High-accuracy prediction of bacterial type III secreted effectors based on position-specific amino acid composition profiles

Motivation: Bacterial type III secreted (T3S) effectors are delivered into host cells specifically via type III secretion systems (T3SSs), which play important roles in the interaction between bacteria and their hosts. Previous computational methods for T3S protein prediction have only achieved limi...

Celý popis

Uložené v:

Podrobná bibliografia
Vydané v:	Bioinformatics Ročník 27; číslo 6; s. 777 - 784
Hlavní autori:	Wang, Yejun, Zhang, Qing, Sun, Ming-an, Guo, Dianjing
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	Oxford Oxford University Press 15.03.2011
Predmet:	Algorithms Amino Acid Sequence Amino acids Bacteria Bacterial Proteins - chemistry Bacterial Proteins - classification Bacterial Secretion Systems Bayes Theorem Bioinformatics Biological and medical sciences Composition effects Computational Biology - methods Effectors Fundamental and applied biological sciences. Psychology General aspects Genome, Bacterial Mathematical models Mathematics in biology. Statistical analysis. Models. Metrology. Data processing in biology (general aspects) Models, Statistical Proteins Ralstonia solanacearum Ralstonia solanacearum - chemistry Secretions Sequence Analysis, Protein - methods Software Support vector machines Composition Accuracy Position Secretion Aminoacid Prediction Bacteria Profile
ISSN:	1367-4803, 1367-4811, 1367-4811, 1460-2059
On-line prístup:	Získať plný text
Tagy:	Pridať tag Žiadne tagy, Buďte prvý, kto otaguje tento záznam!

Popis
Shrnutí:	Motivation: Bacterial type III secreted (T3S) effectors are delivered into host cells specifically via type III secretion systems (T3SSs), which play important roles in the interaction between bacteria and their hosts. Previous computational methods for T3S protein prediction have only achieved limited accuracy, and distinct features for effective T3S protein prediction remain to be identified. Results: In this work, a distinctive N-terminal position-specific amino acid composition (Aac) feature was identified for T3S proteins. A large portion (∼50%) of T3S proteins exhibit distinct position-specific Aac features that can tolerate position shift. A classifier, BPBAac, was developed and trained using Support Vector Machine (SVM) based on the Aac feature extracted using a Bi-profile Bayes model. We demonstrated that the BPBAac model outperformed other implementations in classification of T3S and non-T3S proteins, giving an average sensitivity of ∼90.97% and an average selectivity of ∼97.42% in a 5-fold cross-validation evaluation. The model was also robust when a small-size training dataset was used. The fact that the position-specific Aac feature is commonly found in T3S proteins across different bacterial species gives this model wide application. To demonstrate the model's application, a genome-wide prediction of T3S effector proteins was performed for Ralstonia solanacearum, an important plant pathogenic bacterium, and a number of putative candidates were identified using this model. Availability: An R package of BPBAac tool is freely downloadable from: http://biocomputer.bio.cuhk.edu.hk/softwares/BPBAac. Contact: djguo@cuhk.edu.hk Supplementary information: Supplementary data are available at Bioinformatics online.
Bibliografia:	ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 23
ISSN:	1367-4803 1367-4811 1367-4811 1460-2059
DOI:	10.1093/bioinformatics/btr021