Fragment-based computational protein structure prediction

Abstract

Proteins consist of sequences of amino acids that fold into 3-dimensional structures. The 3-dimensional configuration determines a protein’s function. Hence, it is very important to determine the correct structure in order to identify the wrong folding that indicates a disease situation. Computational protein structure prediction methods have been proposed in order to alleviate the enormous time taken by wet-lab methods. This paper presents a fragment-based protein tertiary structure prediction method which employs the CHARMM36 energy model. The method is based on a two-phase Scatter Search algorithm that minimizes the energy function. Backbone fragments are extracted from the Robetta server and side chains are, extracted from the Dunbrack Library. The results show that the algorithm produces tertiary structures with promising root mean square deviations.