Computational Prediction of Type III and IV Secreted Effectors in Gram-Negative Bacteria

Abstract

In this review, we provide an overview of the methods employed in four recent studies that described novel methods for computational prediction of secreted effectors from type III and IV secretion systems in Gramnegative bacteria. We present the results of these 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 SIEVE server (http://www.sysbep.org/sieve). Finally, we assess the accuracies 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 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.

Expression and Targeting of Secreted Proteins from Chlamydia trachomatis

“…Type III secretion signals occur in the N terminus of the protein but lack primary sequence similarity (49). Computational predictions of type III secreted effectors indicate that the signal sequence is located within the Nterminal 30 amino acids and show compositional bias (50)(51)(52). Similarly, chlamydial Incs lack primary amino acid sequence similarity even in the putative N-terminal signal sequences and the characteristic bilobed hydrophobic domains (23,24).…”

“…In addition, there are now several in silico predictive tools for the identification of putative secreted effector proteins (23,51,52,78). The pBOMB4 vector permits expression of chlamydial proteins under the control of either constitutive or inducible promoters and allows the detection of tagged bacterial proteins by immunofluorescence, immunoblotting, or biochemically.…”

Type III secretion systems: the bacterial flagellum and the injectisome

“…In contrast to other export pathways like Sec or Tat, the T3SS export signals are remarkably vaguely defined. While an uncleaved and generally unstructured N-terminal sequence seems to be common for all T3SS substrates, the length of the minimal signal greatly varies, additional signals seem to be required for some classes of substrates and, despite increasingly sophisticated algorithms [162][163][164][165][166], it is still difficult to precisely predict T3SS substrates.…”

Protein Flexibility and Coiled-Coil Propensity: New Insights Into Type III and Other Bacterial Secretion Systems

“…The basic concepts of this tool are described by Lower & Schneider (2009). The 'SIEVE' Server (http://www.sysbep.org/sieve/) for the prediction of type III secreted effectors was originally described by Samudrala et al, (2009) and recently reviewed by McDermott et al, (2011). Potential T3SS effectors are scored using a computational model developed via Machine-Learning Methodologies.…”