Paper Info
Title
Artificial intelligence-based multi-objective optimization protocol for protein structure refinement.
Abstract
Motivation: Protein structure refinement is an important step of protein structure prediction. Existing approaches have generally used a single scoring function combined with Monte Carlo method or Molecular Dynamics algorithm. The one-dimension optimization of a single energy function may take the structure too far away without a constraint. The basic motivation of our study is to reduce the bias problem caused by minimizing only a single energy function due to the very diversity of different protein structures. Results: We report a new Artificial Intelligence-based protein structure Refinement method called AIR. Its fundamental idea is to use multiple energy functions as multi-objectives in an effort to correct the potential inaccuracy from a single function. A multi-objective particle swarm optimization algorithm-based structure refinement is designed, where each structure is considered as a particle in the protocol. With the refinement iterations, the particles move around. The quality of particles in each iteration is evaluated by three energy functions, and the non-dominated particles are put into a set called Pareto set. After enough iteration times, particles from the Pareto set are screened and part of the top solutions are outputted as the final refined structures. The multi-objective energy function optimization strategy designed in the AIR protocol provides a different constraint view of the structure, by extending the one-dimension optimization to a new three-dimension space optimization driven by the multi-objective particle swarm optimization engine. Experimental results on CASP11, CASP12 refinement targets and blind tests in CASP 13 turn to be promising.
Year
DOI
Venue
2020
10.1093/bioinformatics/btz544
BIOINFORMATICS
Field
DocType
Volume
Computer science,Multi-objective optimization,Artificial intelligence
Journal
36
Issue
ISSN
Citations 
2
1367-4803
1
PageRank 
References 
Authors
0.35
0
7
Name
Order
Citations
PageRank
Di Wang110.35
Ling Geng210.68
Yu-Jun Zhao310.35
Yang Yang472.89
Yan Huang522627.65
Yang Zhang658047.16
Hongbin Shen753348.23