Computational prediction of type III secreted proteins from gram-negative bacteria

Abstract

BackgroundType III secretion system (T3SS) is a specialized protein delivery system in gram-negative bacteria that injects proteins (called effectors) directly into the eukaryotic host cytosol and facilitates bacterial infection. For many plant and animal pathogens, T3SS is indispensable for disease development. Recently, T3SS has also been found in rhizobia and plays a crucial role in the nodulation process. Although a great deal of efforts have been done to understand type III secretion, the precise mechanism underlying the secretion and translocation process has not been fully understood. In particular, defined secretion and translocation signals enabling the secretion have not been identified from the type III secreted effectors (T3SEs), which makes the identification of these important virulence factors notoriously challenging. The availability of a large number of sequenced genomes for plant and animal-associated bacteria demands the development of efficient and effective prediction methods for the identification of T3SEs using bioinformatics approaches.ResultsWe have developed a machine learning method based on the N-terminal amino acid sequences to predict novel type III effectors in the plant pathogen Pseudomonas syringae and the microsymbiont rhizobia. The extracted features used in the learning model (or classifier) include amino acid composition, secondary structure and solvent accessibility information. The method achieved a precision of over 90% on P. syringae in a cross validation study. In combination with a promoter screen for the type III specific promoters, this classifier trained on the P. syringae data was applied to predict novel T3SEs from the genomic sequences of four rhizobial strains. This application resulted in 57 candidate type III secreted proteins, 17 of which are confirmed effectors.ConclusionOur experimental results demonstrate that the machine learning method based on N-terminal amino acid sequences combined with a promoter screen could prove to be a very effective computational approach for predicting novel type III effectors in gram-negative bacteria. Our method and data are available to the public upon request.

SrfJ, a Salmonella Type III Secretion System Effector Regulated by PhoP, RcsB, and IolR

“…As a consequence, computational methods used to detect secretion signals are only partially accurate (2,36,50,60,64). Our experiments with SrfJ fragments fused to CyaA showed that the minimal signal sequence necessary for translocation is contained in the N-terminal 20 amino acids of this protein.…”

A Putative Type III Secretion System Effector Encoded by the MA20_12780 Gene in Bradyrhizobium japonicum Is-34 Causes Incompatibility with Rj 4 Genotype Soybeans

“…1b). Yang et al (31) reported that the Bll8244 protein in B. diazoefficiens USDA 110 is a T3SS effector. Around the MA20_12780 gene, a transposase (MA20_12740), the IS1631 terminal repeat, and hypothetical genes were identified (Fig.…”

Impact of the N-Terminal Secretor Domain on YopD Translocator Function in Yersinia pseudotuberculosis Type III Secretion

“…We believe that a more likely scenario is that the physical and/or chemical characteristics of N-terminal residues in YopD FrameϪ1 fortuitously combine to enable its continued secretion. Of significance is the recent prediction that the overall amphipathicity of the N-terminal secretion signal, with an enrichment of serine, threonine, and proline and, possibly, even short stretches of hydrophobic residues, is a characteristic of a working T3S signal (6,48,64,84). Interestingly, YopD FrameϪ1 still harbors an N terminus interspersed with hydrophobic residues and a relatively high proportion (5 of 15) of serine, threonine, and/or proline residues, a pattern also observed for other secretion-competent YopD N-terminal sequences (see Table 2).…”

“…A chaperoneindependent secretion signal also exists at the extreme N terminus, represented by a complex combination of the mRNA with the protein sequence (16,46,69). While no sequence consensus is visually obvious, there is some evidence of an amphipathic property (47), and various computational approaches based on sophisticated machine-learning methodology can predict T3S substrates on the basis of a conserved secretion signal (6,48,64,84). Nevertheless, the molecular contribution these extensively mapped chaperone-independent signals make to substrate secretion is not yet understood.…”