DNA shape features improve prediction of CRISPR/Cas9 activity.

The CRISPR/Cas9 genome editing technology has transformed basic and translational research in biology and medicine. However, the advances are hindered by off-target effects and a paucity in the knowledge of the mechanism of the Cas9 protein. Machine learning models have been proposed for the prediction of Cas9 activity at unintended sites, yet feature engineering plays a major role in the outcome of the predictors. This study evaluates the improvement in the performance of similar predictors upon inclusion of epigenetic and DNA shape feature groups in the conventionally used sequence-based Cas9 target and off-target datasets. The approach involved the utilization of neural networks trained on a diverse range of parameters, allowing us to systematically assess the performance increase for the meticulously designed datasets- (i) sequence only, (ii) sequence and epigenetic features, and (iii) sequence, epigenetic and DNA shape feature datasets. The addition of DNA shape information significantly improved predictive performance, evaluated by Akaike and Bayesian information criteria. The evaluation of individual feature importance by permutation and LIME-based methods also indicates that not only sequence features like mismatches and nucleotide composition, but also base pairing parameters like opening and stretch, that are indicative of distortion in the DNA-RNA hybrid in the presence of mismatches, influence model outcomes.

Molecular insights into the binding interactions and energetics of the omicron spike variant with hACE2 and a neutralizing antibody.

The global spread of Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) since 2019 has led to a continuous evolution of viral variants, with the latest concern being the Omicron (B.1.1.529) variant. In this study, classical molecular dynamics simulations were conducted to elucidate the biophysical aspects of the Omicron spike protein's receptor-binding domain (RBD) in its interaction with human angiotensin-converting enzyme 2 (hACE2) and a neutralizing antibody, comparing it to the wildtype (WT). To model the Omicron variant, 15 in silico mutations were introduced in the RBD region of WT (retrieved from PDB). The simulations of WT spike-hACE2 and Omicron spike-hACE2 complexes revealed comparable binding stability and dynamics. Notably, the Q493R mutation in the Omicron spike increased interactions with hACE2, particularly with ASP38 and ASP355. Additionally, mutations such as N417K, T478K, and Y505H contributed to enhanced structural stability in the Omicron variant. Conversely, when comparing WT with Omicron in complex with a neutralizing antibody, simulation results demonstrated poorer binding dynamics and stability for the Omicron variant. The E484K mutation significantly decreased binding interactions, resulting in an overall decrease in binding energy (∼-57 kcal/mol) compared to WT (∼-84 kcal/mol). This study provides valuable molecular insights into the heightened infectivity of the Omicron variant, shedding light on the specific mutations influencing its interactions with hACE2 and neutralizing antibodies.

Anti-COVID-19 Potential of Withaferin-A and Caffeic Acid Phenethyl Ester.

BACKGROUND: The recent COVID-19 (coronavirus disease 2019) pandemic triggered research on the development of new vaccines/drugs, repurposing of clinically approved drugs, and assessment of natural anti-COVID-19 compounds. Based on the gender difference in the severity of the disease, such as a higher number of men hospitalized and in intense care units, variations in sex hormones have been predicted to play a role in disease susceptibility. Cell surface receptors (Angiotensin-Converting Enzyme 2; ACE2 and a connected transmembrane protease serine 2- TMPSS2) are upregulated by androgens. Conversely, androgen antagonists have also been shown to lower ACE2 levels, implying their usefulness in COVID-19 management. OBJECTIVE: In this study, we performed computational and cell-based assays to investigate the anti-- COVID-19 potential of Withaferin-A and Caffeic acid phenethyl ester, natural compounds from Withania somnifera and honeybee propolis, respectively. METHODS: Structure-based computational approach was adopted to predict binding stability, interactions, and dynamics of the two test compounds to three target proteins (androgen receptor, ACE2, and TMPRSS2). Further, in vitro, cell-based experimental approaches were used to investigate the effect of compounds on target protein expression and SARS-CoV-2 replication. RESULTS: Computation and experimental analyses revealed that (i) CAPE, but not Wi-A, can act as androgen antagonist and hence inhibit the transcriptional activation function of androgen receptor, (ii) while both Wi-A and CAPE could interact with ACE2 and TMPRSS2, Wi-A showed higher binding affinity, and (iii) combination of Wi-A and CAPE (Wi-ACAPE) caused strong downregulation of ACE2 and TMPRSS2 expression and inhibition of virus infection. CONCLUSION: Wi-A and CAPE possess multimodal anti-COVID-19 potential, and their combination (Wi-ACAPE) is expected to provide better activity and hence warrant further attention in the laboratory and clinic.

