Protein Contact Prediction by Integrating Joint Evolutionary Coupling Analysis and Supervised Learning

TL;DR

This paper introduces a novel protein contact prediction method that combines joint evolutionary coupling analysis across related protein families with supervised learning, significantly improving prediction accuracy over existing techniques.

Contribution

The paper presents a group graphical lasso framework that integrates multi-family EC analysis with supervised learning, leveraging shared co-evolution patterns and diverse information sources.

Findings

Outperforms existing contact prediction methods in accuracy.

Effective on both conserved and family-specific contacts.

Utilizes joint EC analysis across related protein families.

Abstract

Protein contacts contain important information for protein structure and functional study, but contact prediction from sequence remains very challenging. Both evolutionary coupling (EC) analysis and supervised machine learning methods are developed to predict contacts, making use of different types of information, respectively. This paper presents a group graphical lasso (GGL) method for contact prediction that integrates joint multi-family EC analysis and supervised learning. Different from existing single-family EC analysis that uses residue co-evolution information in only the target protein family, our joint EC analysis uses residue co-evolution in both the target family and its related families, which may have divergent sequences but similar folds. To implement joint EC analysis, we model a set of related protein families using Gaussian graphical models (GGM) and then co-estimate their precision matrices by maximum-likelihood, subject to the constraint that the precision matrices shall share similar residue co-evolution patterns. To further improve the accuracy of the estimated precision matrices, we employ a supervised learning method to predict contact probability from a variety of evolutionary and non-evolutionary information and then incorporate the predicted probability as prior into our GGL framework. Experiments show that our method can predict contacts much more accurately than existing methods, and that our method performs better on both conserved and family-specific contacts.