Protein Secondary Structure Prediction Approaches: A Review With Focus on Deep Learning Methods

Protein Secondary Structure Prediction Approaches: A Review With Focus on Deep Learning Methods

Fawaz H. H. Mahyoub, Rosni Abdullah
Copyright: © 2020 |Pages: 23
DOI: 10.4018/978-1-7998-1192-3.ch015
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Abstract

The prediction of protein secondary structure from a protein sequence provides useful information for predicting the three-dimensional structure and function of the protein. In recent decades, protein secondary structure prediction systems have been improved benefiting from the advances in computational techniques as well as the growth and increased availability of solved protein structures in protein data banks. Existing methods for predicting the secondary structure of proteins can be roughly subdivided into statistical, nearest-neighbor, machine learning, meta-predictors, and deep learning approaches. This chapter provides an overview of these computational approaches to predict the secondary structure of proteins, focusing on deep learning techniques, with highlights on key aspects in each approach.
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Background

Proteins

Proteins are the centre of many biological activities, including transporting molecules (Klingenberg, 1981), responding to stimuli (Yoshida, Sanematsu, Shigemura, Yasumatsu, & Ninomiya, 2005), and catalysing metabolic reactions necessary for life possible (Margolis, 2008). Furthermore, proteins act as chemical go-betweens to sustain internal communication, as regulators to turn genes on and off, and as storages to store nutrients and energy-rich molecules for later use (Zvelebil & Baum, 2007). Most of the proteins are globular. Globular proteins are simpler to crystallize because of their chemical characteristics. Conversely, non-globular proteins such as fibrous and membrane proteins are frequently defined by numerous repeated amino acid sequences with less distinctive chemical characteristics (Yoo, Zhou, & Zomaya, 2008).

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