Leveraging AlphaFold2 structural space exploration for generating drug target structures in structure-based virtual screening
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Abstract
Computational virtual screening (VS) plays a vital role in early-stage drug discovery by enabling the efficient selection of candidate compounds and reducing associated costs. However, the absence of experimentally determined three-dimensional protein structures often limits the applicability of structure-based VS. Advances in protein structure prediction, notably AlphaFold2, have begun to address this gap. Yet, studies indicate that direct use of AlphaFold2-predicted structures often leads to suboptimal VS performance-likely because these structures fail to capture ligand-induced conformational changes (apo-to-holo transitions). To overcome this, we propose an approach that explores and modifies the structural space of AlphaFold2 predictions to generate conformations more amenable to VS. Our method deliberately alters the multiple sequence alignment (MSA) by introducing alanine mutations at key residues in the ligand-binding site, thereby inducing significant conformational shifts. The exploration process is guided by iterative ligand docking simulations, with mutation strategies optimized either by a genetic algorithm or via random search. Our evaluation shows that when sufficient active compounds are available, the genetic algorithm significantly enhances VS accuracy. In contrast, with limited active compound data, a random search strategy proves more effective. Moreover, our approach is particularly promising for targets that yield poor screening results when using experimentally determined structures from the PDB. Overall, these findings underscore the practical utility of modified AlphaFold2-derived structures in VS and expand the potential of computationally predicted protein models in drug discovery.
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Li, L., Soriano, L. D., Kedir, W. M., Hoch, F. L., & Loke, D. K. (2026) · Molecular Biomedicine · doi:10.1186/s43556-026-00498-1
- Authors
- Uchikawa, K., Furui, K., & Ohue, M.
- Affiliations
- Institute of Science Tokyo (JP)
- Corresponding author
- Ohue, Masahito
- Venue
- Biochemistry and Biophysics Reports
- Publisher
- Elsevier BV
- Published
- 2025-09
- Volume
- 43
- Pages
- 102110
- ISSN
- 2405-5808 (ISSN-L)
- PMC
- PMC12275061
- OpenAlex
- W4412437215
- Cited by
- 1
- References
- 45
- Citations by year
- 2026: 1
- FWCI
- 0.52
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Biochemistry and Biophysics Reports (publishedVersion, cc-by)
- bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Funders
- Japan Science and Technology Agency
- Japan Society for the Promotion of Science (JP23H04880, JP23K28186, JP22K12258, JP23H04887)
- Japan Science and Technology Corporation (JPMJFR216J)
- Japan Agency for Medical Research and Development (JP25ama121026)
- 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 › Receptor Mechanisms and Signaling
- Keywords
- AlphaFold2, structure-based virtual screening, protein structure prediction, drug target structure, alanine mutations, protein-ligand docking, genetic algorithm, random search, early drug discovery
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