Support Vector Machine-based method for predicting subcellular localization of mycobacterial proteins using evolutionary information and motifs.
BACKGROUND
In past number of methods have been developed for predicting subcellular location of eukaryotic, prokaryotic (Gram-negative and Gram-positive bacteria) and human proteins but no method has been developed for mycobacterial proteins which may represent repertoire of potent immunogens of this dreaded pathogen. In this study, attempt has been made to develop method for predicting subcellular location of mycobacterial proteins.


RESULTS
The models were trained and tested on 852 mycobacterial proteins and evaluated using five-fold cross-validation technique. First SVM (Support Vector Machine) model was developed using amino acid composition and overall accuracy of 82.51% was achieved with average accuracy (mean of class-wise accuracy) of 68.47%. In order to utilize evolutionary information, a SVM model was developed using PSSM (Position-Specific Scoring Matrix) profiles obtained from PSI-BLAST (Position-Specific Iterated BLAST) and overall accuracy achieved was of 86.62% with average accuracy of 73.71%. In addition, HMM (Hidden Markov Model), MEME/MAST (Multiple Em for Motif Elicitation/Motif Alignment and Search Tool) and hybrid model that combined two or more models were also developed. We achieved maximum overall accuracy of 86.8% with average accuracy of 89.00% using combination of PSSM based SVM model and MEME/MAST. Performance of our method was compared with that of the existing methods developed for predicting subcellular locations of Gram-positive bacterial proteins.


CONCLUSIONS
A highly accurate method has been developed for predicting subcellular location of mycobacterial proteins. This method also predicts very important class of proteins that is membrane-attached proteins. This method will be useful in annotating newly sequenced or hypothetical mycobacterial proteins. Based on above study, a freely accessible web server TBpred http://www.imtech.res.in/raghava/tbpred/ has been developed.
Rashid
Mamoon
M
Bioinformatics Centre, Institute of Microbial Technology, Sector-39A, Chandigarh, India. mamoon@imtech.res.in
Saha
Sudipto
S
Raghava
Gajendra Ps
GP
D020816:Amino Acid Motifs; D000595:Amino Acid Sequence; D001185:Artificial Intelligence; D001426:Bacterial Proteins / Q000737:chemistry / Q000235:genetics* / Q000378:metabolism*; D030562:Databases, Protein; D008969:Molecular Sequence Data; D009169:Mycobacterium tuberculosis / Q000235:genetics / Q000378:metabolism*; D010363:Pattern Recognition, Automated / Q000379:methods*; D016415:Sequence Alignment / Q000379:methods; D020539:Sequence Analysis, Protein / Q000379:methods*; D013347:Subcellular Fractions / Q000378:metabolism*
D001426:Bacterial Proteins