Evaluation of protein structures needs a trustworthy potential function. Although several knowledge-based potential functions exist, the impact of different types of amino acids in the scoring functions has not been studied yet. Previously, we have reported the importance of nonlocal interactions in scoring function (based on Delaunay tessellation) in discrimination of native structures. Then, we have questioned the structural impact of hydrophobic amino acids in protein fold recognition. Therefore, a Hydrophobic Reduced Model (HRM) was designed to reduce protein structure of FS (Full Structure) into RS (Reduced Structure). RS is considered as a reduced structure of only seven hydrophobic amino acids (L, V, F, I, A, W, Y) and all their interactions. The presented model was evaluated via four different performance metrics including the number of correctly identified natives, the Z-score of the native energy, the RMSD of the minimum score, and the Pearson correlation coefficient between the energy and the model quality. Results indicated that only nonlocal interactions between hydrophobic amino acids could be sufficient and accurate enough for protein fold recognition. Interestingly, the results of HRM is significantly close to the model that considers all amino acids (20-amino acid model) to discriminate the native structure of the proteins on eleven decoy sets. This indicates that the power of knowledge-based potential functions in protein fold recognition is mostly due to hydrophobic interactions. Hence, we suggest combining a different well-designed scoring function for non-hydrophobic interactions with HRM to achieve better performance in fold recognition.
Keywords: decoy set; hydrophobic amino acid; knowledge-based potential; protein native structure.
© 2018 Wiley Periodicals, Inc.