Benchmarking Refined and Unrefined AlphaFold2 Structures for Hit Discovery

By Yuqi Zhang, Márton Vass, Da Shi, Esam T. Abualrous, Jennifer M. Chambers, N. Chopra, C. Higgs, Koushik Kasavajhala, Hubert Li, P. Nandekar and 4 more. 2023

98 citations recorded by the source corpus.

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.

Subfields: Computational Theory and Mathematics, Molecular Biology

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Citations in the corpus

AlphaFold2 and its applications in the fields of biology and medicine (2023, methodology): “Zhang et al. 87 recently used Glide, 88 a molecular docking program, to benchmark the performance of virtual screening towards 28 common drug targets, each with a known experimental structure and an AF2 structure.”
State-specific protein–ligand complex structure prediction with a multiscale deep generative model (2022, neutral): “On the contrary, 3D coordinates are solely updated for atomic nodes (PredictDrift, Supplementary Algorithm 11), while the rigid-body frames ( t , R ) are passively reconstructed according to the updated atomic coordinates, circumventing numerical issues regarding fitting quaternion or axis-angle var”
Computational approaches streamlining drug discovery (2023, background): “In a benchmark focused on targets with existing crystal structures, most AlphaFold2 models had to be cleaned from loops blocking the binding pocket and/or augmented with known ion or other cofactors to achieve reasonable enrichment of hits 106 .”
Evaluation of AlphaFold2 structures as docking targets (2022, result): “…in broad agreement with the findings of a study on the screening power of AF2-predicted structures in the context of a smaller set of 38 proteins (Zhang et al., 2022), and with a study that suggests AF2 has a tendency to predict holo structures (Saldaño et al., 2022), and in the case of GPCRs…”