Lee, Sumin Kim, Seeun Lee, Gyu Rie Kwon, Sohee Woo, Hyeonuk Seok, Chaok Park, Hahnbeom 2024-01-19T10:30:59Z 2024-01-19T10:30:59Z 2023-02-03 2023-01 https://pubs.kist.re.kr/handle/201004/114145 While deep learning (DL) has brought a revolution in the protein structure prediction field, still an impor-tant question remains how the revolution can be transferred to advances in structure-based drug discov-ery. Because the lessons from the recent GPCRDock challenge were inconclusive primarily due to the size of the dataset, in this work we further elaborated on 70 diverse GPCR complexes bound to either small molecules or peptides to investigate the best-practice modeling and docking strategies for GPCR drug dis-covery. From our quantitative analysis, it is shown that substantial improvements in docking and virtual screening have been possible by the advance in DL-based protein structure predictions with respect to the expected results from the combination of best pre-DL tools. The success rate of docking on DL -based model structures approaches that of cross-docking on experimental structures, showing over 30% improvement from the best pre-DL protocols. This amount of performance could be achieved only when two modeling points were considered properly: 1) correct functional-state modeling of receptors and 2) receptor-flexible docking. Best-practice modeling strategies and the model confidence estimation metric suggested in this work may serve as a guideline for future computer-aided GPCR drug discovery scenarios.(c) 2022 The Authors. Published by Elsevier B.V. on behalf of Research Network of Computational and Structural Biotechnology. English Research Network of Computational and Structural Biotechnology Evaluating GPCR modeling and docking strategies in the era of deep learning-based protein structure prediction Article 10.1016/j.csbj.2022.11.057 1 Computational and Structural Biotechnology Journal, v.21, pp.158 - 167 Computational and Structural Biotechnology Journal 21 158 167 Y scie scopus 000904436800016 2-s2.0-85145657443 Biochemistry & Molecular Biology Biotechnology & Applied Microbiology Biochemistry & Molecular Biology Biotechnology & Applied Microbiology Article SCORING FUNCTION SIMILARITY DISCOVERY LIGANDS GPCR Deep learning Ligand docking Protein structure prediction Drug discovery