Document Details

Document Type : Article In Journal 
Document Title :
Combining evolutionary information extracted from frequency profiles with sequence-based kernels for protein remote homology detection
Combining evolutionary information extracted from frequency profiles with sequence-based kernels for protein remote homology detection
 
Document Language : English 
Abstract : MOTIVATION: Owing to its importance in both basic research (such as molecular evolution and protein attribute prediction) and practical application (such as timely modeling the 3D structures of proteins targeted for drug development), protein remote homology detection has attracted a great deal of interest. It is intriguing to note that the profile-based approach is promising and holds high potential in this regard. To further improve protein remote homology detection, a key step is how to find an optimal means to extract the evolutionary information into the profiles. RESULTS: Here, we propose a novel approach, the so-called profile-based protein representation, to extract the evolutionary information via the frequency profiles. The latter can be calculated from the multiple sequence alignments generated by PSI-BLAST. Three top performing sequence-based kernels (SVM-Ngram, SVM-pairwise and SVM-LA) were combined with the profile-based protein representation. Various tests were conducted on a SCOP benchmark dataset that contains 54 families and 23 superfamilies. The results showed that the new approach is promising, and can obviously improve the performance of the three kernels. Furthermore, our approach can also provide useful insights for studying the features of proteins in various families. It has not escaped our notice that the current approach can be easily combined with the existing sequence-based methods so as to improve their performance as well. AVAILABILITY AND IMPLEMENTATION: For users' convenience, the source code of generating the profile-based proteins and the multiple kernel learning was also provided at http://bioinformatics.hitsz.edu.cn/main/~binliu/remote/ 
ISSN : 1367-4803 
Journal Name : Bioinformatics 
Volume : 30 
Issue Number : 4 
Publishing Year : 1434 AH
2013 AD 
Article Type : Article 
Added Date : Monday, April 4, 2016 

Researchers

Researcher Name (Arabic)Researcher Name (English)Researcher TypeDr GradeEmail
Bin LiuLiu, Bin Investigator  
Deyuan ZhangZhang, Deyuan Researcher  
Ruifeng XuXu, Ruifeng Researcher  
Jinghao XuXu, Jinghao Researcher  
Xiaolong WangWang, Xiaolong Researcher  
Qingcai ChenChen, Qingcai Researcher  
Qiwen DongDong, Qiwen Researcher  
Kuo-Chen ChouChou, Kuo-Chen Researcher  

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