Benchmarking Refined and Unrefined AlphaFold2 Structures for Hit Discovery

• Published in: Journal of chemical information and modeling Vol. 63; no. 6; pp. 1656 - 1667
• Main Authors: Zhang, Yuqi, Vass, Marton, Shi, Da, Abualrous, Esam, Chambers, Jennifer M., Chopra, Nikita, Higgs, Christopher, Kasavajhala, Koushik, Li, Hubert, Nandekar, Prajwal, Sato, Hideyuki, Miller, Edward B., Repasky, Matthew P., Jerome, Steven V.
• Format: Journal Article
• Language: English
• Published: United States American Chemical Society 27.03.2023
• Subjects: Algorithms; Amino acids; Benchmarking; Binding; Binding Sites; Furylfuramide; Humans; Ligands; Machine Learning and Deep Learning; Molecular docking; Molecular Docking Simulation; Peptide Elongation Factor 1 - metabolism; Performance enhancement; Protein Binding; Proteins; Proteins - chemistry; Screening
• ISSN: 1549-9596; 1549-960X
• DOI: 10.1021/acs.jcim.2c01219

The recently developed AlphaFold2 (AF2) algorithm predicts proteins’ 3D structures from amino acid sequences. The open AlphaFold protein structure database covers the complete human proteome. Using an industry-leading molecular docking method (Glide), we investigated the virtual screening performance of 37 common drug targets, each with an AF2 structure and known holo and apo structures from the DUD-E data set. In a subset of 27 targets where the AF2 structures are suitable for refinement, the AF2 structures show comparable early enrichment of known active compounds (avg. EF 1%: 13.0) to apo structures (avg. EF 1%: 11.4) while falling behind early enrichment of the holo structures (avg. EF 1%: 24.2). With an induced-fit protocol (IFD-MD), we can refine the AF2 structures using an aligned known binding ligand as the template to improve the performance in structure-based virtual screening (avg. EF 1%: 18.9). Glide-generated docking poses of known binding ligands can also be used as templates for IFD-MD, achieving similar improvements (avg. EF 1% 18.0). Thus, with proper preparation and refinement, AF2 structures show considerable promise for in silico hit identification.