Energy landscape theory for cotranslational protein folding

TL;DR

This paper extends energy landscape theory to cotranslational protein folding by introducing a nested landscape model and applying differential geometry and stochastic methods, aligning well with experimental data.

Contribution

It develops a novel theoretical framework for cotranslational folding, incorporating nested energy landscapes and stochastic dynamics, advancing understanding beyond previous models.

Findings

The model defines a natural nested energy landscape for cotranslational folding.

The approach quantitatively explores folding dynamics on the ribosome.

Results agree with experimental observations and deterministic Hamiltonian formalism.

Abstract

Energy landscape theory describes how a full-length protein can attain its native fold after sampling only a tiny fraction of all possible structures. Although protein folding is now understood to be concomitant with synthesis on the ribosome there have been few attempts to modify energy landscape theory by accounting for cotranslational folding. This paper introduces a model for cotranslational folding that leads to a natural definition of a nested energy landscape. By applying concepts drawn from submanifold differential geometry the dynamics of protein folding on the ribosome can be explored in a quantitative manner and conditions on the nested potential energy landscapes for a good cotranslational folder are obtained. A generalisation of diffusion rate theory using van Kampen's technique of composite stochastic processes is then used to account for entropic contributions and the effects of variable translation rates on cotranslational folding. This stochastic approach agrees well with experimental results and Hamiltionian formalism in the deterministic limit.