A method for discovering transmembrane beta-barrel proteins in Gram-negative bacterial proteomes.
Transmembrane beta-barrel (TMB) proteins play pivotal roles in many aspects of bacterial functions. This paper presents a k-nearest neighbor (K-NN) method for discriminating TMB and non-TMB proteins. We start with a method that makes predictions based on a distance computed from residue composition and gradually improve the prediction performance by including homologous sequences and searching for a set of residues and di-peptides for calculating the distance. The final method achieves an accuracy of 97.1%, with 0.876 MCC, 86.4% sensitivity and 98.8% specificity. A web server based on the proposed method is available at http://yanbioinformatics.cs.usu.edu:8080/TMBKNNsubmit.
Hu
Jing
J
Department of Computer Science, Utah State University, Logan, UT 84322, USA.
Yan
Changhui
C
D000465:Algorithms; D000595:Amino Acid Sequence; D001426:Bacterial Proteins / Q000032:analysis* / Q000737:chemistry* / Q000235:genetics / Q000378:metabolism; D019295:Computational Biology / Q000379:methods*; D006090:Gram-Negative Bacteria / Q000737:chemistry*; D026901:Membrane Transport Proteins / Q000032:analysis* / Q000737:chemistry* / Q000235:genetics / Q000378:metabolism; D010455:Peptides / Q000737:chemistry / Q000235:genetics / Q000378:metabolism; D011237:Predictive Value of Tests; D020543:Proteome / Q000032:analysis* / Q000737:chemistry; D017385:Sequence Homology
D001426:Bacterial Proteins; D026901:Membrane Transport Proteins; D010455:Peptides; D020543:Proteome