3DIGARS-PSP: A Novel Statistical Energy Function and Effective Conformational Search Strategy based ab initio Protein Structure Prediction

Abstract

To solve protein structure prediction (PSP) problems computationally, a plethora of template-based methods exist. However, there are very few ab initio models for PSP. Template-based modeling relies on the existing structures and therefore is not effective for non-homologous sequence-based structure prediction. Thus, ab initio modeling is indispensable in such cases, even though it is a challenging optimization problem. To cope, we utilize an effective energy function (called 3DIGARS) and an advanced search algorithm (called KGA) based ab initio PSP, called 3DIGARS-PSP. To address critical search, the proposed genetic algorithm deploys two effective operators: angle rotation and segment translation. Further, propensities of torsion angle and secondary structure distribution have been utilized to guide the conformation search. Crucial features, such as sequence-specific accessibility, hydrophobic-hydrophilic properties and torsion angles of protein residues are mined to formulate an optimized energy function, which is then combined with the advanced sampling algorithm to explore critical conformational space. Consequently, 3DIGARS-PSP performed well compared to the state-of-the-art method for a set of low TMscore models from CASP data.