Deep Neural Network for Protein Contact Prediction by Weighting Sequences in a Multiple Sequence Alignment

Abstract

Protein contact prediction is a crucially important step for protein structure prediction. To predict a contact, approaches of two types are used: evolutionary coupling analysis (ECA) and supervised learning. ECA uses a large multiple sequence alignment (MSA) of homologue sequences and extract correlation information between residues. Supervised learning uses ECA analysis results as input features and can produce higher accuracy. As described herein, we present a new approach to contact prediction which can both extract correlation information and predict contacts in a supervised manner directly from MSA using a deep neural network (DNN). Using DNN, we can obtain higher accuracy than with earlier ECA methods. Simultaneously, we can weight each sequence in MSA to eliminate noise sequences automatically in a supervised way. It is expected that the combination of our method and other meta-learning methods can provide much higher accuracy of contact prediction.

Deep Multi-attribute Graph Representation Learning on Protein Structures

“…[1] first applied CNN to predict protein contacts. [19], [20], [2] and [21] used dilated CNN method for protein contact map prediction.…”