Stochastic reconstruction of protein structures from effective connectivity profiles

TL;DR

This paper presents a stochastic Monte Carlo method for reconstructing protein native structures using effective connectivity profiles derived from contact maps, offering a new approach that integrates structural information without relying on native contact assumptions.

Contribution

The study introduces a novel stochastic reconstruction technique utilizing effective connectivity profiles, which are easier to predict from sequences and do not depend on native contact maps.

Findings

Successful reconstruction of protein structures using the method

Effective connectivity profiles can be predicted from amino acid sequences

The approach does not rely on native contact assumptions

Abstract

We discuss a stochastic approach for reconstructing the native structures of proteins from the knowledge of the "effective connectivity", which is a one-dimensional structural profile constructed as a linear combination of the eigenvectors of the contact map of the target structure. The structural profile is used to bias a search of the conformational space towards the target structure in a Monte Carlo scheme operating on a C_alpha-chain of uniform, finite thickness. Structure information thus enters the folding dynamics via the effective connectivity, but the interaction is not restricted to pairs of amino acids that form native contacts, resulting in a free energy landscape which does not rely on the assumption of minimal frustration. Moreover, effective connectivity vectors can be predicted more readily from the amino acid sequence of proteins than the corresponding contact maps, thus suggesting that the stochastic protocol presented here could be effectively combined with other current methods for predicting native structures.