Biology subjects

Sagmeister, T.

Publications and source records attributed to Sagmeister, T..

5 recordsLinked to original sources

bioRxiv2025

M. tuberculosis meets European Lead Factory - identification and structural characterization of novel Rv0183 inhibitors using X-ray crystallography

Tuberculosis, caused by Mycobacterium tuberculosis (Mtb), remains a leading cause of mortality worldwide. Proteins involved in lipid metabolism, such as the monoacylglycerol lipase Rv0183, play critical roles during both the active and dormant phases of Mtb and present novel targets for therapeutic intervention. Through high-throughput screening at the European Lead Factory, we identified a novel chemotype characterized by a hydroxypyrrolidine ring, which demonstrated potent inhibition of Rv0183 and promising results in whole cell bacterial studies. Subsequent co-crystallization studies of this chemotype with Rv0183 revealed non-covalent interactions within the lipases binding pocket, elucidating the inhibitory mechanism. Comparative analysis, augmented by AI-driven 3D-point-cloud approaches, distinguished Rv0183s ligand-binding cavity from that of human monoacylglycerol lipase, implying the possibility for species-selective inhibition. This selectivity was further supported by molecular docking simulations which validated the experimental binding affinities and predicted strong, specific binding modes. Our study presents not only the structural basis for the inhibition of Rv0183 by these novel hydroxypyrrolidine-based inhibitors but also demonstrates the utility of integrating computational and empirical methods to achieve species-specific targeting. This approach could minimize off-target effects in humans, marking a significant step toward developing more effective antitubercular therapies. The potential to selectively inhibit Mtb in its dormant state could lead to treatments that prevent the persistence and resurgence of the disease, addressing a crucial gap in the fight against tuberculosis.

biochemistry↗

bioRxiv2023

Unmasking Crucial Residues in Adipose Triglyceride Lipase (ATGL) for Co-Activation with Comparative Gene Identification-58 (CGI-58)

Lipolysis is an essential metabolic process that releases unesterified fatty acids from neutral lipid stores to maintain energy homeostasis in living organisms. Adipose triglyceride lipase (ATGL) plays a key role in intracellular lipolysis and can be co-activated upon interaction with the protein comparative gene identification-58 (CGI-58). The underlying molecular mechanism of ATGL stimulation by CGI-58 is incompletely understood. Based on analysis of evolutionary conservation, we used site directed mutagenesis to study a C-terminally truncated variant and full-length mouse ATGL providing insights in the protein co-activation on a per-residue level. We identified the region from residues N209-N215 in mouse ATGL as essential for co-activation by mouse CGI-58. ATGL variants with amino-acids exchanges in this region were still able to hydrolyze triacylglycerol at the basal level and to interact with CGI-58, yet could not be activated by CGI-58. Our studies also demonstrate that full-length mouse ATGL showed higher tolerance to specific single amino acid exchanges in the N209-N215 region upon CGI-58 co-activation compared to C-terminally truncated ATGL variants. The region is either directly involved in protein-protein interaction or essential for conformational changes required in the co-activation process. Three-dimensional models of the ATGL/CGI-58 complex with the artificial intelligence software AlphaFold demonstrated that a large surface area is involved in the protein-protein interaction. Mapping important amino acids for co-activation of both proteins, ATGL and CGI-58, onto the 3D model of the complex locates these essential amino acids at the predicted ATGL/CGI-58 interface thus strongly corroborating the significance of these residues in CGI-58 mediated co-activation of ATGL.

biochemistry↗

bioRxiv2023

Vibrio cholerae's ToxRS Bile Sensing System

Cholera represents a diarrheal disease caused by the Gram-negative bacterium Vibrio cholerae. Its environmental persistence causing recurring sudden outbreaks is enabled by V. choleraes 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 e.g. bile resistance and virulence regulation. Currently, transcription factor ToxR is described as main environmental sensor for bile acid, whose activity is enhanced by binding to ToxS. Here, 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. 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. Unraveling the intricate mechanisms governing ToxRSs environmental sensing capabilities, provides a promising tool for disruption of this vital interaction, ultimately inhibiting Vibrios 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.

biochemistry↗

bioRxiv2022

Exploring generality of experimental conformational changes with AlphaFold predictions

Structural predictions have matched the accuracy of experimental structures in the case of close homologues, outperformed docking methods for multimeric complexes and helped sampling the conformational landscape of transporters and receptors. Such successes prompt the question whether predictions can be used to relate experimental structures in the context of available knowledge. LysR-type transcriptional regulators (LTTR) constitute the most common family of bacterial regulators. Intriguingly, their experimental structures are remarkably diverse. The active species, composed of flexible monomers dimerizing through their N- and C-terminal domains in a circular arrangement, differ across LTTR, due to intrinsic sequence differences or because crystals stabilize diverse snapshots of a common dynamic mechanism. We have used AlphaFold2 (AF) to interrogate the experimental AtzR structure in the context of predictions guided towards the different hetero-multimeric conformations known for other LTTR. Our approach drives AF prediction with the structure-based selection of the information input through sequence alignment and template conformation, linked to examination of the energy with PISA and interactions with ALEPH.

biophysics↗

bioRxiv2021

Accurate prediction of protein structures and interactions using a 3-track network

DeepMind presented remarkably accurate protein structure predictions at the CASP14 conference. We explored network architectures incorporating related ideas and obtained the best performance with a 3-track network in which information at the 1D sequence level, the 2D distance map level, and the 3D coordinate level is successively transformed and integrated. The 3-track network produces structure predictions with accuracies approaching those of DeepMind in CASP14, enables rapid solution of challenging X-ray crystallography and cryo-EM structure modeling problems, and provides insights into the functions of proteins of currently unknown structure. The network also enables rapid generation of accurate models of protein-protein complexes from sequence information alone, short circuiting traditional approaches which require modeling of individual subunits followed by docking. We make the method available to the scientific community to speed biological research. One-Sentence SummaryAccurate protein structure modeling enables rapid solution of structure determination problems and provides insights into biological function.

bioinformatics↗