Identification of type VI secretion system effector-immunity pairs using structural bioinformatics

The type VI secretion system (T6SS) is an important mediator of microbe-microbe and microbe-host interactions. Gram-negative bacteria use the T6SS to inject T6SS effectors (T6Es), which are usually proteins with toxic activity, into neighboring cells. Antibacterial effectors have cognate immunity proteins that neutralize self-intoxication. Here, we applied novel structural bioinformatic tools to perform systematic discovery and functional annotation of T6Es and their cognate immunity proteins from a dataset of 17,920 T6SS-encoding bacterial genomes. Using structural clustering, we identified 517 putative T6E families, outperforming sequence-based clustering. We developed a logistic regression model to reliably quantify protein-protein interaction of new T6E-immunity pairs, yielding candidate immunity proteins for 231 out of the 517 T6E families. We used sensitive structure-based annotation which yielded functional annotations for 51% of the T6E families, again outperforming sequence-based annotation. Next, we validated four novel T6E-immunity pairs using basic experiments in E. coli. In particular, we showed that the Pfam domain DUF3289 is a homolog of Colicin M and that DUF943 acts as its cognate immunity protein. Furthermore, we discovered a novel T6E that is a structural homolog of SleB, a lytic transglycosylase, and identified a specific glutamate that acts as its putative catalytic residue. Overall, this study applies novel structural bioinformatic tools to T6E-immunity pair discovery, and provides an extensive database of annotated T6E-immunity pairs. Structural bioinformatic tools were utilized for the discovery of novel specialized Type VI Secretion System (T6SS) effectors and their cognate immunity proteins, highlighting their utility over standard sequence-based tools. The effector predictions were supported by experimental results.Structural clustering provided better compression of effectors than sequence-based methods, with 517 structural clusters representing the structure space of specialized effectors in Proteobacteria. The ipTM score from Alphafold-multimer was used as a reliable and quantitative measure for predicting candidate immunity proteins in 231 out of 517 effector clusters. Annotations were provided for 265 out of the 517 specialized effector domain families using fast and sensitive searches with Foldseek, expanding capabilities beyond Pfam-based annotation alone. Four putative effectors were demonstrated to be toxic to , with co-expression of cognate immunity proteins neutralizing their toxicity. Structural bioinformatic tools were utilized for the discovery of novel specialized Type VI Secretion System (T6SS) effectors and their cognate immunity proteins, highlighting their utility over standard sequence-based tools. The effector predictions were supported by experimental results.