M-Ionic: prediction of metal-ion-binding sites from sequence using residue embeddings.

MOTIVATION: Understanding metal-protein interaction can provide structural and functional insights into cellular processes. As the number of protein sequences increases, developing fast yet precise computational approaches to predict and annotate metal-binding sites becomes imperative. Quick and resource-efficient pre-trained protein language model (pLM) embeddings have successfully predicted binding sites from protein sequences despite not using structural or evolutionary features (multiple sequence alignments). Using residue-level embeddings from the pLMs, we have developed a sequence-based method (M-Ionic) to identify metal-binding proteins and predict residues involved in metal binding. RESULTS: On independent validation of recent proteins, M-Ionic reports an area under the curve (AUROC) of 0.83 (recall = 84.6%) in distinguishing metal binding from non-binding proteins compared to AUROC of 0.74 (recall = 61.8%) of the next best method. In addition to comparable performance to the state-of-the-art method for identifying metal-binding residues (Ca2+, Mg2+, Mn2+, Zn2+), M-Ionic provides binding probabilities for six additional ions (i.e. Cu2+, Po43-, So42-, Fe2+, Fe3+, Co2+). We show that the pLM embedding of a single residue contains sufficient information about its neighbours to predict its binding properties. AVAILABILITY AND IMPLEMENTATION: M-Ionic can be used on your protein of interest using a Google Colab Notebook (https://bit.ly/40FrRbK). The GitHub repository (https://github.com/TeamSundar/m-ionic) contains all code and data.

Construction and characterization of a temperature-sensitive pRC4 replicon for Rhodococcus and Gordonia.

Temperature-sensitive plasmids are useful for genome engineering and several synthetic biology applications. There are only limited reports on temperature-sensitive plasmids for Rhodococcus and none for Gordonia. Here, we report the construction of a temperature-sensitive pRC4 replicon that is functional in Rhodococcus and Gordonia. The amino acid residues were predicted for the temperature-sensitive phenotype in the pRC4 replicon using in silico methods and molecular simulation of the DNA-binding replication protein with the origin of replication. The amino acid residues were mutated, and the temperature-sensitive phenotype was validated in Gordonia sp. IITR100. Similar results were also observed in Rhodococcus erythropolis, suggesting that the temperature-sensitive phenotype was exhibited across genera.

Cucurbitacin-B inhibits cancer cell migration by targeting mortalin and HDM2: computational and in vitro experimental evidence.

