title: Combining evolutionary information extracted from frequency profiles with sequence-based kernels for protein remote homology detection.
abstract: BACKGROUND
  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.
  
  
  METHODS
  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/
authors: School of Computer Science and Technology and Key Laboratory of Network Oriented Intelligent Computation, Harbin Institute of Technology Shenzhen Graduate School, Shenzhen, Guangdong 518055, China, Shanghai Key Laboratory of Intelligent Information Processing, Shanghai 200433, China, Gordon Life Science Institute, Belmont, MA 02478, USA, School of Computer, Shenyang Aerospace University, Shenyang, Liaoning, China, School of Computer Science, Fudan University, Shanghai 200433, China and Center of Excellence in Genomic Medicine Research (CEGMR), King Abdulaziz University, Jeddah 21589, Saudi Arabia.
authors: Bin
authors: B
authors: Liu
authors: Deyuan
authors: D
authors: Zhang
authors: Ruifeng
authors: R
authors: Xu
authors: Jinghao
authors: J
authors: Xu
authors: Xiaolong
authors: X
authors: Wang
authors: Qingcai
authors: Q
authors: Chen
authors: Qiwen
authors: Q
authors: Dong
authors: Kuo-Chen
authors: KC
authors: Chou
mesh_terms: D000465:Algorithms; D020816:Amino Acid Motifs; D019925:Cyclin A / Q000737:chemistry; D019143:Evolution, Molecular; D006801:Humans; D009419:Nerve Tissue Proteins / Q000737:chemistry; D010363:Pattern Recognition, Automated; D011506:Proteins / Q000737:chemistry*; D016415:Sequence Alignment; D020539:Sequence Analysis, Protein / Q000379:methods*; D017386:Sequence Homology, Amino Acid; D012984:Software; D060388:Support Vector Machine
chemical_list: D019925:Cyclin A; D009419:Nerve Tissue Proteins; D011506:Proteins; C089495:neuronal Cdk5 activator (p25-p35)