Prediction of New Bacterial Type III Secreted Effectors with a Recursive Hidden Markov Model Profile-Alignment Strategy

(E-pub Ahead of Print)

Author(s): Zhirong Guo, Xi Cheng, Xinjie Hui, Xingsheng Shu, Aaron P. White, Yueming Hu, Yejun Wang*.

Journal Name: Current Bioinformatics

Volume 12 , 2017


To identify new bacterial type III secreted effectors is computationally a big challenge. At least a dozen machine learning algorithms have been developed, but so far have only achieved limited success. Sequence similarity appears important for biologists but is frequently neglected by algorithm developers for effector prediction, although large success was achieved in the field with this strategy a decade ago. In this study, we propose a recursive sequence alignment strategy with Hidden Markov Models, to comprehensively find homologs of known YopJ/P full-length proteins, effector domains and N-terminal signal sequences. Using this method, we identified 155 different YopJ/P-family effectors and 59 proteins with YopJ/P N-terminal signal sequences from 27 genera and more than 70 species. Among these genera, we also identified one type III secretion system (T3SS) from Uliginosibacterium and two T3SSs from Rhizobacter for the first time. Higher conservation of effector domains, N-terminal fusion of signal sequences to other effectors, and the exchange of N-terminal signal sequences between different effector proteins were frequently observed for YopJ/P-family proteins. This made it feasible to identify new effectors based on separate similarity screening for the N-terminal signal peptides and the effector domains of known effectors. This method can also be applied to search for homologues of other known T3SS effectors.

Keywords: type III secreted effectors; Hidden Markov Model; YopJ/P; machine learning; effector proteins; sequence similarity

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Year: 2017
(E-pub Ahead of Print)
DOI: 10.2174/1574893612666170725122633
Price: $95

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