Inferring protein folding mechanisms from natural sequence diversity

TL;DR

This paper demonstrates that sequence data alone can be used to infer protein folding mechanisms by mapping evolutionary energy fields to a foldon-based model, revealing how topology influences folding variability.

Contribution

It introduces a sequence-based method to predict protein folding mechanisms and shows how topology affects folding diversity within protein families.

Findings

Protein topology limits folding mechanism variability.

Sequence-based models can predict stability and cooperativity changes.

Most beta and alpha/beta proteins have few folding mechanisms.

Abstract

Protein sequences serve as a natural record of the evolutionary constraints that shape their functional structures. We show that it is possible to use only sequence information to go beyond predicting native structures and global stability to infer the folding mechanisms of globular proteins. The one- and two-body evolutionary energy fields at the amino-acid level are mapped to a coarse-grained description of folding, where proteins are divided into contiguous folding elements, commonly referred to as foldons. For 15 diverse protein families, we calculated the folding mechanisms of hundreds of proteins by simulating an Ising chain of foldons, with their energetics determined by the amino acid sequences. We show that protein topology imposes limits on the variability of folding cooperativity within a family. While most beta and alpha/beta structures exhibit only a few possible mechanisms despite high sequence diversity, alpha topologies allow for diverse folding scenarios among family members. We show that both the stability and cooperativity changes induced by mutations can be computed directly using sequence-based evolutionary models.