Clinically relevant mutations in regulatory regions of metabolic genes facilitate early adaptation to ciprofloxacin in Escherichia coli.

The genomic landscape associated with early adaptation to ciprofloxacin is poorly understood. Although the interplay between core metabolism and antimicrobial resistance is being increasingly recognized, mutations in metabolic genes and their biological role remain elusive. Here, we exposed Escherichia coli to increasing gradients of ciprofloxacin with intermittent transfer-bottlenecking and identified mutations in three non-canonical targets linked to metabolism including a deletion (tRNA-ArgΔ414-bp) and point mutations in the regulatory regions of argI (ARG box) and narU. Our findings suggest that these mutations modulate arginine and carbohydrate metabolism, facilitate anaerobiosis and increased ATP production during ciprofloxacin stress. Furthermore, mutations in the regulatory regions of argI and narU were detected in over 70% of sequences from clinical E. coli isolates and were overrepresented among ciprofloxacin-resistant isolates. In sum, we have identified clinically relevant mutations in the regulatory regions of metabolic genes as a central theme that drives physiological changes necessary for adaptation to ciprofloxacin stress.

Hot Excitons Cool in Metal Halide Perovskite Nanocrystals as Fast as CdSe Nanocrystals.

The idea of phonon bottlenecks has long been pursued in nanoscale materials for their application in hot exciton devices, such as photovoltaics. Decades ago, it was shown that there is no quantum phonon bottleneck in strongly confined quantum dots due to their physics of quantum confinement. More recently, it was proposed that there are hot phonon bottlenecks in metal halide perovskites due to their physics. Recent work has called into question these bottlenecks in metal halide perovskites. Here, we compare hot exciton cooling in a range of sizes of CsPbBr3 nanocrystals from weakly to strongly confined. These results are compared to strongly confined CdSe quantum dots of two sizes and degrees of quantum confinement. CdSe is a model system as a ruler for measuring hot exciton cooling being fast, by virtue of its efficient Auger-assisted processes. By virtue of 3 ps time resolution, the hot exciton photoluminescence can now be directly observed, which is the most direct measure of the presence of hot excitons and their lifetimes. The hot exciton photoluminescence decays on nearly the same 2 ps time scale on both the weakly confined perovskite and the larger CdSe quantum dots, much faster than the 10 ps cooling predicted by transient absorption experiments. The smaller CdSe quantum dot has still faster cooling, as expected from quantum size effects. The quantum dots of perovskites show extremely fast hot exciton cooling, decaying faster than detection limits of <1 ps, even faster than the CdSe system, suggesting the efficiency of Auger processes in these metal halide perovskite nanocrystals and especially in their quantum dot form. These results across a range of sizes of nanocrystals reveal extremely fast hot exciton cooling at high exciton density, independent of composition, but dependent upon size. Hence these metal halide perovskite nanocrystals seem to cool heavily following quantum dot physics.

Enhancing Multiexcitonic Emission in Metal-Halide Perovskites by Quantum Confinement.

Semiconductor metal halide perovskite nanocrystals have been under intense investigation for their promise in a variety of optoelectronic applications, which arises from their remarkable properties of defect tolerance and efficient light emission. Recently, quantum dot versions of perovskite nanocrystals have been available, enabling investigation of how quantum size effects control optical function and performance in these quantum dots (QD), past their well-known covalent II-VI analogues. We perform time-resolved photoluminescence (t-PL) experiments on CsPbBr3 perovskite nanocrystals spanning in diameter from 5.8 nm strongly confined quantum dots to 18 nm weakly confined quantum dots. Experiments are performed with sufficient time resolution of 3 ps to observe the interaction energies and recombination kinetics from excitons to multiexcitons. Comparing the same sized QD reveals that perovskite QD have a larger radiative rate constant for emission from X than CdSe QD due to a larger oscillator strength. The multiexciton (MX) regime reveals that perovskite QD emit brightly and with more focused bandwidth than equivalent sized CdSe QD enabling more spectrally pure brightness. The MX kinetics reveals that the perovskite QD maintain efficient radiative decay, effectively competing with Auger recombination. These experiments reveal that the strongly confined QD of perovskites can be efficient multiexcitonic emitters, such as in high brightness light emitting diodes, especially in the blue.

