Computational evluation of protein energy functions

Abstract

Proteins are organic compounds made up of chains of amino acids that fold into complex 3-dimensional structures based on their chemical and physical properties. A protein is characterized by its 3D structure, which defines its biological function. Proteins fold into 3D structures in a way that leads to low-energy state. Predicting these structures is guided by the requirement of minimizing the energy value associated with the protein structure. However, the energy functions proposed so far by biophysicists and biochemists are still in the exploration phase and their usefulness has been demonstrated only individually. Also, assigning equal weights to different terms in energy has not been well-supported. In this project, we carry out a computational evaluation of putative protein energy functions. Our findings show that the CHARMM energy model tends to be more appropriate for ab initio computational techniques that predict protein structures. Also, we propose an approach based on a simulated annealing algorithm to find a better combination of energy terms, by assigning different weights to the terms, for the purpose of improving the capability of the computational prediction methods.