Prediction of Structures and Interactions from Genome Information

TL;DR

This paper reviews recent advances in predicting protein 3D structures from genome data, highlighting improved methods that distinguish direct from indirect amino acid correlations to enhance contact prediction accuracy.

Contribution

It provides a comprehensive overview of statistical methods for extracting causative correlations and approaches to describe protein structure and flexibility from predicted contacts.

Findings

Contact prediction accuracy has significantly improved since CASP11.

Disentangling direct from indirect correlations is key to better predictions.

Predicted contacts enable more accurate 3D protein modeling.

Abstract

Predicting three dimensional residue-residue contacts from evolutionary information in protein sequences was attempted already in the early 1990s. However, contact prediction accuracies of methods evaluated in CASP experiments before CASP11 remained quite low, typically with $<20$% true positives. Recently, contact prediction has been significantly improved to the level that an accurate three dimensional model of a large protein can be generated on the basis of predicted contacts. This improvement was attained by disentangling direct from indirect correlations in amino acid covariations or cosubstitutions between sites in protein evolution. Here, we review statistical methods for extracting causative correlations and various approaches to describe protein structure, complex, and flexibility based on predicted contacts.