BCL::MP-Fold: membrane protein structure prediction guided by EPR restraints

TL;DR

This study presents BCL::MP-Fold, a novel membrane protein structure prediction method guided by EPR restraints, demonstrating accurate topology modeling for various proteins using limited experimental data.

Contribution

The paper introduces a new membrane protein folding algorithm that integrates EPR data with Monte Carlo sampling to improve topology prediction accuracy.

Findings

Most models have RMSD100 below 8 Å

EPR data improves model discrimination from 1.3 to 2.5

Algorithm successfully predicts native topology for 15 of 29 proteins

Abstract

For many membrane proteins the determination of their topology remains a challenge for methods like X-ray crystallography and nuclear magnetic resonance (NMR) spectroscopy. Electron paramagnetic resonance (EPR) spectroscopy has evolved as an alternative technique to study structure and dynamics of membrane proteins. The present study demonstrates the feasibility of membrane protein topology determination using limited EPR distance and accessibility measurements. The BCL::MP-Fold (BioChemical Library membrane protein fold) algorithm assembles secondary structure elements (SSEs) in the membrane using a Monte Carlo Metropolis (MCM) approach. Sampled models are evaluated using knowledge-based potential functions and agreement with the EPR data and a knowledge-based energy function. Twenty-nine membrane proteins of up to 696 residues are used to test the algorithm. The RMSD100 value of the most accurate model is better than 8 {\AA} for twenty-seven, better than 6 {\AA} for twenty-two and better than 4 {\AA} for fifteen out of twenty-nine proteins, demonstrating the algorithms ability to sample the native topology. The average enrichment could be improved from 1.3 to 2.5, showing the improved discrimination power by using EPR data.