Antibacterial T6SS effectors with a VRR-Nuc domain are structure-specific nucleases.

The type VI secretion system (T6SS) secretes antibacterial effectors into target competitors. Salmonella spp. encode five phylogenetically distinct T6SSs. Here, we characterize the function of the SPI-22 T6SS of Salmonella bongori showing that it has antibacterial activity and identify a group of antibacterial T6SS effectors (TseV1-4) containing an N-terminal PAAR-like domain and a C-terminal VRR-Nuc domain encoded next to cognate immunity proteins with a DUF3396 domain (TsiV1-4). TseV2 and TseV3 are toxic when expressed in Escherichia coli and bacterial competition assays confirm that TseV2 and TseV3 are secreted by the SPI-22 T6SS. Phylogenetic analysis reveals that TseV1-4 are evolutionarily related to enzymes involved in DNA repair. TseV3 recognizes specific DNA structures and preferentially cleave splayed arms, generating DNA double-strand breaks and inducing the SOS response in target cells. The crystal structure of the TseV3:TsiV3 complex reveals that the immunity protein likely blocks the effector interaction with the DNA substrate. These results expand our knowledge on the function of Salmonella pathogenicity islands, the evolution of toxins used in biological conflicts, and the endogenous mechanisms regulating the activity of these toxins.

DeepCDpred: Inter-residue distance and contact prediction for improved prediction of protein structure.

Rapid, accurate prediction of protein structure from amino acid sequence would accelerate fields as diverse as drug discovery, synthetic biology and disease diagnosis. Massively improved prediction of protein structures has been driven by improving the prediction of the amino acid residues that contact in their 3D structure. For an average globular protein, around 92% of all residue pairs are non-contacting, therefore accurate prediction of only a small percentage of inter-amino acid distances could increase the number of constraints to guide structure determination. We have trained deep neural networks to predict inter-residue contacts and distances. Distances are predicted with an accuracy better than most contact prediction techniques. Addition of distance constraints improved de novo structure predictions for test sets of 158 protein structures, as compared to using the best contact prediction methods alone. Importantly, usage of distance predictions allows the selection of better models from the structure pool without a need for an external model assessment tool. The results also indicate how the accuracy of distance prediction methods might be improved further.