title: PSSP-RFE: accurate prediction of protein structural class by recursive feature extraction from PSI-BLAST profile, physical-chemical property and functional annotations.
abstract: Protein structure prediction is critical to functional annotation of the massively accumulated biological sequences, which prompts an imperative need for the development of high-throughput technologies. As a first and key step in protein structure prediction, protein structural class prediction becomes an increasingly challenging task. Amongst most homological-based approaches, the accuracies of protein structural class prediction are sufficiently high for high similarity datasets, but still far from being satisfactory for low similarity datasets, i.e., below 40% in pairwise sequence similarity. Therefore, we present a novel method for accurate and reliable protein structural class prediction for both high and low similarity datasets. This method is based on Support Vector Machine (SVM) in conjunction with integrated features from position-specific score matrix (PSSM), PROFEAT and Gene Ontology (GO). A feature selection approach, SVM-RFE, is also used to rank the integrated feature vectors through recursively removing the feature with the lowest ranking score. The definitive top features selected by SVM-RFE are input into the SVM engines to predict the structural class of a query protein. To validate our method, jackknife tests were applied to seven widely used benchmark datasets, reaching overall accuracies between 84.61% and 99.79%, which are significantly higher than those achieved by state-of-the-art tools. These results suggest that our method could serve as an accurate and cost-effective alternative to existing methods in protein structural classification, especially for low similarity datasets.
authors.affiliation: Department of General Surgery, Xinqiao Hospital, Third Military Medical University, Chongqing, China.
authors.forename: Liqi
authors.initials: L
authors.lastname: Li
authors.affiliation: Department of Orthopedics, Xinqiao Hospital, Third Military Medical University, Chongqing, China.
authors.forename: Xiang
authors.initials: X
authors.lastname: Cui
authors.affiliation: Institute of Cardiovascular Diseases of PLA, Xinqiao Hospital, Third Military Medical University, Chongqing, China.
authors.forename: Sanjiu
authors.initials: S
authors.lastname: Yu
authors.affiliation: Department of Orthopedics, Xinqiao Hospital, Third Military Medical University, Chongqing, China.
authors.forename: Yuan
authors.initials: Y
authors.lastname: Zhang
authors.affiliation: Key Laboratory of Biorheological Science and Technology, Ministry of Education, College of Bioengineering, Chongqing University, Chongqing, China.
authors.forename: Zhong
authors.initials: Z
authors.lastname: Luo
authors.affiliation: Department of General Surgery, Xinqiao Hospital, Third Military Medical University, Chongqing, China; Department of Surgery, The University of Michigan Medical School, Ann Arbor, Michigan, United States of America.
authors.forename: Hua
authors.initials: H
authors.lastname: Yang
authors.affiliation: Department of Orthopedics, Xinqiao Hospital, Third Military Medical University, Chongqing, China.
authors.forename: Yue
authors.initials: Y
authors.lastname: Zhou
authors.affiliation: Department of Mathematics, Shanghai Normal University, Shanghai, China; Department of Biostatistics and Computational Biology, Harvard School of Public Health, Boston, United States of America.
authors.forename: Xiaoqi
authors.initials: X
authors.lastname: Zheng
mesh_terms: D000465:Algorithms; D019295:Computational Biology / Q000379:methods*; D066264:Datasets as Topic; D011487:Protein Conformation; D011506:Proteins / Q000737:chemistry* / Q000235:genetics; D012372:ROC Curve; D015203:Reproducibility of Results; D012984:Software; D013329:Structure-Activity Relationship
chemical_list: D011506:Proteins