title: COMSAT: Residue contact prediction of transmembrane proteins based on support vector machines and mixed integer linear programming.
abstract: In this article, we present COMSAT, a hybrid framework for residue contact prediction of transmembrane (TM) proteins, integrating a support vector machine (SVM) method and a mixed integer linear programming (MILP) method. COMSAT consists of two modules: COMSAT_SVM which is trained mainly on position-specific scoring matrix features, and COMSAT_MILP which is an ab initio method based on optimization models. Contacts predicted by the SVM model are ranked by SVM confidence scores, and a threshold is trained to improve the reliability of the predicted contacts. For TM proteins with no contacts above the threshold, COMSAT_MILP is used. The proposed hybrid contact prediction scheme was tested on two independent TM protein sets based on the contact definition of 14 Å between Cα-Cα atoms. First, using a rigorous leave-one-protein-out cross validation on the training set of 90 TM proteins, an accuracy of 66.8%, a coverage of 12.3%, a specificity of 99.3% and a Matthews' correlation coefficient (MCC) of 0.184 were obtained for residue pairs that are at least six amino acids apart. Second, when tested on a test set of 87 TM proteins, the proposed method showed a prediction accuracy of 64.5%, a coverage of 5.3%, a specificity of 99.4% and a MCC of 0.106. COMSAT shows satisfactory results when compared with 12 other state-of-the-art predictors, and is more robust in terms of prediction accuracy as the length and complexity of TM protein increase. COMSAT is freely accessible at http://hpcc.siat.ac.cn/COMSAT/.
authors.affiliation: Centre for High Performance Computing, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, 518055, China.
authors.forename: Huiling
authors.initials: H
authors.lastname: Zhang
authors.affiliation: Centre for High Performance Computing, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, 518055, China.
authors.forename: Qingsheng
authors.initials: Q
authors.lastname: Huang
authors.affiliation: Center for Cloud Computing, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, 518055, China.
authors.forename: Zhendong
authors.initials: Z
authors.lastname: Bei
authors.affiliation: Centre for High Performance Computing, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, 518055, China.
authors.forename: Yanjie
authors.initials: Y
authors.lastname: Wei
authors.affiliation: Department of Chemical Engineering, Texas A&M University, College Station, Texas, 77843.|Texas A&M Energy Institute, Texas A&M University, College Station, Texas, 77843.
authors.forename: Christodoulos A
authors.initials: CA
authors.lastname: Floudas
mesh_terms: D000465:Algorithms; D003198:Computer Simulation; D008565:Membrane Proteins / Q000737:chemistry*; D008958:Models, Molecular; D010363:Pattern Recognition, Automated; D056510:Position-Specific Scoring Matrices; D011382:Programming, Linear; D017433:Protein Structure, Secondary; D017434:Protein Structure, Tertiary; D015203:Reproducibility of Results; D016415:Sequence Alignment; D020539:Sequence Analysis, Protein; D040681:Structural Homology, Protein; D060388:Support Vector Machine
chemical_list: D008565:Membrane Proteins
keywords: MILP; ab initio prediction; hybrid framework; machine learning; protein structure prediction