Protein-protein interactions (PPIs) play crucial roles in diverse cellular functions and biological processes, and structural knowledge of the protein complexes is valuable for the elucidation of those functions and designing new drugs. Due to the limitations of experimental methods, computational modeling approaches capable of producing reliable protein complex models using molecular docking tools are of considerable practical interest. The success of protein docking largely depends on an accurate scoring function that can pick out good protein docking models. In this work, we present a neural network-based scoring function for scoring protein-protein docking models, NNDock2, the updated version of our previous scoring function, NNDock1. To improve NNDock1, we augmented the training decoys by adding a large number of more distant decoys. In addition, instead of interface root mean square deviation (iRMSD) in NNDock1, we used the fraction of native contact (Formula: see text as a target function, which shows better correlation with true model quality. We also applied regularization during training to avoid overfitting. We tested NNDock2 on the protein-protein docking benchmark version 5.0 (BM5), DOCKGROUND dataset, and the CAPRI score set and compared the performance of NNDock2 with other state-of-the-art scoring functions. NNDock2 performed comparably to other state-of-the-art scoring functions, despite the simplicity of the method and low computational costs. We envision that NNDock2 could be used as an independent scoring function or as an element or feature of composite or deep learning-based scoring functions for protein complex model quality estimation.
Chae et al. (Wed,) studied this question.