PRIMSIPLR: prediction of inner-membrane situated pore-lining residues for alpha-helical transmembrane proteins.
Transmembrane proteins such as transporters and channels mediate the passage of inorganic and organic substances across biological membranes through their central pore. Pore-lining residues (PLRs) that make direct contacts to the substrates have a crucial impact on the function of the protein and, hence, their identification is a key step in mechanistic studies. Here, we established a nonredundant data set containing the three-dimensional (3D) structures of 90 α-helical transmembrane proteins and annotated the PLRs of these proteins by a pore identification software. A support vector machine was then trained to distinguish PLRs from other residues based on the protein sequence alone. Using sixfold cross-validation, our best performing predictor gave a Matthews's correlation coefficient of 0.41 with an accuracy of 0.86, sensitivity of 0.61, and specificity of 0.89, respectively. We provide a novel software tool that will aid biomedical scientists working on transmembrane proteins with unknown 3D structures. Both standalone version and web service are freely available from the URL http://service.bioinformatik.uni-saarland.de/PRIMSIPLR/.
Center for Bioinformatics, Saarland University, D-66041, Saarbrücken, Germany.
Duy
D
Nguyen
Volkhard
V
Helms
Po-Hsien
PH
Lee
D000595:Amino Acid Sequence; D019295:Computational Biology / Q000379:methods*; D030562:Databases, Protein; D008565:Membrane Proteins / Q000737:chemistry*; D017433:Protein Structure, Secondary; D015203:Reproducibility of Results; D012984:Software; D060388:Support Vector Machine
D008565:Membrane Proteins
amino acid composition; evolutionary conservation; imbalanced data; pore identification; support vector machine; transmembrane protein