Direct Observation of Higher Multiexciton Formation and Annihilation in CdSe Quantum Dots.

Most experiments on multiexcitons (MX) in quantum dots focused on the biexciton (XX), which is now well-understood. In contrast, there is little understanding of higher MX in quantum dots as a result of their difficulty to observe. Here, we apply time-resolved photoluminescence (t-PL) spectroscopy with 3 ps time resolution, sufficient to directly resolve previously unobserved spectral dynamics of a higher MX in CdSe quantum dots. These experiments resolve the controversy of the sequence of MX emissions, revealing that the higher channels sequentially populate the lower channels. There is a strong dependence of MX recombination kinetics upon a higher MX state, following a universal volume scaling law for Auger recombination for larger dots. Smaller dots show deviations for higher MX. In addition to triexcitons (3X), these experiments reveal MX up to the tetraexciton (4X). These experiments provide a direct observation of MX formation and annihilation in quantum dots. The impact of this observation is a step toward designing quantum dots to exploit higher MX processes.

Identifying Markers of Emerging SARS-CoV-2 Variants in Patients With Secondary Immunodeficiency.

Since the end of 2019, the world has been challenged by the coronavirus disease 2019 (COVID-19) pandemic. With COVID-19 cases rising globally, severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) continues to evolve, resulting in the emergence of variants of interest (VOI) and of concern (VOC). Of the hundreds of millions infected, immunodeficient patients are one of the vulnerable cohorts that are most susceptible to this virus. These individuals include those with preexisting health conditions and/or those undergoing immunosuppressive treatment (secondary immunodeficiency). In these cases, several researchers have reported chronic infections in the presence of anti-COVID-19 treatments that may potentially lead to the evolution of the virus within the host. Such variations occurred in a variety of viral proteins, including key structural ones involved in pathogenesis such as spike proteins. Tracking and comparing such mutations with those arisen in the general population may provide information about functional sites within the SARS-CoV-2 genome. In this study, we reviewed the current literature regarding the specific features of SARS-CoV-2 evolution in immunocompromised patients and identified recurrent de novo amino acid changes in virus isolates of these patients that can potentially play an important role in SARS-CoV-2 pathogenesis and evolution.

Dual inhibition of SARS-CoV-2 spike and main protease through a repurposed drug, rutin.

The global health emergency caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has led to alarming numbers of fatalities across the world. So far the researchers worldwide have not been able to discover a breakthrough in the form of a potent drug or an effective vaccine. Therefore, it is imperative to discover drugs to curb the ongoing menace. In silico approaches using FDA approved drugs can expedite the drug discovery process by providing leads that can be pursued. In this report, two drug targets, namely the spike protein and main protease, belonging to structural and non-structural class of proteins respectively, were utilized to carry out drug repurposing based screening. The exposed nature of the spike protein on the viral surface along with its instrumental role in host infection and the involvement of main protease in processing of polyproteins along with no human homologue make these proteins attractive drug targets. Interestingly, the screening identified a common high efficiency binding molecule named rutin. Further, molecular dynamics simulations in explicit solvent affirmed the stable and sturdy binding of rutin with these proteins. The decreased Rg value (4 nm for spike-rutin and 2.23 nm for main protease-rutin) and stagnant SASA analysis (485 nm/S2/N in spike-rutin and 152 nm/S2/N in main protease-rutin) for protein surface and its orientation in the exposed and buried regions suggests a strong binding interaction of the drug. Further, cluster analysis and secondary structure analysis of complex trajectories validated the conformational changes due to binding of rutin.

Predicting phosphorylation sites using machine learning by integrating the sequence, structure, and functional information of proteins.

