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De novo protein ligand design including protein flexibility and conformational adaptation
Open accessPreprint — published as 10.1093/bioinformatics/btag027 · Paper page
Abstract
Motivation: The rational design of chemical compounds that bind to a desired protein target molecule is a major goal of drug discovery. Most current molecular docking but also fragment-based build-up or machine-learning based generative drug design approaches employ a rigid protein target structure. Results: Based on recent progress in predicting protein structures and complexes with chemical compounds we have designed an approach, AI-MCLig, to optimize a chemical compound bound to a fully flexible and conformationally adaptable protein binding region. During a Monte-Carlo (MC) type simulation to randomly change a chemical compound the target protein-compound complex is completely rebuilt at every MC step using the Chai-1 protein structure prediction program. Besides compound flexibility it allows the protein to adapt to the chemically changing compound. MC-protocols based on atom/bond type changes or based on combining larger chemical fragments have been tested. Simulations on three test targets resulted in potential ligands that show very good binding scores comparable to experimentally known binders using several different scoring schemes. The MC-based compound design approach is complementary to existing approaches and could help for the rapid design of putative binders including induced fit of the protein target. Availability and implementation: Datasets, examples and source code are available on our public GitHub repository https://github.com/JakobAgamia/AI-MCLig and on Zenodo at https://doi.org/10.5281/zenodo.17800140 .
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Highly accurate protein structure prediction with AlphaFold
Jumper, J., Evans, R., Pritzel, A., Green, T., Figurnov, M., Ronneberger, O., Tunyasuvunakool, K., Bates, R., Žídek, A., Potapenko, A., et al. (2021) · Nature · doi:10.1038/s41586-021-03819-2 · Cited by 43,764
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Metadata
- Authors
- Agamia, J., & Zacharias, M.
- Affiliations
- Technical University of Munich (DE)
- Corresponding author
- Zacharias, Martin
- ORCID
- Zacharias, Martin
- Venue
- Bioinformatics
- Publisher
- openRxiv
- Published
- 2026-01-08
- Volume
- 42
- Issue
- 2
- ISSN
- 1367-4803 (ISSN-L)
- PMC
- PMC12944823
- OpenAlex
- W7125487729
- Cited by
- 2
- References
- 1
- Citations by year
- 2026: 2
- FWCI
- 7.21
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Bioinformatics (publishedVersion, cc-by)
- bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion, cc-by)
- PubMed (publishedVersion)
- PubMed Central (publishedVersion, cc-by)
- Funders
- Deutsche Forschungsgemeinschaft (DFG Za153/29-1, DFG INST 95/1610-1 FUGG, INST 95/1610-1 FUGG)
- Topics
- Life Sciences › Pharmacology, Toxicology and Pharmaceutics › Drug Discovery › 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 › vaccines and immunoinformatics approaches
- Keywords
- de novo ligand design, protein-ligand binding, protein flexibility, conformational adaptation, induced fit, Monte Carlo simulation, protein structure prediction, drug discovery
- MeSH terms
- Binding Sites, Ligands, Monte Carlo Method, Protein Binding, Protein Conformation, Proteins, Drug Design, Molecular Docking Simulation
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