Abstract
Problem statement: Predicting the tertiary structure of proteins from their linear sequence is really a big challenge in biology. This challenge is related to the fact that the traditional computational methods are not powerful enough to search for the correct structure in the huge conformational space. This inadequate capability of the computational methods, however, is a major obstacle in facing this problem. Trying to solve the problem of the protein fold recognition, most of the researchers have examined the use of the protein threading technique. This problem is known as NP-hard; researchers have used various methods such as neural networks, Monte Carlo, support vector machine and genetic algorithms to solve it. Some researchers tried the use of the parallel evolutionary methods for protein fold recognition but it is less well known. Approach: We reviewed various algorithms that have been developed for protein structure prediction by threading and fold recognition. Moreover, we provided a survey of parallel evolutionary methods for protein fold recognition. Results: The findings of this survey showed that evolutionary methods can be used to resolve the protein fold recognition problem. Conclusion: There are two aspects of protein fold recognition problem: First is the computational difficulty and second is that current energy functions are still not accurate enough to calculate the free energy of a given conformation.
Highlights
It is a great challenge for nowadays biologists to predict the three-dimensional structure of a protein from its linear sequence
Protein model representation, mapping to algorithm domain, tool selection modifications and conducted experiments were discussed in this study. They claimed that their progress of using multi-objective implementation of the fmGA (MOfmGA) have been modified to scale its efficiency to 4.7 times a serial run time and computational results support their hypothesis that the MO version provides more acceptable results
A parallel hybrid Genetic Algorithm (GA) for peptide 3-D structure prediction Island parallel genetic algorithm for multiple molecular sequence alignment A parallel hybrid genetic algorithm for solving the sumof-pairs multiple protein sequence alignment Multiobjective implementation of the fast messy GA (fmGA) (MOfmGA) and a farming model for the parallel fmGA A Multi-Objective Feature Analysis and Selection Algorithm (MOFASA) for protein fold recognition A novel approach based on evolution strategy for protein threading A novel evolution strategy for the protein threading problem using evaluation strategy EST
Summary
It is a great challenge for nowadays biologists to predict the three-dimensional structure of a protein from its linear sequence. In 2002, Nguyen et al.[19] proposed a parallel hybrid genetic algorithm for solving the sum-of-pairs multiple protein sequence alignment problem They present a new GA-based method for more efficient multiple protein sequence alignment. The authors describe an iPGA strategy that runs on a distributed network of workstations Their parallel approach was implemented on PARAM 10000; a parallel machine developed at the Center of Development of Advanced Computing, Pune and is shown to consistently perform better than the sequential genetic algorithm. Protein model representation, mapping to algorithm domain, tool selection modifications and conducted experiments were discussed in this study They claimed that their progress of using MOfmGA have been modified to scale its efficiency to 4.7 times a serial run time and computational results support their hypothesis that the MO version provides more acceptable results. STAPL, they were able to parallelize their sequential code to obtain scalable speedups
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