A Real Valued Genetic Algorithm for Generating Native Like Structure of Small Globular Protein

Abstract

AbstractPredicting 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 phi and psi . This vector converts all these biophysical measures into a real value by using specific weights or factors. Overall goal of the GA is to maximize the fitness value. The algorithm has been validted on a set of known globular protein containing 2-4 secondary structure elements. For all the test protein the algorithm converges rapidly and the converged structure shows a RMSD (root mean square deviation) of 3-6A as compared to the native structure. Index TermsReal valued genetic algorithm, protein folding, structure prediction, structure evaluation, Bio-physical filters.