Selecting fast folding proteins by their rate of convergence

TL;DR

This paper introduces a novel, efficient method to predict good protein folders by analyzing their initial dynamical trajectories, applicable across various models without prior native state knowledge.

Contribution

It presents a new approach based on the rate of convergence of amino acid sequences, significantly reducing computational time and identifying promising folding sequences.

Findings

Successfully identified new good folders in lattice and off-lattice models

Determined optimal folding temperature efficiently

Reduced computational effort by 3-4 orders of magnitude

Abstract

We propose a general method for predicting potentially good folders from a given number of amino acid sequences. Our approach is based on the calculation of the rate of convergence of each amino acid chain towards the native structure using only the very initial parts of the dynamical trajectories. It does not require any preliminary knowledge of the native state and can be applied to different kinds of models, including atomistic descriptions. We tested the method within both the lattice and off-lattice model frameworks and obtained several so far unknown good folders. The unbiased algorithm also allows to determine the optimal folding temperature and takes at least 3--4 orders of magnitude less time steps than those needed to compute folding times.