Abstract
In this paper, a new fold recognition model with mixed environment-specific substitution mapping (called MESSM) is proposed with three key features: 1) a structurally-derived substitution score is generated using neural networks; 2) a mixed environment-specific substitution mapping is developed by combing the structural-derived substitution score with sequence profile from well-developed sequence substitution matrices; 3) a support vector machine is employed to measure the significance of the sequence-structure alignment. Tested on two benchmark problems, the MESSM model shows comparable performance to those more computational intensive, energy potential based fold recognition models. The results also demonstrate that the new fold recognition model with mixed substitution mapping has a better performance than the one with either structure or sequence profile only. The MESSM model presents a new way to develop an efficient tool for protein fold recognition.
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Jiang, N., Wu, W.X., Mitchell, I. (2005). Protein Fold Recognition Using Neural Networks and Support Vector Machines. In: Gallagher, M., Hogan, J.P., Maire, F. (eds) Intelligent Data Engineering and Automated Learning - IDEAL 2005. IDEAL 2005. Lecture Notes in Computer Science, vol 3578. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11508069_60
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DOI: https://doi.org/10.1007/11508069_60
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