Cancer metastasis, a highly complex process wherein cancer cells move from the primary site to other sites in the body, is a major hurdle in its therapeutics. A large array of synthetic chemotherapeutic molecules used for the treatment of metastatic cancers, besides being extremely expensive and unaffordable, are known to cause severe adverse effects leading to poor quality of life (QOL) of the patients. In this premise, natural compounds (considered safe, easily available and economic) that possess the potential to inhibit migration of cancer cells are deemed useful and hence are on demand. Cucurbitacin-B (19-(10→9ß)-abeo-10-lanost-5-ene triterpene, called Cuc-B) is a steroid mostly found in plants of Cucurbitaceae family. It has been shown to possess anticancer activity although the molecular mechanism remains poorly defined. We present evidence that Cuc-B has the ability to interact with mortalin and HDM2 proteins that are enriched in cancer cells, suppress wild type p53 function and promote cancer cell migration. Computational analyses showed that Cuc-B interacts with mortalin similar to MKT077 and Withanone, both have been shown to reactivate p53 function and inhibit cell migration. Furthermore, Cuc-B interacted with HDM2 similar to Y30, a well-known inhibitor of HDM2. Experimental cell and molecular analyses demonstrated the downregulation of several proteins, critically involved in cell migration in Cuc-B (low non-toxic doses)-treated cancer cells and exhibited inhibition of cell migration. The data suggested that Cuc-B is a potential natural drug that warrants further mechanistic and clinical studies for its use in the management of metastatic cancers.Communicated by Ramaswamy H. Sarma.

TCR-ESM: Employing protein language embeddings to predict TCR-peptide-MHC binding.

Cognate target identification for T-cell receptors (TCRs) is a significant barrier in T-cell therapy development, which may be overcome by accurately predicting TCR interaction with peptide-bound major histocompatibility complex (pMHC). In this study, we have employed peptide embeddings learned from a large protein language model- Evolutionary Scale Modeling (ESM), to predict TCR-pMHC binding. The TCR-ESM model presented outperforms existing predictors. The complementarity-determining region 3 (CDR3) of the hypervariable TCR is located at the center of the paratope and plays a crucial role in peptide recognition. TCR-ESM trained on paired TCR data with both CDR3α and CDR3ß chain information performs significantly better than those trained on data with only CDR3ß, suggesting that both TCR chains contribute to specificity, the relative importance however depends on the specific peptide-MHC targeted. The study illuminates the importance of MHC information in TCR-peptide binding which remained inconclusive so far and was thought dependent on the dataset characteristics. TCR-ESM outperforms existing approaches on external datasets, suggesting generalizability. Overall, the potential of deep learning for predicting TCR-pMHC interactions and improving the understanding of factors driving TCR specificity are highlighted. The prediction model is available at http://tcresm.dhanjal-lab.iiitd.edu.in/ as an online tool.

Improved Machine Learning-Based Model for the Classification of Off-Targets in the CRISPR/Cpf1 System.

Targeted nucleases are widely used for altering the specific location of the genome with precision. The endonucleases facilitate efficient genome editing via designing a guide RNA (gRNA) consisting of a 20-nucleotide target sequence. gRNA preferably binds to the target location, but the on- and off-target activities of gRNAs vary widely. The off-target activity due to mismatch tolerance in the CRISPR-Cas system is a major factor inhibiting its clinical applications. Ensuring on-target efficiency and minimizing off-targets for a target sequence are the major objectives of this study. A pipeline has been designed to predict potential off-target sites in the human genome for a target sequence, and a multilayer perceptron (MLP) has been used to predict the cleavage efficiency of the potential off-target sites. An MLP-based classifier was trained with sequence- and base-dependent binding energy-associated features for AsCpf1 and LbCpf1 to predict the target efficiencies. Positional preferences of nucleotides, distribution of mismatches, and classification-dependent feature importance between high-activity and low-activity off-targets were also studied. Positional preference of nucleotides revealed that thymine is highly disfavored at positions adjacent to Protospacer Adjacent Motif (PAM), whereas guanine is favored in high-activity off-targets. Mismatch distribution analysis revealed that mismatches were more prominent in the trunk region (16, 17, 18 nucleotides from PAM sequence), and the promiscuous region and transition type mismatch were more preferred at 16, 17, and 18 nucleotides positions. The distribution of mismatches was a distinctive feature between high-activity and low-activity off-targets. Thermodynamics-associated features such as low to moderate melting temperature of the nonseed region and base-dependent PAM binding energy were predicted as best predictors by the multilayer perceptron for high-activity off-targets. GC content, some types of dinucleotide frequencies, number of bulges, and mismatches in the seed and trunk regions were other characteristic features between high-activity and low-activity off-targets for both LbCpf1 and AsCpf1.

