, Protein Structure, Secondary
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