Energy Landscape Theory for Protein Structure Prediction Using a Lattice Based Coarse-Grained Model

Abstract

The spontaneous folding of protein has posed a fundamental problem popularly known as Levinthal paradox. Effort has been harnessed both in vivo and in vitro to obtain the native (functional) structure of proteins which is the most stable thermodynamically from the experimental and theoretical point of view. A coarse-grained simplified model has been a very useful tool for the study of protein structure by representing proteins as linear and self-avoiding chains which contain two types of monomers H (hydrophobic) and P (polar) on a lattice model. We present the results of numerical studies of the energy landscape on three sequences of amino acid and distinct funnels were generated with contact interactions to obtain the ground state conformation on a square lattice using a move-biased Monte Carlo simulation (MBMC). This method is very efficient and outperforms the conventional Monte Carlo method in the state-of-the-art results.