Molecular insights to the anti-COVID-19 potential of α-, ß- and γ-cyclodextrins.

SARS-CoV-2 viral infection is regulated by the host cell receptors ACE2 and TMPRSS2, and therefore the effect of various natural and synthetic compounds on these receptors has recently been the subject of investigations. Cyclodextrins, naturally occurring polysaccharides derived from starch, are soluble in water and have a hydrophobic cavity at their center enabling them to accommodate small molecules and utilize them as carriers in the food, supplements, and pharmaceutical industries to improve the solubility, stability, and bioavailability of target compounds. In the current study, computational molecular simulations were used to investigate the ability of α-, ß- and γ-Cyclodextrins on human cell surface receptors. Cell-based experimental approaches, including expression analyses at mRNA and protein levels and virus replication, were used to assess the effect on receptor expression and virus infection, respectively. We found that none of the three CDs could dock effectively to human cell surface receptor ACE2 and viral protease Mpro (essential for virus replication). On the other hand, α- and ß-CD showed strong and stable interactions with TMPRSS2, and the expression of both ACE2 and TMPRSS2 was downregulated at the mRNA and protein levels in cyclodextrin (CD)-treated cells. A cell-based virus replication assay showed ∼20% inhibition by ß- and γ-CD. Taken together, the study suggested that (i) downregulation of expression of host cell receptors may not be sufficient to inhibit virus infection (ii) activity of the receptors and virus protein Mpro may play a critical and clinically relevant role, and hence (iii) newly emerging anti-Covid-19 compounds warrant multimodal functional analyses.Communicated by Ramaswamy H. Sarma.

A systematic comparison of human mitochondrial genome assembly tools.

BACKGROUND: Mitochondria are the cell organelles that produce most of the chemical energy required to power the cell's biochemical reactions. Despite being a part of a eukaryotic host cell, the mitochondria contain a separate genome whose origin is linked with the endosymbiosis of a prokaryotic cell by the host cell and encode independent genomic information throughout their genomes. Mitochondrial genomes accommodate essential genes and are regularly utilized in biotechnology and phylogenetics. Various assemblers capable of generating complete mitochondrial genomes are being continuously developed. These tools often use whole-genome sequencing data as an input containing reads from the mitochondrial genome. Till now, no published work has explored the systematic comparison of all the available tools for assembling human mitochondrial genomes using short-read sequencing data. This evaluation is required to identify the best tool that can be well-optimized for small-scale projects or even national-level research. RESULTS: In this study, we have tested the mitochondrial genome assemblers for both simulated datasets and whole genome sequencing (WGS) datasets of humans. For the highest computational setting of 16 computational threads with the simulated dataset having 1000X read depth, MitoFlex took the least execution time of 69 s, and IOGA took the longest execution time of 1278 s. NOVOPlasty utilized the least computational memory of approximately 0.098 GB for the same setting, whereas IOGA utilized the highest computational memory of 11.858 GB. In the case of WGS datasets for humans, GetOrganelle and MitoFlex performed the best in capturing the SNPs information with a mean F1-score of 0.919 at the sequencing depth of 10X. MToolBox and NOVOPlasty performed consistently across all sequencing depths with a mean F1 score of 0.897 and 0.890, respectively. CONCLUSIONS: Based on the overall performance metrics and consistency in assembly quality for all sequencing data, MToolBox performed the best. However, NOVOPlasty was the second fastest tool in execution time despite being single-threaded, and it utilized the least computational resources among all the assemblers when tested on simulated datasets. Therefore, NOVOPlasty may be more practical when there is a significant sample size and a lack of computational resources. Besides, as long-read sequencing gains popularity, mitochondrial genome assemblers must be developed to use long-read sequencing data.

