Back

A survey of ADP-ribosyltransferase families in the pathogenic Legionella

Krysinska, M.; Gradowski, M.; Baranowski, B.; Pawlowski, K.; Dudkiewicz, M.

2024-07-31 bioinformatics
10.1101/2024.07.30.605764 bioRxiv
Show abstract

BackgroundADP-ribosyltransferases (ARTs) are a superfamily of enzymes implicated in various cellular processes, including pathogenic mechanisms. The Legionella genus, known for causing Legionnaires disease, possesses diverse ART-like effectors. This study explores the proteomes of 41 Legionella species to bioinformatically identify and characterise novel ART-like families, providing insights into their potential roles in pathogenesis and host interactions. MethodsWe conducted a comprehensive bioinformatic survey of 41 Legionella species to identify proteins with significant sequence or structural similarity to known ARTs. Sensitive sequence searches were performed to detect candidate ART-like families. Subsequent validation, including structure prediction of such families, was achieved using artificial intelligence-driven tools, such as AlphaFold. Comparative analyses were performed to assess sequence and structural similarities between the novel ART-like families and known ARTs. ResultsOur analysis identified 63 proteins with convincing similarity to ARTs, organised into 39 ART-like families, including 26 novel families. Key findings include: O_LIDUF2971 family: exhibits sequence similarity to DarT toxins and other DNA-acting ARTs. C_LIO_LIDUF4291 family: the largest newly identified family shows structural and sequence similarity to the diphtheria toxin, suggesting the ability to modify proteins. C_LI Most members of the novel ART families are predicted effectors. Although experimental validation of the predicted ART effector functions is necessary, the novel ART-like families identified present promising targets for understanding Legionella pathogenicity and developing therapeutic strategies. We publish a complete catalogue of our results in the astARTe database: http://bioinfo.sggw.edu.pl/astarte/.

Matching journals

The top 13 journals account for 50% of the predicted probability mass.

50% of probability mass above

"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.