Variations in the SDN Loop of Class A Beta-Lactamases: A Study of the Molecular Mechanism of BlaC (Mycobacterium tuberculosis) to Alter the Stability and Catalytic Activity Towards Antibiotic Resistance of MBIs

The emergence of multidrug-resistant (MDR) and extensively drug-resistant (XDR) tuberculosis calls for an immediate search for novel treatment strategies. Recently, BlaC, the principal beta-lactamase of Mycobacterium tuberculosis, was recognized as a potential therapeutic target. BlaC belongs to Ambler class A, which is generally susceptible to the beta-lactamase inhibitors currently used in clinics: tazobactam, sulbactam, and clavulanate. Alterations at Ser130 in conserved SDN loop confer resistance to mechanism-based inhibitors (MBIs) commonly observed in various clinical isolates. The absence of clinical evidence of S130G conversion in M. tuberculosis draws our attention to build laboratory mutants of S130G and S130A of BlaC. The study involving steady state, inhibition kinetics, and fluorescence microscopy shows the emergence of resistance against MBIs to the mutants expressing S130G and S130A. To understand the molecular reasoning behind the unavailability of such mutation in real life, we have used circular dichroism (CD) spectroscopy, differential scanning calorimetry (DSC), molecular dynamics (MD) simulation, and stability-based enzyme activity to compare the stability and dynamic behaviors of native and S130G/A mutant form of BlaC. A significant decrease in melting temperature (BlaC T M 60°C, S130A T M 50°C, and S130G T M 45°C), kinetic instability at higher temperature, and comparative dynamic instability correlate the fact that resistance to beta-lactam/beta-lactamase inhibitor combinations will likely not arise from the structural alteration of BlaC, therefore establishing confidence that this therapeutic modality can be potentially applied as a part of a successful treatment regimen against M. tuberculosis

Repurposing therapeutics for COVID-19: Rapid prediction of commercially available drugs through machine learning and docking.

BackgroundThe outbreak of the novel coronavirus disease COVID-19, caused by the SARS-CoV-2 virus has spread rapidly around the globe during the past 3 months. As the virus infected cases and mortality rate of this disease is increasing exponentially, scientists and researchers all over the world are relentlessly working to understand this new virus along with possible treatment regimens by discovering active therapeutic agents and vaccines. So, there is an urgent requirement of new and effective medications that can treat the disease caused by SARS-CoV-2.Methods and findingsWe perform the study of drugs that are already available in the market and being used for other diseases to accelerate clinical recovery, in other words repurposing of existing drugs. The vast complexity in drug design and protocols regarding clinical trials often prohibit developing various new drug combinations for this epidemic disease in a limited time. Recently, remarkable improvements in computational power coupled with advancements in Machine Learning (ML) technology have been utilized to revolutionize the drug development process. Consequently, a detailed study using ML for the repurposing of therapeutic agents is urgently required. Here, we report the ML model based on the Naive Bayes algorithm, which has an accuracy of around 73% to predict the drugs that could be used for the treatment of COVID-19. Our study predicts around ten FDA approved commercial drugs that can be used for repurposing. Among all, we found that 3 of the drugs fulfils the criterions well among which the antiretroviral drug Amprenavir (DrugBank ID-DB00701) would probably be the most effective drug based on the selected criterions.ConclusionsOur study can help clinical scientists in being more selective in identifying and testing the therapeutic agents for COVID-19 treatment. The ML based approach for drug discovery as reported here can be a futuristic smart drug designing strategy for community applications

Ultrasensitive and multiplexed tracking of single cells using whole-body PET/CT.

In vivo molecular imaging tools are crucially important for elucidating how cells move through complex biological systems; however, achieving single-cell sensitivity over the entire body remains challenging. Here, we report a highly sensitive and multiplexed approach for tracking upward of 20 single cells simultaneously in the same subject using positron emission tomography (PET). The method relies on a statistical tracking algorithm (PEPT-EM) to achieve a sensitivity of 4 becquerel per cell and a streamlined workflow to reliably label single cells with over 50 becquerel per cell of 18F-fluorodeoxyglucose (FDG). To demonstrate the potential of the method, we tracked the fate of more than 70 melanoma cells after intracardiac injection and found they primarily arrested in the small capillaries of the pulmonary, musculoskeletal, and digestive organ systems. This study bolsters the evolving potential of PET in offering unmatched insights into the earliest phases of cell trafficking in physiological and pathological processes and in cell-based therapies