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.
Highlights
A major way for proteins to perform their functions is through interacting with other proteins and producing protein complexes (Tuncbag et al, 2011)
Due to the cost and labor required in these experimental techniques, it is often more feasible and efficient to model the 3D structure of protein complex in silico (Choi et al, 2009; Abdel et al, 2014)
We formulated the protein docking model quality assessment (QA) process as a binary classification problem, which takes a 3D structure of the candidate docking model as the input and outputs its probability of a near-native model
Summary
A major way for proteins to perform their functions is through interacting with other proteins and producing protein complexes (Tuncbag et al, 2011). Several experimental techniques can be used to obtain a 3D structure of protein complexes, such as X-ray crystallography, NMR and cryo-EM (Topf et al, 2008). Due to the cost and labor required in these experimental techniques, it is often more feasible and efficient to model the 3D structure of protein complex in silico (Choi et al, 2009; Abdel et al, 2014). Since antibody as a particular category of proteins produced by the immune system is capable of binding with high specificity to an antigen, protein docking tools are adopted to generate accurate antigen-antibody complexes for evaluating the diversity of polyclonal responses in vaccine development (Rosalba et al, 2017; Weitzner et al, 2017)
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