scholarly journals Artificial intelligence-based multi-objective optimization protocol for protein structure refinement

Author(s):  
Di Wang ◽  
Ling Geng ◽  
Yu-Jun Zhao ◽  
Yang Yang ◽  
Yan Huang ◽  
...  

AbstractMotivationProtein 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.ResultsWe 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.Availability and implementationThe AIR is available online at: www.csbio.sjtu.edu.cn/bioinf/AIR/.Supplementary informationSupplementary data are available at Bioinformatics online.

2021 ◽  
Vol 22 (9) ◽  
pp. 4408
Author(s):  
Cheng-Peng Zhou ◽  
Di Wang ◽  
Xiaoyong Pan ◽  
Hong-Bin Shen

Protein structure refinement is a crucial step for more accurate protein structure predictions. Most existing approaches treat it as an energy minimization problem to intuitively improve the quality of initial models by searching for structures with lower energy. Considering that a single energy function could not reflect the accurate energy landscape of all the proteins, our previous AIR 1.0 pipeline uses multiple energy functions to realize a multi-objectives particle swarm optimization-based model refinement. It is expected to provide a general balanced conformation search protocol guided from different energy evaluations. However, AIR 1.0 solves the multi-objective optimization problem as a whole, which could not result in good solution diversity and convergence on some targets. In this study, we report a decomposition-based method AIR 2.0, which is an updated version of AIR, for protein structure refinement. AIR 2.0 decomposes a multi-objective optimization problem into a number of subproblems and optimizes them simultaneously using particle swarm optimization algorithm. The solutions yielded by AIR 2.0 show better convergence and diversity compared to its previous version, which increases the possibilities of digging out better structure conformations. The experimental results on CASP13 refinement benchmark targets and blind tests in CASP 14 demonstrate the efficacy of AIR 2.0.


1999 ◽  
Vol 285 (4) ◽  
pp. 1691-1710 ◽  
Author(s):  
Daron M. Standley ◽  
Volker A. Eyrich ◽  
Anthony K. Felts ◽  
Richard A. Friesner ◽  
Ann E. McDermott

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