Biology subjects

Lazaridi, S.

Publications and source records attributed to Lazaridi, S..

3 recordsLinked to original sources

bioRxiv2024

Atomic insights into the signaling landscape of E. coli PhoQ Histidine Kinase from Molecular Dynamics simulations

Bacteria rely on two-component systems to sense environmental cues and regulate gene expression for adaptation. The PhoQ/PhoP system exemplifies this crucial role, playing a key part in sensing magnesium (Mg2+) levels, antimicrobial peptides, mild acidic pH, osmotic upshift, and long-chain unsaturated fatty acids, promoting virulence in certain bacterial species. However, the precise details of PhoQ activation remain elusive. To elucidate PhoQs signaling mechanism at atomic resolution, we combined AlphaFold2 predictions with molecular modeling and carried out extensive Molecular Dynamics (MD) simulations. Our MD simulations revealed three distinct PhoQ conformations that were validated by experimental data. Notably, one conformation was characterized by Mg2+ bridging the acidic patch in the sensor domain to the membrane, potentially representing a repressed state. Furthermore, the high hydration observed in a putative intermediate state lends support to the hypothesis of water-mediated conformational changes during PhoQ signaling. Our findings not only revealed specific conformations within the PhoQ signaling pathway, but also hold significant promise for understanding the broader histidine kinase family due to their shared structural features. Our approach paves the way for a more comprehensive understanding of histidine kinase signaling mechanisms across various bacterial species and opens the door for developing novel therapeutics that target PhoQ modulation.

molecular biology↗

bioRxiv2024

TemBERTure: Advancing protein thermostability prediction with Deep Learning and attention mechanisms

Understanding protein thermostability is essential for various biotechnological and biological applications. However, traditional experimental methods for assessing this property are time-consuming, expensive, and error-prone. Recently, the application of Deep Learning techniques from Natural Language Processing (NLP) was extended to the field of biology, with an emphasis on protein modeling. From a linguistic perspective, the primary sequence of proteins can be viewed as a string of amino acids that follow a physicochemical grammar. This study explores the potential of Deep Learning models trained on protein sequences to predict protein thermostability which provide improvements with respect to current approaches. We implemented TemBERTure, a Deep Learning framework to classify the thermal class (non-thermophilic or thermophilic) and predict and melting temperature of a protein, based on its primary sequence. Our findings highlight the critical role that data diversity plays on training robust models. Models trained on datasets with a wider range of sequences from various organisms exhibited superior performance compared to those with limited diversity. This emphasizes the need for a comprehensive data curation strategy that ensures a balanced representation of diverse species in the training data, to avoid the risk that the model focuses on recognizing the evolutionary lineage of the sequence rather than the intrinsic thermostability features. In order to gain more nuanced insights into protein thermostability, we propose leveraging attention scores within Deep Learning models to gain more nuanced insights into protein thermostability. We show that analyzing these scores alongside the 3D protein structure could offer a better understanding of the complex interplay between amino acid properties, their positioning, and the surrounding microenvironment, all crucial factors influencing protein thermostability. This work sheds light on the limitations of current protein thermostability prediction methods and introduces new avenues for exploration. By emphasizing data diversity and utilizing refined attention scores, future research can pave the way for more accurate and informative methods for predicting protein thermostability. Availability and ImplementationTemBERTure model and the data are available at https://github.com/ibmm-unibe-ch/TemBERTure

bioinformatics↗

bioRxiv2022

The inhibitory mechanism of a small protein reveals its role in antimicrobial peptide sensing

A large number of small membrane proteins have been discovered in bacteria, but their mechanism of action has remained mostly elusive. Here, we investigate the mechanism of a physiologically important small protein, MgrB, which represses the activity of the sensor kinase PhoQ and is widely distributed among enterobacteria. The PhoQ/PhoP two-component system is a master regulator of the bacterial virulence program and interacts with MgrB to modulate bacterial virulence, fitness, and drug resistance. A combination of crosslinking approaches with functional assays and protein dynamic simulations revealed structural rearrangements due to interactions between MgrB and PhoQ near the membrane/periplasm interface and along the transmembrane helices. These interactions induce the movement of the PhoQ catalytic domain and the repression of its activity. Without MgrB, PhoQ appears to be much less sensitive to antimicrobial peptides, including the commonly used C18G. In the presence of MgrB, C18G promotes MgrB to dissociate from PhoQ, thus activating PhoQ via derepression. Our findings reveal the inhibitory mechanism of the small protein MgrB and uncover its importance in antimicrobial peptide sensing. Significance StatementSmall proteins have high prevalence, vast diversity, and primarily regulatory functions in biological processes across all domains of life. However, their mechanisms of action remain largely elusive. In this study, we investigate the mechanism of the small protein, MgrB. It interacts with the sensor kinase PhoQ, rearranges its conformation, represses its kinase activity, and regulates bacterial response to environmental changes. In particular for antimicrobial peptides, MgrB is required for bacteria to have a selective response to this host-exclusive stimulus. Our findings underline the importance of a small protein in bacterial fitness and drug resistance and provide a molecular basis for engineering novel peptide-based regulators.

microbiology↗