Evaluating GPCR modeling and docking strategies in the era of deep learning-based protein structure prediction

Sumin Lee; Seeun Kim; Gyu Rie Lee; Sohee Kwon; Hyeonuk Woo; Chaok Seok; Hahnbeom Park

doi:10.1016/j.csbj.2022.11.057

Evaluating GPCR modeling and docking strategies in the era of deep learning-based protein structure prediction

Comput Struct Biotechnol J. 2022 Dec 1:21:158-167. doi: 10.1016/j.csbj.2022.11.057. eCollection 2023.

Authors

Sumin Lee¹, Seeun Kim², Gyu Rie Lee³, Sohee Kwon², Hyeonuk Woo², Chaok Seok², Hahnbeom Park⁴

Affiliations

¹ Interdisciplinary Program in Artificial Intelligence, Seoul National University, Seoul 08826, Republic of Korea.
² Department of Chemistry, Seoul National University, Seoul 08826, Republic of Korea.
³ Department of Biochemistry, University of Washington, WA, USA.
⁴ Brain Science Institute, Korea Institute of Science and Technology, Seoul 02792, Republic of Korea.

Abstract

While deep learning (DL) has brought a revolution in the protein structure prediction field, still an important question remains how the revolution can be transferred to advances in structure-based drug discovery. Because the lessons from the recent GPCR dock 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 discovery. 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.

Keywords: AF, AlphaFold; CAPRI, critical assessment of predicted interactions, DOF, Degree-of-freedom; DL, deep learning; Deep learning; Drug discovery; GALD, Rosetta GA LigandDock; GD3, GalaxyDock3; GDT, global distance test; GPCR; Ligand docking; MD, molecular dynamics; Protein structure prediction; RMSD, root-mean-squared deviation; SBDD, Structure-based drug design; TBM, template-based modeling or template-based model; p-lDDT, predicted local distance difference test.