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In this chapter we provide a survey of protein secondary and supersecondary structure prediction using methods from machine learning. Our focus is on machine learning methods applicable to β-hairpin and β-sheet prediction, but we also discuss methods for more general supersecondary structure prediction. We provide background on the secondary and supersecondary structures that we discuss, the features used to describe them, and the basic theory behind the machine learning methods used. We survey the machine learning methods available for secondary and supersecondary structure prediction and compare them where possible.


Hui Kian Ho, Lei Zhang, Kotagiri Ramamohanarao, Shawn Martin. A survey of machine learning methods for secondary and supersecondary protein structure prediction. Methods in molecular biology (Clifton, N.J.). 2013;932:87-106

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PMID: 22987348

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