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. It shows great potential to provide structural information to enable and enhance existing and new drug discovery projects. Using an industry-leading molecular docking method (Glide), we benchmarked the virtual screening performance of 28 common drug targets each with an AF2 structure and known holo and apo structures from the DUD-E dataset. The AF2 structures show comparable early enrichment of known active compounds (avg. EF 1%: 13.16) to apo structures (avg. EF 1%: 11.56), while falling behind early enrichment of the holo structures (avg. EF 1%: 24.81). We also demonstrated that with the IFD-MD induced-fit docking approach, we can refine the AF2 structures using a known binding ligand to improve the performance in structure-based virtual screening (avg. EF 1%: 19.25). Thus, with proper preparation and refinement, AF2 structures show considerable promise for in silico hit identification.
Free energy perturbation (FEP) remains an indispensable method for computationally assaying prospective compounds in advance of synthesis. But before FEP can be deployed prospectively, it must demonstrate retrospective recapitulation of known experimental data where the subtle details of the atomic ligand-receptor model are consequential. An open question is whether AlphaFold models can serve as useful initial models for FEP in the regime where there exists a congeneric series of known chemical matter but where no experimental structures are available either of the target or of close homologues. As AlphaFold structures are provided without a ligand bound, we employ induced-fit docking to refine the AlphaFold models in the presence of one or more congeneric ligands. In this work, we first validate the performance of our latest induced-fit docking technology, IFD-MD on a retrospective set of public experimental GPCR structures with 95% of crossdocks producing a pose with a ligand RMSD ≤ 2.5 Å in the top 2 predictions. We then apply IFD-MD and FEP on AlphaFold models of the somatostatin receptor family of GPCRs. We use AlphaFold models produced prior to the availability of any experi-mental structure from within this family. We arrive at FEP-validated models for SSTR2, SSTR4, and SSTR5, with RMSE around 1 kcal/mol and explore the challenges of model validation under scenarios of limited ligand-data, ample ligand data, and categorical data.
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. It shows great potential to provide structural information to enable and enhance existing and new drug discovery projects. Using an industry-leading molecular docking method (Glide), we benchmarked the virtual screening performance of 28 common drug targets each with an AF2 structure and known holo and apo structures from the DUD-E dataset. The AF2 structures show comparable early enrichment of known active compounds (avg. EF 1%: 13.16) to apo structures (avg. EF 1%: 11.56), while falling behind early enrichment of the holo structures (avg. EF 1%: 24.81). We also demonstrated that with the IFD-MD induced-fit docking approach, we can refine the AF2 structures using a known binding ligand to improve the performance in structure-based virtual screening (avg. EF 1%: 19.25). Thus, with proper preparation and refinement, AF2 structures show considerable promise for in silico hit identification.
Free energy perturbation (FEP) remains an indispensable method for computationally assaying prospective compounds in advance of synthesis. But before FEP can be deployed prospectively, it must demonstrate retrospective recapitulation of known experimental data where the subtle details of the atomic ligand-receptor model are consequential. An open question is whether AlphaFold models can serve as useful initial models for FEP in the regime where there exists a congeneric series of known chemical matter but where no experimental structures are available either of the target or of close homologues. As AlphaFold structures are provided without a ligand bound, we employ induced-fit docking to refine the AlphaFold models in the presence of one or more congeneric ligands. In this work, we first validate the performance of our latest induced-fit docking technology, IFD-MD on a retrospective set of public experimental GPCR structures with 95% of crossdocks produc-ing a pose with a ligand RMSD ≤ 2.5 Å in the top 2 predictions. We then apply IFD-MD and FEP on AlphaFold models of the somatostatin receptor family of GPCRs. We use AlphaFold models produced prior to the availability of any experi-mental structure from within this family. We arrive at FEP-validated models for SSTR2, SSTR4, and SSTR5, with RMSE around 1 kcal/mol and explore the challenges of model validation under scenarios of limited ligand-data, ample ligand data, and categorical data.
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