Sikander Hayat1, Arne Elofsson. 1. Center for Biomembrane Research, Department of Biochemistry and Biophysics, Stockholm Bioinformatics Center, SciLifeLab, Swedish E-science Research Center, Stockholm University, SE-10691 Stockholm, Sweden.
Abstract
MOTIVATION: Transmembrane β barrel proteins (TMBs) are found in the outer membrane of Gram-negative bacteria, chloroplast and mitochondria. They play a major role in the translocation machinery, pore formation, membrane anchoring and ion exchange. TMBs are also promising targets for antimicrobial drugs and vaccines. Given the difficulty in membrane protein structure determination, computational methods to identify TMBs and predict the topology of TMBs are important. RESULTS: Here, we present BOCTOPUS; an improved method for the topology prediction of TMBs by employing a combination of support vector machines (SVMs) and Hidden Markov Models (HMMs). The SVMs and HMMs account for local and global residue preferences, respectively. Based on a 10-fold cross-validation test, BOCTOPUS performs better than all existing methods, reaching a Q3 accuracy of 87%. Further, BOCTOPUS predicted the correct number of strands for 83% proteins in the dataset. BOCTOPUS might also help in reliable identification of TMBs by using it as an additional filter to methods specialized in this task. AVAILABILITY: BOCTOPUS is freely available as a web server at: http://boctopus.cbr.su.se/. The datasets used for training and evaluations are also available from this site.
MOTIVATION: Transmembrane β barrel proteins (TMBs) are found in the outer membrane of Gram-negative bacteria, chloroplast and mitochondria. They play a major role in the translocation machinery, pore formation, membrane anchoring and ion exchange. TMBs are also promising targets for antimicrobial drugs and vaccines. Given the difficulty in membrane protein structure determination, computational methods to identify TMBs and predict the topology of TMBs are important. RESULTS: Here, we present BOCTOPUS; an improved method for the topology prediction of TMBs by employing a combination of support vector machines (SVMs) and Hidden Markov Models (HMMs). The SVMs and HMMs account for local and global residue preferences, respectively. Based on a 10-fold cross-validation test, BOCTOPUS performs better than all existing methods, reaching a Q3 accuracy of 87%. Further, BOCTOPUS predicted the correct number of strands for 83% proteins in the dataset. BOCTOPUS might also help in reliable identification of TMBs by using it as an additional filter to methods specialized in this task. AVAILABILITY: BOCTOPUS is freely available as a web server at: http://boctopus.cbr.su.se/. The datasets used for training and evaluations are also available from this site.
Authors: Rhonda R Caston; John T Loh; Bradley J Voss; W Hayes McDonald; Matthew B Scholz; Mark S McClain; Timothy L Cover Journal: J Proteomics Date: 2019-05-04 Impact factor: 4.044