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Prediction of protein structural classes using support vector machines

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Summary.

The support vector machine, a machine-learning method, is used to predict the four structural classes, i.e. mainly α, mainly β, α–β and fss, from the topology-level of CATH protein structure database. For the binary classification, any two structural classes which do not share any secondary structure such as α and β elements could be classified with as high as 90% accuracy. The accuracy, however, will decrease to less than 70% if the structural classes to be classified contain structure elements in common. Our study also shows that the dimensions of feature space 202 = 400 (for dipeptide) and 203 = 8 000 (for tripeptide) give nearly the same prediction accuracy. Among these 4 structural classes, multi-class classification gives an overall accuracy of about 52%, indicating that the multi-class classification technique in support of vector machines may still need to be further improved in future investigation.

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  1. College of Life Science and Biotechnology, Guangxi University, Nanning, Guangxi, China

    X.-D. Sun & R.-B. Huang

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Sun, XD., Huang, RB. Prediction of protein structural classes using support vector machines. Amino Acids 30, 469–475 (2006). https://doi.org/10.1007/s00726-005-0239-0

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