The genetic algorithm approach to protein structure prediction

Predicting the three-dimensional structure of proteins from their linear sequence is one of the major challenges in modern biology. It is widely recognized that one of the major obstacles in addressing this question is that the "standard" computational approaches are not powerful enough to search for the correct structure in the huge conformational space. Genetic algorithms, a cooperative computational method, have been successful in many difficult computational tasks. Thus, it is not surprising that in recent years several studies were performed to explore the possibility of using genetic algorithms to address the protein structure prediction problem. In this review, a general framework of how genetic algorithms can be used for structure prediction is described. Using this framework, the significant studies that were published in recent years are discussed and compared. Applications of genetic algorithms to the related question of protein alignments are also mentioned. The rationale of why genetic algorithms are suitable for protein structure prediction is presented, and future improvements that are still needed are discussed.