scholarly journals Chaotic Multiquenching Annealing Applied to the Protein Folding Problem

2014 ◽  
Vol 2014 ◽  
pp. 1-11 ◽  
Author(s):  
Juan Frausto-Solis ◽  
Ernesto Liñan-García ◽  
Mishael Sánchez-Pérez ◽  
Juan Paulo Sánchez-Hernández
Keyword(s):  
Protein Folding ◽  
Simulated Annealing ◽  
Least Squares ◽  
Simulated Annealing Algorithm ◽  
Least Squares Method ◽  
Equilibrium Phase ◽  
Dynamical Equilibrium ◽  
Annealing Algorithm ◽  
Cooling Function ◽  
Very High

The Chaotic Multiquenching Annealing algorithm (CMQA) is proposed. CMQA is a new algorithm, which is applied to protein folding problem (PFP). This algorithm is divided into three phases: (i) multiquenching phase (MQP), (ii) annealing phase (AP), and (iii) dynamical equilibrium phase (DEP). MQP enforces several stages of quick quenching processes that include chaotic functions. The chaotic functions can increase the exploration potential of solutions space of PFP. AP phase implements a simulated annealing algorithm (SA) with an exponential cooling function. MQP and AP are delimited by different ranges of temperatures; MQP is applied for a range of temperatures which goes from extremely high values to very high values; AP searches for solutions in a range of temperatures from high values to extremely low values. DEP phase finds the equilibrium in a dynamic way by applying least squares method. CMQA is tested with several instances of PFP.

Protein Folding Simulation Based on Improved Simulated Annealing Algorithm

2012 ◽  
Vol 424-425 ◽  
pp. 246-249
Author(s):  
Jing Fa Liu ◽  
Zi Ling Zhou ◽  
Ze Xu Gao ◽  
Guo Jian Zhang
Keyword(s):  
Protein Folding ◽  
Simulated Annealing ◽  
Ground State ◽  
Lower Energy ◽  
Simulated Annealing Algorithm ◽  
Two Dimensional ◽  
Folding Simulation ◽  
Simulation Based ◽  
Annealing Algorithm ◽  
Annealing Method

Protein folding problem is one of the most important problems in bioinformatics. By combining simulated annealing method with pull moves which is a local move set and conformation update mechanism, we put forward an improved simulated annealing (ISA) algorithm for the two-dimensional hydrophobic- polar (2D HP) protein folding problem. Numerical results show that the ISA algorithm can find the known lowest-energy ground state more rapidly and efficiently than the genetic algorithm (GA) for the several given HP sequences. For the sequence with length 20, we obtain the lower-energy conformation than GA. The performance of the algorithm show ISA is an effective method for protein folding simulation

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