Bioinformatics: the machine learning approach
Bioinformatics: the machine learning approach
Protein fold recognition using neural networks and support vector machines
IDEAL'05 Proceedings of the 6th international conference on Intelligent Data Engineering and Automated Learning
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Protein threading programs align a probe amino acid sequence onto a library of representative folds of known protein structure to identify a structural homology. A scoring function is usually formulated in terms of the threading energy to evaluate protein sequence-structure fitness. The structure that yields the lowest total energy is considered the leading template of the probe protein. An alternative approach is to predict the probabilities of observing amino acid side-chains in structural environment without considering the energy of contacts. In this paper, a model named TES is proposed on building a new environment-specific protein sequence-structure mapping with artificial neural network. The decoy sets obtained from the web are used to test the proposed TES method on discrimination of native and decoy protein three-dimensional structure. The verified approach shows that the performance of the proposed method is comparable to those of knowledge-based potential energy function.