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Molecules 2017, 22(7), 1057;

Recent Advances in Conotoxin Classification by Using Machine Learning Methods

Key Laboratory for Neuro-Information of Ministry of Education, School of Life Science and Technology, Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu 610054, China
Development and Planning Department, Inner Mongolia University, Hohhot 010021, China
College of Computer and Information Engineering, Xiamen University of Technology, Xiamen 361024, China
Department of Physics, School of Sciences, and Center for Genomics and Computational Biology, North China University of Science and Technology, Tangshan 063000, China
Department of Pathophysiology, Southwest Medical University, Luzhou 646000, China
Authors to whom correspondence should be addressed.
Received: 17 May 2017 / Revised: 12 June 2017 / Accepted: 19 June 2017 / Published: 25 June 2017
(This article belongs to the Special Issue Computational Analysis for Protein Structure and Interaction)
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Conotoxins are disulfide-rich small peptides, which are invaluable peptides that target ion channel and neuronal receptors. Conotoxins have been demonstrated as potent pharmaceuticals in the treatment of a series of diseases, such as Alzheimer’s disease, Parkinson’s disease, and epilepsy. In addition, conotoxins are also ideal molecular templates for the development of new drug lead compounds and play important roles in neurobiological research as well. Thus, the accurate identification of conotoxin types will provide key clues for the biological research and clinical medicine. Generally, conotoxin types are confirmed when their sequence, structure, and function are experimentally validated. However, it is time-consuming and costly to acquire the structure and function information by using biochemical experiments. Therefore, it is important to develop computational tools for efficiently and effectively recognizing conotoxin types based on sequence information. In this work, we reviewed the current progress in computational identification of conotoxins in the following aspects: (i) construction of benchmark dataset; (ii) strategies for extracting sequence features; (iii) feature selection techniques; (iv) machine learning methods for classifying conotoxins; (v) the results obtained by these methods and the published tools; and (vi) future perspectives on conotoxin classification. The paper provides the basis for in-depth study of conotoxins and drug therapy research. View Full-Text
Keywords: conotoxin; superfamily; ion channel; machine learning method conotoxin; superfamily; ion channel; machine learning method

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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).

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Dao, F.-Y.; Yang, H.; Su, Z.-D.; Yang, W.; Wu, Y.; Hui, D.; Chen, W.; Tang, H.; Lin, H. Recent Advances in Conotoxin Classification by Using Machine Learning Methods. Molecules 2017, 22, 1057.

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