Inter-Residue Distance Prediction From Duet Deep Learning Models - Zhang, Huiling; Huang, Ying; Bei, Zhendong; Ju, Zhen; Meng, Jintao; Hao, Min; Zhang, Jingjing; Zhang, Haiping; Xi, Wenhui (Frontiers in Genetics, 2022)
Residue distance prediction from the sequence is critical for many bological applications such as protein structure reconstruction, protein-protein interaction prediction and protein design. However, prediction of fine-grained distances between residues with long sequence separations still remains challenging. In this paper, we propose DuetDis, a method based on duet feature sets and deep residual network with squeeze-and-excitation (SE), for protein inter-residue distance prediction. Duet embraces the ability to learn and fuse features directly or indirectly extracted from the whole-genome/metagenomic databases, and therefore minimize the information loss through ensembling models trained on different feature sets. We evaluate DuetDis and 11 widely-used peer methods on a large-scale test set (610 proteins chains). The experimental results suggest that (1) prediction results from different feature sets show obvious differences; (2) ensembling different feature sets can improve the prediction performance; (3) high-quality multi-sequence alignment (MSA) used for both training and testing can greatly improve the prediction performance; (4) DuetDis is more accurate than peer methods for the overall prediction, more reliable in terms of model prediction score, and more robust against shallow MSA.