title: Identifying the Types of Ion Channel-Targeted Conotoxins by Incorporating New Properties of Residues into Pseudo Amino Acid Composition.
abstract: Conotoxins are a kind of neurotoxin which can specifically interact with potassium, sodium type, and calcium channels. They have become potential drug candidates to treat diseases such as chronic pain, epilepsy, and cardiovascular diseases. Thus, correctly identifying the types of ion channel-targeted conotoxins will provide important clue to understand their function and find potential drugs. Based on this consideration, we developed a new computational method to rapidly and accurately predict the types of ion-targeted conotoxins. Three kinds of new properties of residues were proposed to use in pseudo amino acid composition to formulate conotoxins samples. The support vector machine was utilized as classifier. A feature selection technique based on F-score was used to optimize features. Jackknife cross-validated results showed that the overall accuracy of 94.6% was achieved, which is higher than other published results, demonstrating that the proposed method is superior to published methods. Hence the current method may play a complementary role to other existing methods for recognizing the types of ion-target conotoxins.
authors.affiliation: College of Computer and Information Engineering, Xiamen University of Technology, Xiamen 361024, China.
authors.forename: Yun
authors.initials: Y
authors.lastname: Wu
authors.affiliation: College of Computer and Information Engineering, Xiamen University of Technology, Xiamen 361024, China.
authors.forename: Yufei
authors.identifier: 0000-0002-3800-7676
authors.initials: Y
authors.lastname: Zheng
authors.affiliation: Department of Pathophysiology, Southwest Medical University, Luzhou 646000, China.
authors.forename: Hua
authors.identifier: 0000-0001-6728-4544
authors.initials: H
authors.lastname: Tang
mesh_terms: D000465:Algorithms; D000595:Amino Acid Sequence; D019943:Amino Acid Substitution; D001665:Binding Sites; D020916:Conotoxins / Q000737:chemistry*; D007473:Ion Channels / Q000737:chemistry*; D000069550:Machine Learning; D062105:Molecular Docking Simulation / Q000379:methods*; D008969:Molecular Sequence Data; D010363:Pattern Recognition, Automated / Q000379:methods; D011485:Protein Binding; D025941:Protein Interaction Mapping / Q000379:methods*; D020539:Sequence Analysis, Protein / Q000379:methods*
chemical_list: D020916:Conotoxins; D007473:Ion Channels