Deep graph learning to estimate protein model quality using structural constraints from multiple sequence alignments

Abstract

Our perception of protein's function is highly related to our understanding of the protein's three-dimensional (3D) structure and how the structure is computationally predicted. Evaluating the quality of a predicted 3D structural model is crucial for protein structure prediction. In recent years, many research works have leveraged deep learning architectures for the protein structure prediction alongside combinations of massive protein features to evaluate the predicted model's quality. Most recent works have proven that the inter-residue distance and alignment-based coevolutionary information significantly improve the accuracy of protein structure prediction tasks. This work utilizes the structural constraints derived from multiple sequence alignments, powered by the deep graph convolutional neural network, to estimate the protein model accuracy (EMA). The method models protein structure as a connected graph, in which each node encodes the residue's structural information, and the edge represents the structural relationship between any pair of residues in a structure. We incorporate a new feature embedding block in deep graph learning that utilizes the convolution and self-attention technique to leverage sequence alignment information for high-accurate protein quality estimation. We benchmark our methods to other state-of-the-art quality assessment approaches on the CASP13 and CASP14 datasets. The results indicate the effectiveness of alignment-based features and attention-based graph learning in EMA problems and show an improvement of our method among the previous works.