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, 29 (4), 504-5

BETAWARE: A Machine-Learning Tool to Detect and Predict Transmembrane Beta-Barrel Proteins in Prokaryotes

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BETAWARE: A Machine-Learning Tool to Detect and Predict Transmembrane Beta-Barrel Proteins in Prokaryotes

Castrense Savojardo et al. Bioinformatics.

Abstract

Summary: The annotation of membrane proteins in proteomes is an important problem of Computational Biology, especially after the development of high-throughput techniques that allow fast and efficient genome sequencing. Among membrane proteins, transmembrane β-barrels (TMBBs) are poorly represented in the database of protein structures (PDB) and difficult to identify with experimental approaches. They are, however, extremely important, playing key roles in several cell functions and bacterial pathogenicity. TMBBs are included in the lipid bilayer with a β-barrel structure and are presently found in the outer membranes of Gram-negative bacteria, mitochondria and chloroplasts. Recently, we developed two top-performing methods based on machine-learning approaches to tackle both the detection of TMBBs in sets of proteins and the prediction of their topology. Here, we present our BETAWARE program that includes both approaches and can run as a standalone program on a linux-based computer to easily address in-home massive protein annotation or filtering.

Availability and implementation: http://www.biocomp.unibo.it/∼savojard/betawarecl .

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