Improved Protein Residue-Residue Contact Prediction Using Image Denoising Methods

Abstract

A protein contact map is a simplified matrix representation of the protein structure, where the spatial proximity of two amino acid residues is reflected. Although the accurate prediction of protein inter-residue contacts from the amino acid sequence is an open problem, considerable progress has been made in recent years. This progress has been driven by the development of contact predictors that identify the coevolutionary events occurring in a protein multiple sequence alignment (MSA). However, it has been shown that these methods introduce Gaussian noise in the estimated contact map, making its reduction necessary. In this paper, we propose the use of two different Gaussian denoising approximations in order to enhance the protein contact estimation. These approaches are based on (i) sparse representations over learned dictionaries, and (ii) deep residual convolutional neural networks. The results highlight that the residual learning strategy allows a better reconstruction of the contact map, thus improving contact predictions.