Effect of Withaferin-A, Withanone, and Caffeic Acid Phenethyl Ester on DNA Methyltransferases: Potential in Epigenetic Cancer Therapy.

BACKGROUND: DNA methyltransferases (DNMTs) have been reported to be potential drug targets in various cancers. The major hurdle in inhibiting DNMTs is the lack of knowledge about different DNMTs and their role in the hypermethylation of gene promoters in cancer cells. Lack of information on specificity, stability, and higher toxicity of previously reported DNMT inhibitors is the major reason for inadequate epigenetic cancer therapy. DNMT1 and DNMT3A are the two DNMTs that are majorly overexpressed in cancers. OBJECTIVE: In this study, we have presented computational and experimental analyses of the potential of some natural compounds, withaferin A (Wi-A), withanone (Wi-N), and caffeic acid phenethyl ester (CAPE), as DNMT inhibitors, in comparison to sinefungin (SFG), a known dual inhibitor of DNMT1 and DNMT3A. METHODS: We used classical simulation methods, such as molecular docking and molecular dynamics simulations, to investigate the binding potential and properties of the test compounds with DNMT1 and DNMT3A. Cell culture-based assays were used to investigate the inactivation of DNMTs and the resulting hypomethylation of the p16INK4A promoter, a key tumour suppressor that is inactivated by hypermethylation in cancer cells, resulting in upregulation of its expression. RESULTS: Among the three test compounds (Wi-A, Wi-N, and CAPE), Wi-A showed the highest binding affinity to both DNMT1 and DNMT3A; CAPE showed the highest affinity to DNMT3A, and Wi-N showed a moderate affinity interaction with both. The binding energies of Wi-A and CAPE were further compared with SFG. Expression analysis of DNMTs showed no difference between control and treated cells. Cell viability and p16INK4A expression analysis showed a dose-dependent decrease in viability, an increase in p16INK4A, and a stronger effect of Wi-A compared to Wi-N and CAPE. CONCLUSION: The study demonstrated the differential binding ability of Wi-A, Wi-N, and CAPE to DNMT1 and DNMT3A, which was associated with their inactivation, leading to hypomethylation and desilencing of the p16INK4A tumour suppressor in cancer cells. The test compounds, particularly Wi-A, have the potential for cancer therapy.

Polypharmacological repurposing approach identifies approved drugs as potential inhibitors of Mycobacterium tuberculosis.

Mycobacterium tuberculosis (M. tb), the causative pathogen of tuberculosis (TB) remains the leading cause of death from single infectious agent. Furthermore, its evolution to multi-drug resistant (MDR) and extremely drug-resistant (XDR) strains necessitate de novo identification of drug-targets/candidates or to repurpose existing drugs against known targets through drug repurposing. Repurposing of drugs has gained traction recently where orphan drugs are exploited for new indications. In the current study, we have combined drug repurposing with polypharmacological targeting approach to modulate structure-function of multiple proteins in M. tb. Based on previously established essentiality of genes in M. tb, four proteins implicated in acceleration of protein folding (PpiB), chaperone assisted protein folding (MoxR1), microbial replication (RipA) and host immune modulation (S-adenosyl dependent methyltransferase, sMTase) were selected. Genetic diversity analyses in target proteins showed accumulation of mutations outside respective substrate/drug binding sites. Using a composite receptor-template based screening method followed by molecular dynamics simulations, we have identified potential candidates from FDA approved drugs database; Anidulafungin (anti-fungal), Azilsartan (anti-hypertensive) and Degarelix (anti-cancer). Isothermal titration calorimetric analyses showed that the drugs can bind with high affinity to target proteins and interfere with known protein-protein interaction of MoxR1 and RipA. Cell based inhibitory assays of these drugs against M. tb (H37Ra) culture indicates their potential to interfere with pathogen growth and replication. Topographic assessment of drug-treated bacteria showed induction of morphological aberrations in M. tb. The approved candidates may also serve as scaffolds for optimization to future anti-mycobacterial agents which can target MDR strains of M. tb.

