A real valued Genetic Algorithm for generating native like structure of small globular protein

Predicting the 3D native conformation of a protein given the amino acid sequence is known as protein structure prediction (PSP) problem and is one of the greatest challenges of computational biology. The current work uses a real valued Genetic Algorithm (GA), a powerful variate of GA to simulate the PSP problem. This algorithm consists of basic evolutionary operators and a fitness vector. The fitness vector is designed by combining a set of knowledge based biophysical filters viz. persistence length, radius of gyration, packing fraction, hydrophobicity ratio and irregularity index of φ and ψ. This vector converts all these biophysical measures into a real value by using specific weights or factors. The algorithm has been validated on six known globular proteins, with their length varying from 17–61 residues and total number of helices and strands in the range of 2–4. For all the test protein the algorithm converges rapidly and the converged structure shows a backbone RMSD (root mean square deviation) of 3–6Å as compared to the native structure.