Predicting protein folding dynamics using sequence information

Abstract

Natural protein sequences somehow encode the structural forms that these molecules adopt. Recentdevelopments in structure prediction are agnostic to the mechanisms by which proteins fold and representthem as static objects. However, the amino acid sequences also encode information about how the foldingprocess can happen and how variations in the sequences impact on the populations of the distinct structuralforms that proteins acquire. Here, we present a method to infer protein folding dynamics based only onsequence information. For this, we will rely first on the obtention of a precise “evolutionary field” from theobserved variations in the sequences of homologous proteins. We then show how to map the energetics to acoarse-grained folding model where the protein is treated as a string of foldons that interact. We thendescribe how, for any given protein sequence of a family, the equilibrium folding curve can be computedand how the emergence of protein-folding subdomains can be identified. We finally present protocols toanalyze how mutations perturb both the folding stability and the cooperativity, which represent predictionsfor a deep mutational scan of a protein of interest.