Accurate prediction of bacterial type IV secreted effectors using amino acid composition and PSSM profiles.
BACKGROUND
Various human pathogens secret effector proteins into hosts cells via the type IV secretion system (T4SS). These proteins play important roles in the interaction between bacteria and hosts. Computational methods for T4SS effector prediction have been developed for screening experimental targets in several isolated bacterial species; however, widely applicable prediction approaches are still unavailable


RESULTS
In this work, four types of distinctive features, namely, amino acid composition, dipeptide composition, .position-specific scoring matrix composition and auto covariance transformation of position-specific scoring matrix, were calculated from primary sequences. A classifier, T4EffPred, was developed using the support vector machine with these features and their different combinations for effector prediction. Various theoretical tests were performed in a newly established dataset, and the results were measured with four indexes. We demonstrated that T4EffPred can discriminate IVA and IVB effectors in benchmark datasets with positive rates of 76.7% and 89.7%, respectively. The overall accuracy of 95.9% shows that the present method is accurate for distinguishing the T4SS effector in unidentified sequences. A classifier ensemble was designed to synthesize all single classifiers. Notable performance improvement was observed using this ensemble system in benchmark tests. To demonstrate the model's application, a genome-scale prediction of effectors was performed in Bartonella henselae, an important zoonotic pathogen. A number of putative candidates were distinguished.


BACKGROUND
A web server implementing the prediction method and the source code are both available at http://bioinfo.tmmu.edu.cn/T4EffPred.
Department of Microbiology, College of Basic Medical Sciences, Third Military Medical University (TMMU), Chongqing 40038, China and Department of Tuberculosis, Institute of Infectious TB Prevention, Third Hospital of PLA, Baoji, Shanxi 721006, China.
Lingyun
L
Zou
Chonghan
C
Nan
Fuquan
F
Hu
D000596:Amino Acids / Q000737:chemistry*; D001426:Bacterial Proteins / Q000145:classification / Q000235:genetics / Q000378:metabolism*; D058947:Bacterial Secretion Systems; D001474:Bartonella Infections / Q000235:genetics / Q000382:microbiology; D018416:Bartonella henselae / Q000472:pathogenicity; D019295:Computational Biology; D015964:Gene Expression Regulation, Bacterial; D016680:Genome, Bacterial; D056510:Position-Specific Scoring Matrices; D060388:Support Vector Machine; D037521:Virulence Factors / Q000378:metabolism
D000596:Amino Acids; D001426:Bacterial Proteins; D058947:Bacterial Secretion Systems; D037521:Virulence Factors