The usual two-body score (energy) function to recognize native folds of proteins is Miyazawa–Jernigan (MJ) pairwise-contact function. The pairwise-contact parameters between two amino acids in MJ function are symmetric in a sense that a directional order of amino acids sequence along the backbone of a protein is ignored in constructing score parameters. Here we report that we succeeded in constructing a nonsymmetric two-body score function, capturing a directional order of amino acids sequence, by a perceptron learning and a protein threading. We considered pairs of two adjacent amino acids that are separated by two consecutive peptide bonds with the backbone directionality from the N-terminus to the C-terminus of a protein. We also considered the local environmental character, such as the secondary structures and the hydrophobicity (solvation), of amino acids in protein structures. The score is a corresponding propensity for a directional alignment of these two adjacent amino acids with their local environments. The resulting score function simultaneously recognized native folds of 1006 proteins covering all representative proteins with a homology less than 30% among them. The quality of this score function was validated by a threading test of new distinct 382 proteins with a homology less than 90% among them, and it entailed a high success ratio for recognizing native folds of 364 (95.3%) proteins. It showed a good feasibility of designing protein score functions for protein fold recognition by a perceptron learning and a protein threading.
A machine learning information retrieval approach to protein fold recognition
