title: Using amino acid physicochemical distance transformation for fast protein remote homology detection.
abstract: Protein remote homology detection is one of the most important problems in bioinformatics. Discriminative methods such as support vector machines (SVM) have shown superior performance. However, the performance of SVM-based methods depends on the vector representations of the protein sequences. Prior works have demonstrated that sequence-order effects are relevant for discrimination, but little work has explored how to incorporate the sequence-order information along with the amino acid physicochemical properties into the prediction. In order to incorporate the sequence-order effects into the protein remote homology detection, the physicochemical distance transformation (PDT) method is proposed. Each protein sequence is converted into a series of numbers by using the physicochemical property scores in the amino acid index (AAIndex), and then the sequence is converted into a fixed length vector by PDT. The sequence-order information can be efficiently included into the feature vector with little computational cost by this approach. Finally, the feature vectors are input into a support vector machine classifier to detect the protein remote homologies. Our experiments on a well-known benchmark show the proposed method SVM-PDT achieves superior or comparable performance with current state-of-the-art methods and its computational cost is considerably superior to those of other methods. When the evolutionary information extracted from the frequency profiles is combined with the PDT method, the profile-based PDT approach can improve the performance by 3.4% and 11.4% in terms of ROC score and ROC50 score respectively. The local sequence-order information of the protein can be efficiently captured by the proposed PDT and the physicochemical properties extracted from the amino acid index are incorporated into the prediction. The physicochemical distance transformation provides a general framework, which would be a valuable tool for protein-level study.
authors.affiliation: School of Computer Science and Technology, Harbin Institute of Technology Shenzhen Graduate School, Shenzhen, Guangdong, People's Republic of China. bliu@insun.hit.edu.cn
authors.forename: Bin
authors.initials: B
authors.lastname: Liu
authors.forename: Xiaolong
authors.initials: X
authors.lastname: Wang
authors.forename: Qingcai
authors.initials: Q
authors.lastname: Chen
authors.forename: Qiwen
authors.initials: Q
authors.lastname: Dong
authors.forename: Xun
authors.initials: X
authors.lastname: Lan
mesh_terms: D000596:Amino Acids / Q000737:chemistry*; D019143:Evolution, Molecular; D008390:Markov Chains; D008958:Models, Molecular; D011506:Proteins / Q000737:chemistry* / Q000235:genetics; D012372:ROC Curve; D020539:Sequence Analysis, Protein / Q000379:methods*; D017386:Sequence Homology, Amino Acid; D060388:Support Vector Machine
chemical_list: D000596:Amino Acids; D011506:Proteins