The molecular architecture of Lactobacillus S-layer : Assembly and attachment to teichoic acids

S-layers are crystalline arrays found on bacterial and archaeal cells. Lactobacillus is a diverse family of bacteria known especially for potential gut health benefits. This study focuses on the S-layer proteins from Lactobacillus acidophilus and Lactobacillus amylovorus common in the mammalian gut. Atomic resolution structures of Lactobacillus S-layer proteins SlpA and SlpX exhibit domain swapping, and the obtained assembly model of the main S-layer protein SlpA aligns well with prior electron microscopy and mutagenesis data. The S-layer’s pore size suggests a protective role, with charged areas aiding adhesion. A highly similar domain organization and interaction network are observed across the Lactobacillus genus. Interaction studies revealed conserved binding areas specific for attachment to teichoic acids. The structure of the SlpA S-layer and the suggested incorporation of SlpX as well as its interaction with teichoic acids lay the foundation for deciphering its role in immune responses and for developing effective treatments for a variety of infectious and bacteria-mediated inflammation processes, opening opportunities for targeted engineering of the S-layer or lactobacilli bacteria in general.Peer reviewe

Vibrio cholerae’s ToxRS bile sensing system

The seventh pandemic of the diarrheal cholera disease, which began in 1960, is caused by the Gram-negative bacterium Vibrio cholerae. Its environmental persistence provoking recurring sudden outbreaks is enabled by V. cholerae’s rapid adaption to changing environments involving sensory proteins like ToxR and ToxS. Located at the inner membrane, ToxR and ToxS react to environmental stimuli like bile acid, thereby inducing survival strategies for example bile resistance and virulence regulation. The presented crystal structure of the sensory domains of ToxR and ToxS in combination with multiple bile acid interaction studies, reveals that a bile binding pocket of ToxS is only properly folded upon binding to ToxR. Our data proposes an interdependent functionality between ToxR transcriptional activity and ToxS sensory function. These findings support the previously suggested link between ToxRS and VtrAC-like co-component systems. Besides VtrAC, ToxRS is now the only experimentally determined structure within this recently defined superfamily, further emphasizing its significance. In-depth analysis of the ToxRS complex reveals its remarkable conservation across various Vibrio species, underlining the significance of conserved residues in the ToxS barrel and the more diverse ToxR sensory domain. Unravelling the intricate mechanisms governing ToxRS’s environmental sensing capabilities, provides a promising tool for disruption of this vital interaction, ultimately inhibiting Vibrio’s survival and virulence. Our findings hold far-reaching implications for all Vibrio strains that rely on the ToxRS system as a shared sensory cornerstone for adapting to their surroundings

Accurate prediction of protein structures and interactions using a three-track neural network

DeepMind presented notably accurate predictions at the recent 14th Critical Assessment of Structure Prediction (CASP14) conference. We explored network architectures that incorporate related ideas and obtained the best performance with a three-track network in which information at the one-dimensional (1D) sequence level, the 2D distance map level, and the 3D coordinate level is successively transformed and integrated. The three-track network produces structure predictions with accuracies approaching those of DeepMind in CASP14, enables the rapid solution of challenging x-ray crystallography and cryo–electron microscopy structure modeling problems, and provides insights into the functions of proteins of currently unknown structure. The network also enables rapid generation of accurate protein-protein complex models from sequence information alone, short-circuiting traditional approaches that require modeling of individual subunits followed by docking. We make the method available to the scientific community to speed biological research

Accurate prediction of protein structures and interactions using a three-track neural network.

DeepMind presented notably accurate predictions at the recent 14th Critical Assessment of Structure Prediction (CASP14) conference. We explored network architectures that incorporate related ideas and obtained the best performance with a three-track network in which information at the one-dimensional (1D) sequence level, the 2D distance map level, and the 3D coordinate level is successively transformed and integrated. The three-track network produces structure predictions with accuracies approaching those of DeepMind in CASP14, enables the rapid solution of challenging x-ray crystallography and cryo-electron microscopy structure modeling problems, and provides insights into the functions of proteins of currently unknown structure. The network also enables rapid generation of accurate protein-protein complex models from sequence information alone, short-circuiting traditional approaches that require modeling of individual subunits followed by docking. We make the method available to the scientific community to speed biological research