Skip to content
#protein folding Open access

Molecular characterization of the orphan toxin-immunity pair Rhs2-SciX from Salmonella Typhimurium.

Sep 2026 · Journal of Molecular Biology · pp. 170032 · 0 citations · 83 references
Medicine

TL;DR

This work probes the structure and function of the orphan toxin-immunity pair Rhs2-SciX, demonstrates the biochemical diversity of Salmonella T6SS effectors, and highlights the conserved T6SS toxicity strategy of binding EF-Tu.

Abstract

The type VI secretion system (T6SS) is a dynamic nanomachine used by bacteria to compete for space and nutrients. To kill rival bacteria, the T6SS secretes toxic effector proteins directly into adjacent cells in a contact-dependent manner. Effectors have diverse biochemical functions that arise from subtle structural modifications to related enzyme folds. To protect from self-intoxication, effectors are encoded with a cognate immunity protein in effector-immunity pairs. Immunity proteins inhibit toxicity by directly binding the active site of the toxin and are as structurally diverse as their effectors. Salmonella Typhimurium has several known effectors of diverse biochemical functions, including an orphan toxin-immunity pair Rhs2-SciX. Although Rhs2 has antibacterial activity, the biochemical mechanism of Rhs2 toxicity remains unknown. Here, we take a structural approach and show that Rhs2 is related to BECR family nucleases through modeling and bacterial toxicity assays. Furthermore, we solve an X-ray crystal structure of SciX and study the solution-state properties of the immunity protein to gain insight into the binding and inhibition mechanism of Rhs2. Finally, we discover that Rhs2 binds directly to EF-Tu suggesting that Rhs2 may inhibit translation to cause cell death. Our work probes the structure and function of the orphan toxin-immunity pair Rhs2-SciX, demonstrates the biochemical diversity of Salmonella T6SS effectors, and highlights the conserved T6SS toxicity strategy of binding EF-Tu.

Read PDF

Similar papers

#computer vision Review Open access May 2015

A survey study on major technical barriers affecting the decision to adopt cloud services

The comparison of adopter and non-adopter sample reveals three potential adoption inhibitor, security, data privacy, and portability, which underlines the importance of the technical and security perspectives for research investigating the adoption of technology.

Nattakarn Phaphoom, Xiaofeng Wang, S. Samuel et al. · 111 citations · ⚡8
#computer vision Open access Feb 2018

Lean Internal Startups for Software Product Innovation in Large Companies: Enablers and Inhibitors

This study investigates how Lean internal startup facilitates software product innovation in large companies and identifies its enablers and inhibitors, and shows the potential of the method-in-action framework to investigate the Lean startup approach in non-startup context.

Henry Edison, Nina M. Smørsgård, Xiaofeng Wang et al. · 78 citations · ⚡6
#computer vision Book Open access Jul 2015

Understanding the affect of developers: theoretical background and guidelines for psychoempirical software engineering

This paper highlights the challenges to conduct proper affect-related studies with psychology, provides a comprehensive literature review in affect theory, and proposes guidelines for conducting psychoempirical software engineering.

D. Graziotin, Xiaofeng Wang, P. Abrahamsson · 56 citations · ⚡4
#machine learning Open access May 2017

What Influences the Speed of Prototyping? An Empirical Investigation of Twenty Software Startups

This study conducts a multiple case study on twenty European software startups and proposes a prototype-centric learning model in early stage software startups, and identifies factors that occur as barriers but also facilitators for prototyping in earlystage software startups.

Anh Nguyen-Duc, Xiaofeng Wang, P. Abrahamsson · 44 citations · ⚡5
#protein folding Open access Sep 2026

Programmable design of functional proteins from natural language

Pinal, a 16-billion-parameter foundation model that produces protein candidates from natural-language functional descriptions, supports natural language as a high-level interface for candidate generation in protein design, enabling programmable exploration with reduced reliance on manually specified structural or seque...

Fengyuan Dai, Shiyang You, Yudian Zhu et al. · 31 citations · ⚡3

Related blog posts

Google DeepMind Blog Sep 30, 2026

Introducing SynthID Bio

Proof of concept for watermarking AI-generated proteins while preserving biological function.

MIT News · Artificial Intelligence Aug 27, 2026

Looking beyond natural sequences

A new machine-learning framework aims to improve the success rate of computational protein design while moving away from results that reproduce sequences found in nature.

We use cookies to run the site and, with your consent, for analytics and to show ads. See our Cookie Policy.