ArticleFrontiers in bioinformatics2021
Quality Assessment of Protein Docking Models Based on Graph Neural Network.
Article in Frontiers in bioinformatics, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed.
- Using AlphaFold and Symmetrical Docking to Predict Protein-Protein Interactions for Exploring Potential Crystallization Conditions.Proteins · 2025Article
- A Survey of Deep Learning Methods for Estimating the Accuracy of Protein Quaternary Structure Models.Biomolecules · 2024Review
- Assessment of Protein-Protein Docking Models Using Deep Learning.Methods in molecular biology (Clifton, N.J.) · 2024Review
- A gated graph transformer for protein complex structure quality assessment and its performance in CASP15.Bioinformatics (Oxford, England) · 2023Article
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Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Protein docking provides a structural basis for the design of drugs and vaccines. Among the processes of protein docking, quality assessment (QA) is utilized to pick near-native models from numerous protein docking candidate conformations, and it directly determines the final docking results. Although extensive efforts have been made to improve QA accuracy, it is still the bottleneck of current protein docking systems. In this paper, we presented a Deep Graph Attention Neural Network (DGANN) to evaluate and rank protein docking candidate models. DGANN learns inter-residue physio-chemical properties and structural fitness across the two protein monomers in a docking model and generates their probabilities of near-native models. On the ZDOCK decoy benchmark, our DGANN outperformed the ranking provided by ZDOCK in terms of ranking good models into the top selections. Furthermore, we conducted comparative experiments on an independent testing dataset, and the results also demonstrated the superiority and generalization of our proposed method.
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