Hybrid Multitask Learning Reveals Sequence Features Driving Specificity in the CRISPR/Cas9 System.

CRISPR/Cas9 technology is capable of precisely editing genomes and is at the heart of various scientific and medical advances in recent times. The advances in biomedical research are hindered because of the inadvertent burden on the genome when genome editors are employed-the off-target effects. Although experimental screens to detect off-targets have allowed understanding the activity of Cas9, that knowledge remains incomplete as the rules do not extrapolate well to new target sequences. Off-target prediction tools developed recently have increasingly relied on machine learning and deep learning techniques to reliably understand the complete threat of likely off-targets because the rules that drive Cas9 activity are not fully understood. In this study, we present a count-based as well as deep-learning-based approach to derive sequence features that are important in deciding on Cas9 activity at a sequence. There are two major challenges in off-target determination-the identification of a likely site of Cas9 activity and the prediction of the extent of Cas9 activity at that site. The hybrid multitask CNN-biLSTM model developed, named CRISP-RCNN, simultaneously predicts off-targets and the extent of activity on off-targets. Employing methods of integrated gradients and weighting kernels for feature importance approximation, analysis of nucleotide and position preference, and mismatch tolerance have been performed.

Modulation of DNA-protein Interactions by Proximal Genetic Elements as Uncovered by Interpretable Deep Learning.

Transcription factors (TF) recognize specific motifs in the genome that are typically 6-12 bp long to regulate various aspects of the cellular machinery. Presence of binding motifs and favorable genome accessibility are key drivers for a consistent TF-DNA interaction. Although these pre-requisites may occur thousands of times in the genome, there seems to be a high degree of selectivity for the sites that are actually bound. Here, we present a deep-learning framework that identifies and characterizes the upstream and downstream genetic elements to the binding motif, for their role in enforcing the mentioned selectivity. The proposed framework is based on an interpretable recurrent neural network architecture that enables for the relative analysis of sequence context features. We apply the framework to model twenty-six transcription factors and score the TF-DNA binding at a base-pair resolution. We find significant differences in activations of DNA context features for bound and unbound sequences. In addition to standardized evaluation protocols, we offer outstanding interpretability that enables us to identify and annotate DNA sequence with possible elements that modulate TF-DNA binding. Also, differences in data processing have a huge influence on the overall model performance. Overall, the proposed framework allows for novel insights on the non-coding genetic elements and their role in facilitating a stable TF-DNA interaction.

Computational Methods and Deep Learning for Elucidating Protein Interaction Networks.

Protein interactions play a critical role in all biological processes, but experimental identification of protein interactions is a time- and resource-intensive process. The advances in next-generation sequencing and multi-omics technologies have greatly benefited large-scale predictions of protein interactions using machine learning methods. A wide range of tools have been developed to predict protein-protein, protein-nucleic acid, and protein-drug interactions. Here, we discuss the applications, methods, and challenges faced when employing the various prediction methods. We also briefly describe ways to overcome the challenges and prospective future developments in the field of protein interaction biology.

Implementing accelerated dynamics to unravel the effects of high-fidelity Cas9 mutants on target DNA and guide RNA hybrid stability.

The clustered regularly interspersed short palindromic repeats (CRISPR) and its associated nuclease (Cas9) offers a unique and easily reprogrammable system for editing eukaryotic genomes. Cas9 is guided to the target by an RNA strand, and precise edits are created by introducing double-stranded breaks. However, nuclease activity of Cas9 is also triggered at other sites other than the target sit, which is a major limitation for various applications. Cas9 variants have been designed to improve the efficacy of the tool by introducing certain mutations. However, the on-target activity of such Cas9 variants is often seen as compromised. Hence, understanding the sub-molecular differences in the variants is essential to elucidate the factors that contribute to efficiency. The study reveals distortions in the PAM-distal regions of the nucleic hybrids as well as changes in the interactions between the Cas9 variants and RNA-DNA hybrid, contributing to the explanation for differences in on-target activity.Communicated by Ramaswamy H. Sarma.

