Leveraging AlphaFold2 structural space exploration for generating drug target structures in structure-based virtual screening
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 str...
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Elsevier
2025-09-01
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| Series: | Biochemistry and Biophysics Reports |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2405580825001979 |
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| author | Keisuke Uchikawa Kairi Furui Masahito Ohue |
| author_facet | Keisuke Uchikawa Kairi Furui Masahito Ohue |
| author_sort | Keisuke Uchikawa |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-86d19b7a8ece4cfe9fc27286fcb8145d |
| institution | Kabale University |
| issn | 2405-5808 |
| language | English |
| publishDate | 2025-09-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Biochemistry and Biophysics Reports |
| spelling | doaj-art-86d19b7a8ece4cfe9fc27286fcb8145d2025-08-20T03:50:21ZengElsevierBiochemistry and Biophysics Reports2405-58082025-09-014310211010.1016/j.bbrep.2025.102110Leveraging AlphaFold2 structural space exploration for generating drug target structures in structure-based virtual screeningKeisuke Uchikawa0Kairi Furui1Masahito Ohue2Department of Computer Science, School of Computing, Institute of Science Tokyo, 4259 G3-56 Nagatsuta-cho, Midori-ku, Yokohama, 226-8501, Kanagawa, JapanDepartment of Computer Science, School of Computing, Institute of Science Tokyo, 4259 G3-56 Nagatsuta-cho, Midori-ku, Yokohama, 226-8501, Kanagawa, JapanCorresponding author.; Department of Computer Science, School of Computing, Institute of Science Tokyo, 4259 G3-56 Nagatsuta-cho, Midori-ku, Yokohama, 226-8501, Kanagawa, JapanComputational 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.http://www.sciencedirect.com/science/article/pii/S2405580825001979AlphaFold2Structure-based virtual screeningStructure-based drug designProtein structuresConformational changes |
| spellingShingle | Keisuke Uchikawa Kairi Furui Masahito Ohue Leveraging AlphaFold2 structural space exploration for generating drug target structures in structure-based virtual screening Biochemistry and Biophysics Reports AlphaFold2 Structure-based virtual screening Structure-based drug design Protein structures Conformational changes |
| title | Leveraging AlphaFold2 structural space exploration for generating drug target structures in structure-based virtual screening |
| title_full | Leveraging AlphaFold2 structural space exploration for generating drug target structures in structure-based virtual screening |
| title_fullStr | Leveraging AlphaFold2 structural space exploration for generating drug target structures in structure-based virtual screening |
| title_full_unstemmed | Leveraging AlphaFold2 structural space exploration for generating drug target structures in structure-based virtual screening |
| title_short | Leveraging AlphaFold2 structural space exploration for generating drug target structures in structure-based virtual screening |
| title_sort | leveraging alphafold2 structural space exploration for generating drug target structures in structure based virtual screening |
| topic | AlphaFold2 Structure-based virtual screening Structure-based drug design Protein structures Conformational changes |
| url | http://www.sciencedirect.com/science/article/pii/S2405580825001979 |
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