title: Prediction of membrane transport proteins and their substrate specificities using primary sequence information.
abstract: BACKGROUND
  Membrane transport proteins (transporters) move hydrophilic substrates across hydrophobic membranes and play vital roles in most cellular functions. Transporters represent a diverse group of proteins that differ in topology, energy coupling mechanism, and substrate specificity as well as sequence similarity. Among the functional annotations of transporters, information about their transporting substrates is especially important. The experimental identification and characterization of transporters is currently costly and time-consuming. The development of robust bioinformatics-based methods for the prediction of membrane transport proteins and their substrate specificities is therefore an important and urgent task.
  
  
  RESULTS
  Support vector machine (SVM)-based computational models, which comprehensively utilize integrative protein sequence features such as amino acid composition, dipeptide composition, physico-chemical composition, biochemical composition, and position-specific scoring matrices (PSSM), were developed to predict the substrate specificity of seven transporter classes: amino acid, anion, cation, electron, protein/mRNA, sugar, and other transporters. An additional model to differentiate transporters from non-transporters was also developed. Among the developed models, the biochemical composition and PSSM hybrid model outperformed other models and achieved an overall average prediction accuracy of 76.69% with a Mathews correlation coefficient (MCC) of 0.49 and a receiver operating characteristic area under the curve (AUC) of 0.833 on our main dataset. This model also achieved an overall average prediction accuracy of 78.88% and MCC of 0.41 on an independent dataset.
  
  
  CONCLUSIONS
  Our analyses suggest that evolutionary information (i.e., the PSSM) and the AAIndex are key features for the substrate specificity prediction of transport proteins. In comparison, similarity-based methods such as BLAST, PSI-BLAST, and hidden Markov models do not provide accurate predictions for the substrate specificity of membrane transport proteins. TrSSP: The Transporter Substrate Specificity Prediction Server, a web server that implements the SVM models developed in this paper, is freely available at http://bioinfo.noble.org/TrSSP.
authors: Plant Biology Division, The Samuel Roberts Noble Foundation, Ardmore, Oklahoma, United States of America.
authors: Nitish K
authors: NK
authors: Mishra
authors: Plant Biology Division, The Samuel Roberts Noble Foundation, Ardmore, Oklahoma, United States of America.
authors: Junil
authors: J
authors: Chang
authors: Plant Biology Division, The Samuel Roberts Noble Foundation, Ardmore, Oklahoma, United States of America.
authors: Patrick X
authors: PX
authors: Zhao
mesh_terms: D000595:Amino Acid Sequence; D000818:Animals; D055598:Chemical Phenomena; D019295:Computational Biology / Q000379:methods*; D030562:Databases, Protein; D019143:Evolution, Molecular; D006801:Humans; D008390:Markov Chains; D026901:Membrane Transport Proteins / Q000737:chemistry* / Q000378:metabolism*; D015203:Reproducibility of Results; D013379:Substrate Specificity; D060388:Support Vector Machine
chemical_list: D026901:Membrane Transport Proteins