Glocal: Reconstructing Protein 3D Structure from 2D Contact Map by Combining Global and Local Optimization Schemes

Jun      Chen; Hong-Bin      Shen

doi:10.2174/157489312800604381

Abstract

Prediction of protein 3D structure from solely its amino acid sequence is one of the most challenging problems in structural bioinformatics, where the 3D structure reconstruction from observed constraints is the key step. In this paper, we propose a novel protocol called Glocal to recover a protein’s 3D coordinates based on a given 2D contact map by combining both global and local optimization schemes achieved by the swarm intelligence of Particle Swarm Optimization (PSO) and the Simulated Annealing (SA) techniques respectively. Our results demonstrate that Glocal can recover the 3D structures with the average RMSD less than 2 Å from the native contact map. Further analysis also shows that Glocal is powerful for handling with noisy contact map with the proposed combination optimization approaches.

Keywords: Glocal, particle swarm optimization, protein contact map, protein structure reconstruction, simulated annealing, Simulated Annealing, protein, Threshold, resolutions