title: Prediction of transmembrane regions of beta-barrel proteins using ANN- and SVM-based methods.
abstract: This article describes a method developed for predicting transmembrane beta-barrel regions in membrane proteins using machine learning techniques: artificial neural network (ANN) and support vector machine (SVM). The ANN used in this study is a feed-forward neural network with a standard back-propagation training algorithm. The accuracy of the ANN-based method improved significantly, from 70.4% to 80.5%, when evolutionary information was added to a single sequence as a multiple sequence alignment obtained from PSI-BLAST. We have also developed an SVM-based method using a primary sequence as input and achieved an accuracy of 77.4%. The SVM model was modified by adding 36 physicochemical parameters to the amino acid sequence information. Finally, ANN- and SVM-based methods were combined to utilize the full potential of both techniques. The accuracy and Matthews correlation coefficient (MCC) value of SVM, ANN, and combined method are 78.5%, 80.5%, and 81.8%, and 0.55, 0.63, and 0.64, respectively. These methods were trained and tested on a nonredundant data set of 16 proteins, and performance was evaluated using "leave one out cross-validation" (LOOCV). Based on this study, we have developed a Web server, TBBPred, for predicting transmembrane beta-barrel regions in proteins (available at http://www.imtech.res.in/raghava/tbbpred).
authors.affiliation: Institute of Microbial Technology, Chandigarh, India.
authors.forename: Navjyot K
authors.initials: NK
authors.lastname: Natt
authors.forename: Harpreet
authors.initials: H
authors.lastname: Kaur
authors.forename: G P S
authors.initials: GP
authors.lastname: Raghava
mesh_terms: D000465:Algorithms; D001426:Bacterial Proteins / Q000737:chemistry; D002462:Cell Membrane / Q000378:metabolism*; D019295:Computational Biology / Q000379:methods*; D003198:Computer Simulation; D019143:Evolution, Molecular; D057927:Hydrophobic and Hydrophilic Interactions; D016247:Information Storage and Retrieval; D020407:Internet; D008565:Membrane Proteins / Q000737:chemistry* / Q000378:metabolism; D008958:Models, Molecular; D016571:Neural Networks, Computer; D018583:Pliability; D017433:Protein Structure, Secondary; D012680:Sensitivity and Specificity; D016415:Sequence Alignment; D012997:Solvents / Q000737:chemistry; D013816:Thermodynamics
chemical_list: D001426:Bacterial Proteins; D008565:Membrane Proteins; D012997:Solvents