A Comparative Study of Machine Learning and Evolutionary Computation Approaches for Protein Secondary Structure Classification

Proteins are essential to life and they have countless biological functions. Proteins are synthesized in the ribosome of cells following a template given by the messenger RNA (mRNA). During the synthesis, the protein folds into a unique three-dimensional structure, known as native conformation. This process is called protein folding. The biological function of a protein depends on its three-dimensional conformation, which in turn, is a function of its primary and secondary structures. It is known that ill-formed proteins can be completely inactive or even harmful to the organism. Several diseases are believed to result from the accumulation of ill-formed proteins, such as Alzheimer’s disease, cystic fibrosis, Huntington’s disease and some types of cancer. Therefore, acquiring knowledge about the secondary structure of proteins is an important issue, since such knowledge can lead to important medical and biochemical advancements and even to the development of new drugs with specific functionality. A possible way to infer the full structure of an unknown protein is to identify potential secondary structures in it. However, the pattern formation rules of secondary structure of proteins are still not known precisely. This paper aims at applying Machine Learning and Evolutionary Computation methods to define suitable classifiers for predicting the secondary structure of proteins, starting from their primary structure (that is, their linear sequence of amino acids). The organization of this paper is as follows: in Section 2 we introduce some basic concepts and some important aspects of molecular biology, computational methods for classification tasks and the protein classification problem. Next, in Sections 3 and 4, we present, respectively, a review of the machine learning and evolutionary computation methods used in this work. In Section 6, we describe the methodology applied to develop the comparison of different classification algorithms. Next, Section 7, the computational experiments and results are detailed. Finally, in the last Section 8, discussion about results, conclusions and future directions are pointed out. 12

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