Guest editorial: Computational intelligence and machine learning in bioinformatics
Artificial Intelligence in Medicine
Topology prediction of α-helical and β-barrel transmembrane proteins using RBF networks
ICIC'10 Proceedings of the 6th international conference on Advanced intelligent computing theories and applications: intelligent computing
Bayesian Models and Algorithms for Protein β-Sheet Prediction
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
Prediction of permuted super-secondary structures in β-barrel proteins
Proceedings of the 2011 ACM Symposium on Applied Computing
Computational Biology and Chemistry
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Motivation: Transmembrane β-barrel (TMB) proteins are embedded in the outer membranes of mitochondria, Gram-negative bacteria and chloroplasts. These proteins perform critical functions, including active ion-transport and passive nutrient intake. Therefore, there is a need for accurate prediction of secondary and tertiary structure of TMB proteins. Traditional homology modeling methods, however, fail on most TMB proteins since very few non-homologous TMB structures have been determined. Yet, because TMB structures conform to specific construction rules that restrict the conformational space drastically, it should be possible for methods that do not depend on target-template homology to be applied successfully. Results: We develop a suite (TMBpro) of specialized predictors for predicting secondary structure (TMBpro-SS), β-contacts (TMBpro-CON) and tertiary structure (TMBpro-3D) of transmembrane β-barrel proteins. We compare our results to the recent state-of-the-art predictors transFold and PRED-TMBB using their respective benchmark datasets, and leave-one-out cross-validation. Using the transFold dataset TMBpro predicts secondary structure with per-residue accuracy (Q2) of 77.8%, a correlation coefficient of 0.54, and TMBpro predicts β-contacts with precision of 0.65 and recall of 0.67. Using the PRED-TMBB dataset, TMBpro predicts secondary structure with Q2 of 88.3% and a correlation coefficient of 0.75. All of these performance results exceed previously published results by 4% or more. Working with the PRED-TMBB dataset, TMBpro predicts the tertiary structure of transmembrane segments with RMSD Availability: http://www.igb.uci.edu/servers/psss.html Contact: pfbaldi@ics.uci.edu Supplementary information: Supplementary data are available at Bioinformatics online.