BCL::Fold--de novo prediction of complex and large protein topologies by assembly of secondary structure elements.

Mert Karakaş,Brian E Weiner,Jens Meiler,Nils Woetzel,Rene Staritzbichler,Nathan Alexander,Charlotte M Deane

doi:10.1371/journal.pone.0049240

Abstract

Computational de novo protein structure prediction is limited to small proteins of simple topology. The present work explores an approach to extend beyond the current limitations through assembling protein topologies from idealized α-helices and β-strands. The algorithm performs a Monte Carlo Metropolis simulated annealing folding simulation. It optimizes a knowledge-based potential that analyzes radius of gyration, β-strand pairing, secondary structure element (SSE) packing, amino acid pair distance, amino acid environment, contact order, secondary structure prediction agreement and loop closure. Discontinuation of the protein chain favors sampling of non-local contacts and thereby creation of complex protein topologies. The folding simulation is accelerated through exclusion of flexible loop regions further reducing the size of the conformational search space. The algorithm is benchmarked on 66 proteins with lengths between 83 and 293 amino acids. For 61 out of these proteins, the best SSE-only models obtained have an RMSD100 below 8.0 Å and recover more than 20% of the native contacts. The algorithm assembles protein topologies with up to 215 residues and a relative contact order of 0.46. The method is tailored to be used in conjunction with low-resolution or sparse experimental data sets which often provide restraints for regions of defined secondary structure.

Highlights

Understanding of protein function and mechanics is facilitated by and often depends on the availability of structural information
The Protein Data Bank (PDB), as of April 2011, holds 66,726 protein structure entries, 87% determined by X-Ray crystallography and 12% determined by Nuclear Magnetic Resonance (NMR) spectroscopy, and the remaining 1% determined by Electron microscopy and hybrid methods [1,2]
In fragment assembly based approaches to de novo protein structure prediction, local contacts are sampled more efficiently than the non-local ones due to inherent restrictions imposed by the connectivity of the amino acid sequence

Summary

Introduction

Understanding of protein function and mechanics is facilitated by and often depends on the availability of structural information. Despite impressive advancements in the combination of experimental protein structure determination techniques [4,5] with comparative modeling [6], entire classes of proteins remain underrepresented in the PDB as they evade crystallization or are unsuitable for NMR studies; e.g. membrane proteins [7] and proteins that only fold as part of a large macromolecular assembly [8,9] Such proteins more frequently adopt topologies not yet represented in the PDB such that the current structural knowledge fails to encapsulate necessary information to represent all protein families and folds expected to be found in nature [10]. In such situations de novo methods for prediction of protein structure from the primary sequence alone can be applied

Objectives

Methods

Results

Conclusion