NONSYMMETRIC TWO-BODY SCORE FUNCTION FOR PROTEIN FOLD RECOGNITION: NEXT NEAREST NEIGHBOR-ADJACENCY OF TWO AMINO ACIDS

Muyoung Heo, Mookyung Cheon and Iksoo Chang ()
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Muyoung Heo: National Research Laboratory for Computational Proteomics and Biophysics, Department of Physics, Pusan National University, Busan 609-735, Korea
Mookyung Cheon: National Research Laboratory for Computational Proteomics and Biophysics, Department of Physics, Pusan National University, Busan 609-735, Korea
Iksoo Chang: National Research Laboratory for Computational Proteomics and Biophysics, Department of Physics, Pusan National University, Busan 609-735, Korea

International Journal of Modern Physics C (IJMPC), 2004, vol. 15, issue 08, 1087-1094

Abstract: 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 theN-terminus to theC-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.

Keywords: Protein fold recognition; protein score function; perceptron learning; protein threading (search for similar items in EconPapers)
Date: 2004
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DOI: 10.1142/S0129183104006546

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