BACKGROUND: Post-translational modification (PTM) is a biological process that alters proteins and is therefore involved in the regulation of various cellular activities and pathogenesis. Protein phosphorylation is an essential process and one of the most-studied PTMs: it occurs when a phosphate group is added to serine (Ser, S), threonine (Thr, T), or tyrosine (Tyr, Y) residue. Dysregulation of protein phosphorylation can lead to various diseases-most commonly neurological disorders, Alzheimer's disease, and Parkinson's disease-thus necessitating the prediction of S/T/Y residues that can be phosphorylated in an uncharacterized amino acid sequence. Despite a surplus of sequencing data, current experimental methods of PTM prediction are time-consuming, costly, and error-prone, so a number of computational methods have been proposed to replace them. However, phosphorylation prediction remains limited, owing to substrate specificity, performance, and the diversity of its features. METHODS: In the present study we propose machine-learning-based predictors that use the physicochemical, sequence, structural, and functional information of proteins to classify S/T/Y phosphorylation sites. Rigorous feature selection, the minimum redundancy/maximum relevance approach, and the symmetrical uncertainty method were employed to extract the most informative features to train the models. RESULTS: The RF and SVM models generated using diverse feature types in the present study were highly accurate as is evident from good values for different statistical measures. Moreover, independent test sets and benchmark validations indicated that the proposed method clearly outperformed the existing methods, demonstrating its ability to accurately predict protein phosphorylation. CONCLUSIONS: The results obtained in the present work indicate that the proposed computational methodology can be effectively used for predicting putative phosphorylation sites further facilitating discovery of various biological processes mechanisms.

Long-range replica exchange molecular dynamics guided drug repurposing against tyrosine kinase PtkA of Mycobacterium tuberculosis.

Tuberculosis (TB) is a leading cause of death worldwide and its impact has intensified due to the emergence of multi drug-resistant (MDR) and extensively drug-resistant (XDR) TB strains. Protein phosphorylation plays a vital role in the virulence of Mycobacterium tuberculosis (M.tb) mediated by protein kinases. Protein tyrosine phosphatase A (MptpA) undergoes phosphorylation by a unique tyrosine-specific kinase, protein tyrosine kinase A (PtkA), identified in the M.tb genome. PtkA phosphorylates PtpA on the tyrosine residues at positions 128 and 129, thereby increasing PtpA activity and promoting pathogenicity of MptpA. In the present study, we performed an extensive investigation of the conformational behavior of the intrinsically disordered domain (IDD) of PtkA using replica exchange molecular dynamics simulations. Long-term molecular dynamics (MD) simulations were performed to elucidate the role of IDD on the catalytic activity of kinase core domain (KCD) of PtkA. This was followed by identification of the probable inhibitors of PtkA using drug repurposing to block the PtpA-PtkA interaction. The inhibitory role of IDD on KCD has already been established; however, various analyses conducted in the present study showed that IDDPtkA had a greater inhibitory effect on the catalytic activity of KCDPtkA in the presence of the drugs esculin and inosine pranobex. The binding of drugs to PtkA resulted in formation of stable complexes, indicating that these two drugs are potentially useful as inhibitors of M.tb.

Computational models for the prediction of adverse cardiovascular drug reactions.

BACKGROUND: Predicting adverse drug reactions (ADRs) has become very important owing to the huge global health burden and failure of drugs. This indicates a need for prior prediction of probable ADRs in preclinical stages which can improve drug failures and reduce the time and cost of development thus providing efficient and safer therapeutic options for patients. Though several approaches have been put forward for in silico ADR prediction, there is still room for improvement. METHODS: In the present work, we have used machine learning based approach for cardiovascular (CV) ADRs prediction by integrating different features of drugs, biological (drug transporters, targets and enzymes), chemical (substructure fingerprints) and phenotypic (therapeutic indications and other identified ADRs), and their two and three level combinations. To recognize quality and important features, we used minimum redundancy maximum relevance approach while synthetic minority over-sampling technique balancing method was used to introduce a balance in the training sets. RESULTS: This is a rigorous and comprehensive study which involved the generation of a total of 504 computational models for 36 CV ADRs using two state-of-the-art machine-learning algorithms: random forest and sequential minimization optimization. All the models had an accuracy of around 90% and the biological and chemical features models were more informative as compared to the models generated using chemical features. CONCLUSIONS: The results obtained demonstrated that the predictive models generated in the present study were highly accurate, and the phenotypic information of the drugs played the most important role in drug ADRs prediction. Furthermore, the results also showed that using the proposed method, different drugs properties can be combined to build computational predictive models which can effectively predict potential ADRs during early stages of drug development.