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Predicting the Start of Protein α-Helices Using Machine Learning Algorithms

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Advances in Bioinformatics

Introduction

Proteins are complex structures synthesised by living organisms. They are actually a fundamental type of molecules and can perform a large number of functions in cell biology. Proteins can assume catalytic roles and accelerate or inhibit chemical reactions in our body. They can assume roles of transportation of smaller molecules, storage, movement, mechanical support, immunity and control of cell growth and differentiation [25]. All of these functions rely on the 3D-structure of the protein. The process of going from a linear sequence of amino acids, that together compose a protein, to the protein’s 3D shape is named protein folding. Anfinsen’s work [29] has proven that primary structure determines the way protein folds. Protein folding is so important that whenever it does not occur correctly it may produce diseases such as Alzheimer’s, Bovine Spongiform Encephalopathy (BSE), usually known as mad cows disease, Creutzfeldt-Jakob (CJD) disease, a Amyotrophic Lateral Sclerosis (ALS), Huntingtons syndrome, Parkinson disease, and other diseases related to cancer.

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Camacho, R. et al. (2010). Predicting the Start of Protein α-Helices Using Machine Learning Algorithms. In: Rocha, M.P., Riverola, F.F., Shatkay, H., Corchado, J.M. (eds) Advances in Bioinformatics. Advances in Intelligent and Soft Computing, vol 74. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-13214-8_5

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  • DOI: https://doi.org/10.1007/978-3-642-13214-8_5

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