Environment-dependent one-body score function for proteins by perceptron learning and protein threading

Abstract

The design and construction of a global protein energy function which can recognize the native folds of all representative proteins of different classes with a low sequence homology has been one of the important issues and a formidable task in protein science. We used perceptron learning and protein threading to construct a one-body score function of proteins, which could recognize simultaneously the native folds of 1,006 training proteins covering all available representative proteins in a sequence homology with less than 30 % between them. When the score parameters for the 1,006 training proteins were subject to a threading test, 370 (96.9 %) native folds of the 382 new distinct proteins were recognized compared to the previous score parameters obtained using 387 training-proteins, which recognized 190 (89.2 %) native folds of the 213 new proteins. We performed an analysis of the score parameters by using a singular value decomposition and a self-organizing map to elucidate the biological clustering characters of 20 amino acids. The self-organizing map analysis of the new score-parameters revealed better biological clustering of 20 amino acids, which agreed with their known properties. The same analysis for the previous score-parameters could not provide such a result because the 387 training proteins employed before did not fully cover all representative proteins of different classes of a sequence homology with less than 30 % between them. We illuminated the marked difference in the new score-parameters that relative to previous score-parameters, not only performed better in recognizing the native folds of new distinct proteins but also captured better the biological clustering characters of amino acids.