iCTX-Type: A Sequence-Based Predictor for Identifying the Types of Conotoxins in Targeting Ion Channels

被引:221
作者
Ding, Hui [1 ]
Deng, En-Ze [1 ]
Yuan, Lu-Feng [1 ]
Liu, Li [2 ]
Lin, Hao [1 ,3 ]
Chen, Wei [3 ,4 ]
Chou, Kuo-Chen [3 ,5 ]
机构
[1] Univ Elect Sci & Technol China, Ctr Bioinformat, Sch Life Sci & Technol, Key Lab Neuro Informat,Minist Educ, Chengdu 610054, Peoples R China
[2] Inner Mongolia Univ, Sch Phys Sci & Technol, Lab Theoret Biophys, Hohhot 010021, Peoples R China
[3] Gordon Life Sci Inst, Boston, MA 02478 USA
[4] Hebei United Univ, Ctr Genom & Computat Biol, Sch Sci, Dept Phys, Tangshan 063000, Peoples R China
[5] King Abdulaziz Univ, CEGMR, Jeddah 21589, Saudi Arabia
关键词
AMINO-ACID-COMPOSITION; PROTEIN STRUCTURAL CLASS; SUPPORT VECTOR MACHINES; RADIAL BASIS FUNCTION; FLEXIBLE WEB SERVER; GENERAL-FORM; SUBCELLULAR LOCATION; PSEAAC; CLASSIFIER; PEPTIDES;
D O I
10.1155/2014/286419
中图分类号
Q81 [生物工程学(生物技术)]; Q93 [微生物学];
学科分类号
071005 ; 0836 ; 090102 ; 100705 ;
摘要
Conotoxins are small disulfide-rich neurotoxic peptides, which can bind to ion channels with very high specificity and modulate their activities. Over the last few decades, conotoxins have been the drug candidates for treating chronic pain, epilepsy, spasticity, and cardiovascular diseases. According to their functions and targets, conotoxins are generally categorized into three types: potassium-channel type, sodium-channel type, and calcium-channel types. With the avalanche of peptide sequences generated in the postgenomic age, it is urgent and challenging to develop an automated method for rapidly and accurately identifying the types of conotoxins based on their sequence information alone. To address this challenge, a new predictor, called iCTX-Type, was developed by incorporating the dipeptide occurrence frequencies of a conotoxin sequence into a 400-D (dimensional) general pseudoamino acid composition, followed by the feature optimization procedure to reduce the sample representation from 400-D to 50-D vector. The overall success rate achieved by iCTX-Type via a rigorous cross-validation was over 91%, outperforming its counterpart (RBF network). Besides, iCTX-Type is so far the only predictor in this area with its web-server available, and hence is particularly useful for most experimental scientists to get their desired results without the need to follow the complicated mathematics involved.
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页数:10
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