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

To solve protein structure prediction (PSP) problems computationally, a plethora of template-based methods exist. However, there are very few ab initio models exist. 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 advanced search algorithm (called KGA) based ab initio PSP, called 3DIGARS-PSP. The proposed approach deploys a memory assisted genetic algorithm to sample the energy hypersurface of the protein folding process to search the native fold of the protein. 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 methods for a set of low TMscore models from CASP data and E. coli proteins.