What are the computational tools for the prediction of beta-barrel transmembrane proteins?
The computational tools for the prediction of beta-barrel transmembrane proteins (TMBs) are based mainly on the following methodologies: Hidden Markov Models (HMMs), hydrophobicity, structural data, k-nearest neighbor algorithm, Neural Networks and Support Vector Machines. The state-of-the-art computational tools for the prediction of TMBs are: BETAWARE, BOCTOPUS, BOMP, BTMX, HMM-B2TMR, OMPdb,PRED-TMBB, PROB, ProfTMB, PV, TMBETA-NET, TMB finding pipeline, TMBETADISC-RBF, TMBETAPRED-RBF, TMBHMM, TMB-Hunt, TMB-Hunt2, TMBKNN, TMBpro, transFold.