Abstract
G-protein coupled receptors (GPCRs) are a class of pharmacologically relevant transmembrane proteins with specific characteristics. They play a key role in different biological process and are very important for understanding human diseases. However, ligand specificity of many receptors remains unknown and only one crystal structure solved to date. It is highly desirable to predict receptor’s type using only sequence information. In this paper, Support Vector Machine is introduced to predict receptor’s type based on its amino acid composition. The prediction is performed to the amine-binding classes of the rhodopsin-like family. The overall predictive accuracy about 94% has been achieved in a ten-fold cross-validation.
This work was funded by the National Natural Science Grant in China (No.60171038 and No.60234020) and the National Basic Research Priorities Program of the Ministry of Science and Technology (No.2001CCA0). Y.H. also thanks Tsinghua University Ph.D. Grant for the support.
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Huang, Y., Li, Y. (2004). Classifying G-protein Coupled Receptors with Support Vector Machine. In: Yin, FL., Wang, J., Guo, C. (eds) Advances in Neural Networks - ISNN 2004. ISNN 2004. Lecture Notes in Computer Science, vol 3174. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-28648-6_71
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DOI: https://doi.org/10.1007/978-3-540-28648-6_71
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