Structural and Functional Characterization of Rv0792c from Mycobacterium tuberculosis: Identifying Small Molecule Inhibitor against HutC Protein

Recent advances in research on Mycobacterium tuberculosis virulence factors and their role in pathogenesis

Mycobacterium tuberculosis (Mtb) is the causative agent of tuberculosis (TB) in humans and animals. Mtb invades the host's lungs via airborne transmission, infecting macrophages and causing TB. In some cases, the infection can spread to other tissues and organs. Despite the availability of several drugs for TB treatment, the emergence of multidrug-resistant TB has led to high morbidity and mortality rates worldwide. Therefore, it is urgent to discover new anti-tuberculosis drugs for more effective treatment. Recent studies have shown that Mtb virulence factors play a crucial role in its pathogenicity. By evading the host's immune surveillance through mechanisms such as anti-oxidative stress, nutrient synthesis and metabolism, and apoptosis in host cells, Mtb can achieve long-term survival in the host. Understanding the pathogenicity mechanisms of Mtb will aid the development of new vaccines and anti-tuberculosis drugs. In this review, we summarize the latest research progress on Mtb virulence factors to provide a reference for targeted TB treatment.

Targeting bacterial transcription factors for infection control: opportunities and challenges

The rising threat of antibiotic resistance in pathogenic bacteria emphasizes the need for new therapeutic strategies. This review focuses on bacterial transcription factors (TFs), which play crucial roles in bacterial pathogenesis. We discuss the regulatory roles of these factors through examples, and we outline potential therapeutic strategies targeting bacterial TFs. Specifically, we discuss the use of small molecules to interfere with TF function and the development of transcription factor decoys, oligonucleotides that compete with promoters for TF binding. We also cover peptides that target the interaction between the bacterial TF and other factors, such as RNA polymerase, and the targeting of sigma factors. These strategies, while promising, come with challenges, from identifying targets to designing interventions, managing side effects, and accounting for changing bacterial resistance patterns. We also delve into how Artificial Intelligence contributes to these efforts and how it may be exploited in the future, and we touch on the roles of multidisciplinary collaboration and policy to advance this research domain.Abbreviations: AI, artificial intelligence; CNN, convolutional neural networks; DTI: drug-target interaction; HTH, helix-turn-helix; IHF, integration host factor; LTTRs, LysR-type transcriptional regulators; MarR, multiple antibiotic resistance regulator; MRSA, methicillin resistant Staphylococcus aureus; MSA: multiple sequence alignment; NAP, nucleoid-associated protein; PROTACs, proteolysis targeting chimeras; RNAP, RNA polymerase; TF, transcription factor; TFD, transcription factor decoying; TFTRs, TetR-family transcriptional regulators; wHTH, winged helix-turn-helix.

Visualizing gaussian-chain like structural models of human α-synuclein in monomeric pre-fibrillar state: Solution SAXS data and modeling analysis

Here, using small angle X-ray scattering (SAXS) data profile as reference, we attempted to visualize conformational ensemble accessible prefibrillar monomeric state of α-synuclein in solution. In agreement with previous reports, our analysis also confirmed that α-synuclein molecules adopted disordered shape profile under non-associating conditions. Chain-ensemble modeling protocol with dummy residues provided two weighted averaged clusters of semi-extended shapes. Further, Ensemble Optimization Method (EOM) computed mole fractions of semi-extended "twisted" conformations which might co-exist in solution. Since these were only Cα traces of the models, ALPHAFOLD2 server was used to search for all-atom models. Comparison with experimental data showed all predicted models disagreed equally, as individuals. Finally, we employed molecular dynamics simulations and normal mode analysis-based search coupled with SAXS data to seek better agreeing models. Overall, our analysis concludes that a shifting equilibrium of curved models with low α-helical content best-represents non-associating monomeric α-synuclein.