Fast and flexible coarse-grained prediction of protein folding routes using ensemble modeling and evolutionary sequence variation

Abstract

Motivation: Protein folding is a dynamic process through which polypeptide chains reach their native 3D structures. Although the importance of this mechanism is widely acknowledged, very few high-throughput computational methods have been developed to study it.

Results: In this paper, we report a computational platform named P3Fold that combines statistical and evolutionary information for predicting and analyzing protein folding routes. P3Fold uses coarse-grained modeling and efficient combinatorial schemes to predict residue contacts and evaluate the folding routes of a protein sequence within minutes or hours. To facilitate access to this technology, we devise graphical representations and implement an interactive web interface that allows end-users to leverage P3Fold predictions. Finally, we use P3Fold to conduct large and short scale experiments on the human proteome that reveal the broad conservation and variations of structural intermediates within protein families.

Availability and implementation: A Web server of P3Fold is freely available at http://csb.cs.mcgill.ca/P3Fold.

Supplementary information: Supplementary data are available at Bioinformatics online.