McFee M, Kim PM. GDockScore: a graph-based protein-protein docking scoring function.
Bioinform Adv 2023;
3:vbad072. [PMID:
37359726 PMCID:
PMC10290236 DOI:
10.1093/bioadv/vbad072]
[Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 04/14/2023] [Revised: 05/30/2023] [Accepted: 06/10/2023] [Indexed: 06/28/2023]
Abstract
Summary
Protein complexes play vital roles in a variety of biological processes, such as mediating biochemical reactions, the immune response and cell signalling, with 3D structure specifying function. Computational docking methods provide a means to determine the interface between two complexed polypeptide chains without using time-consuming experimental techniques. The docking process requires the optimal solution to be selected with a scoring function. Here, we propose a novel graph-based deep learning model that utilizes mathematical graph representations of proteins to learn a scoring function (GDockScore). GDockScore was pre-trained on docking outputs generated with the Protein Data Bank biounits and the RosettaDock protocol, and then fine-tuned on HADDOCK decoys generated on the ZDOCK Protein Docking Benchmark. GDockScore performs similarly to the Rosetta scoring function on docking decoys generated using the RosettaDock protocol. Furthermore, state-of-the-art is achieved on the CAPRI score set, a challenging dataset for developing docking scoring functions.
Availability and implementation
The model implementation is available at https://gitlab.com/mcfeemat/gdockscore.
Supplementary information
Supplementary data are available at Bioinformatics Advances online.
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