Refined Contact Map Prediction of Peptides Based on GCN and ResNet - Gu, Jiawei; Zhang, Tianhao; Wu, Chunguo; Liang, Yanchun; Shi, Xiaohu (Frontiers in Genetics, 2022)
Predicting peptide inter-residue contact map plays an important role in computational biology, which determine the topology of the peptide structure. However, due to the limited number of known homologous structures, there is still much room for inter-residue contact map prediction. Current models are not sufficient for capturing high accuracy relationship between residues, especially for those with long-range distance. In this paper, we develop a novel deep neural network framework to refine the rough contact map produced by existing methods. The rough contact map is used to construct the residue graph that is processed by graph convolutional neural network (GCN). GCN can better capture global information, and therefore is used to grasp the long-range contact relationship. Residual convolutional neural network is also applied in the framework for learning local information. We conducted experiments on four different test datasets, and the inter-residue long-range contact map prediction accuracy demonstrates the effectiveness of our proposed method.