Insights into the potential of withanolides as Phosphodiesterase-4 (PDE4D) inhibitors.

Medicinal herbs have been used as traditional medicines for centuries. The molecular mechanism of action of their bioactive molecules against various diseases or therapeutic targets is still being explored. Here, the active compounds (withanolides) of a well-known Indian medicinal herb, Ashwagandha (Withania somnifera), have been studied for their most potential therapeutic targets and their mechanism of action using ligand-based screening and receptor-based approaches. Ligand-based screening predicted the six top therapeutic targets, namely, Protein kinase C alpha (PRKCA), Protein kinase C delta (PRKCD), Protein kinase C epsilon (PRKCE), Androgenic Receptor (AR), Cycloxygenase-2 (PTGS-2) and Phosphodiesterase-4D (PDE4D). Further, when these predictions were validated using receptor-based studies, i.e. molecular docking, molecular dynamics simulation and free energy calculations, it was found that PDE4D was the most potent target for four withanolides, namely, Withaferin-A, 17-Hydroxywithaferin-A, 27-Hydroxywithanone and Withanolide-R. These compounds had a better binding affinity and similar interactions as that of an already known inhibitor (Zardaverine) of PDE4D. These results warrant further in-vitro and in-vivo investigations to examine their therapeutic potential as an inhibitor of PDE4D.Communicated by Ramaswamy H. Sarma.

Computational analysis of RNA methyltransferase Rv3366 as a potential drug target for combating drug-resistant Mycobacterium tuberculosis.

Mycobacterium tuberculosis (M.tb) remains a formidable global health threat. The increasing drug resistance among M.tb clinical isolates is exacerbating the current tuberculosis (TB) burden. In this study we focused on identifying novel repurposed drugs that could be further investigated as potential anti-TB drugs. We utilized M.tb RNA methyltransferase Rv3366 (spoU) as a potential drug target due to its imperative activity in RNA modification and no structural homology with human proteins. Using computational modeling approaches the structure of Rv3366 was determined followed by high throughput virtual screening of Food and Drug Administration (FDA) approved drugs to screen potential binders of Rv3366. Molecular dynamics (MD) simulations were performed to assess the drug-protein binding interactions, complex stability and rigidity. Through this multi-step structure-based drug repurposing workflow two promising inhibitors of Rv3366 were identified, namely, Levodopa and Droxidopa. This study highlights the significance of targeting M.tb RNA methyltransferases to combat drug-resistant M.tb. and proposes Levodopa and Droxidopa as promising inhibitors of Rv3366 for future pre-clinical investigations.

Evolution of SARS-CoV-2: BA.4/BA.5 Variants Continues to Pose New Challenges.

The acquisition of a high number of mutations, notably, the gain of two mutations L452R and F486V in RBD, and the ability to evade vaccine/natural infection-induced immunity suggests that Omicron is continuing to use "immune-escape potential" as an evolutionary space to maintain a selection advantage within the population. Despite the low hospitalizations and lower death rate, the surges by these variants may offset public health measures and disrupt health care facilities as seen recently in Portugal and the USA. Interestingly these BA.4/BA.5 variants have been found to be more severe than the earlier-emerged Omicron variants. We believe that aggressive COVID-19 surveillance using affordable testing strategies might actually help understand the evolution and transmission pattern of new variants. The sudden dip in reporting of new cases in some of the low- and middle-income countries is an alarming situation and needs to be addressed as this could lead to undetected transmission of future variants of interest/concern of SARS-CoV-2 in large population settings, including advent of a 'super' virus. It would be interesting to examine the possible role/influence, if any, of the two different kinds of vaccines, the spike protein-based versus the inactivated whole virus, in the evolution of BA.4/BA.5.