Using Sequence Alignments to Predict Protein Structure and Stability With High Accuracy

TL;DR

This paper introduces a sequence-based probabilistic method that improves protein contact prediction and accurately estimates free energy changes due to mutations, linking evolutionary sequence data with protein stability.

Contribution

It presents a novel probabilistic formalism that captures co-operative effects in protein sequences, enhancing structure and stability predictions over previous methods.

Findings

Significantly improved contact prediction accuracy.

Accurate quantitative predictions of free energy changes.

Strong correlation between sequence statistics and protein stability.

Abstract

We present a sequence-based probabilistic formalism that directly addresses co-operative effects in networks of interacting positions in proteins, providing significantly improved contact prediction, as well as accurate quantitative prediction of free energy changes due to non-additive effects of multiple mutations. In addition to these practical considerations, the agreement of our sequence-based calculations with experimental data for both structure and stability demonstrates a strong relation between the statistical distribution of protein sequences produced by natural evolutionary processes, and the thermodynamic stability of the structures to which these sequences fold.