Computational prediction of type III and IV secreted effectors in Gram-negative bacteria
In this review, we provide an overview of the methods employed by four recent papers that described novel methods for computational prediction of secreted effectors from type III and IV secretion systems in Gram-negative bacteria. The results of the studies in terms of performance at accurately predicting secreted effectors and similarities found between secretion signals that may reflect biologically relevant features for recognition. We discuss the web-based tools for secreted effector prediction described in these studies and announce the availability of our tool, the SIEVEserver (http://www.biopilot.org). Finally, we assess the accuracy of the three type III effector prediction methods on a small set of proteins not known prior to the development of these tools that we have recently discovered and validated using both experimental and computational approaches. Our comparison shows that all methods use similar approaches and, in general arrive at similar conclusions. We discuss the possibility of an order-dependent motif in the secretion signal, which was a point of disagreement in the studies. Our results show that there may be classes of effectors in which the signal has a loosely defined motif, and others in which secretion is dependent only on compositional biases. Computational prediction of secreted effectors from protein sequences represents an important step toward better understanding the interaction between pathogens and hosts.
- Research Organization:
- Pacific Northwest National Lab. (PNNL), Richland, WA (United States)
- Sponsoring Organization:
- USDOE
- DOE Contract Number:
- AC05-76RL01830
- OSTI ID:
- 1009726
- Report Number(s):
- PNNL-SA-72872; INFIBR; 400412000; TRN: US201107%%835
- Journal Information:
- Infection and Immunity, 79(1):23-32, Vol. 79, Issue 1; ISSN 0019-9567
- Country of Publication:
- United States
- Language:
- English
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