Structure-guided mutations in CDRs for enhancing the affinity of neutralizing SARS-CoV-2 nanobody.

The optimization of antibodies to attain the desired levels of affinity and specificity holds great promise for the development of next generation therapeutics. This study delves into the refinement and engineering of complementarity-determining regions (CDRs) through in silico affinity maturation followed by binding validation using isothermal titration calorimetry (ITC) and pseudovirus-based neutralization assays. Specifically, it focuses on engineering CDRs targeting the epitopes of receptor-binding domain (RBD) of the spike protein of SARS-CoV-2. A structure-guided virtual library of 112 single mutations in CDRs was generated and screened against RBD to select the potential affinity-enhancing mutations. Protein-protein docking analysis identified 32 single mutants of which nine mutants were selected for molecular dynamics (MD) simulations. Subsequently, biophysical ITC studies provided insights into binding affinity, and consistent with in silico findings, six mutations that demonstrated better binding affinity than native nanobody were further tested in vitro for neutralization activity against SARS-CoV-2 pseudovirus. Leu106Thr mutant was found to be most effective in virus-neutralization with IC50 values of ∼0.03 µM, as compared to the native nanobody (IC50 ∼0.77 µM). Thus, in this study, the developed computational pipeline guided by structure-aided interface profiles and thermodynamic analysis holds promise for the streamlined development of antibody-based therapeutic interventions against emerging variants of SARS-CoV-2 and other infectious pathogens.

StaBle-ABPpred: a stacked ensemble predictor based on biLSTM and attention mechanism for accelerated discovery of antibacterial peptides.

Due to the rapid emergence of multi-drug resistant (MDR) bacteria, existing antibiotics are becoming ineffective. So, researchers are looking for alternatives in the form of antibacterial peptides (ABPs) based medicines. The discovery of novel ABPs using wet-lab experiments is time-consuming and expensive. Many machine learning models have been proposed to search for new ABPs, but there is still scope to develop a robust model that has high accuracy and precision. In this work, we present StaBle-ABPpred, a stacked ensemble technique-based deep learning classifier that uses bidirectional long-short term memory (biLSTM) and attention mechanism at base-level and an ensemble of random forest, gradient boosting and logistic regression at meta-level to classify peptides as antibacterial or otherwise. The performance of our model has been compared with several state-of-the-art classifiers, and results were subjected to analysis of variance (ANOVA) test and its post hoc analysis, which proves that our model performs better than existing classifiers. Furthermore, a web app has been developed and deployed at https://stable-abppred.anvil.app to identify novel ABPs in protein sequences. Using this app, we identified novel ABPs in all the proteins of the Streptococcus phage T12 genome. These ABPs have shown amino acid similarities with experimentally tested antimicrobial peptides (AMPs) of other organisms. Hence, they could be chemically synthesized and experimentally validated for their activity against different bacteria. The model and app developed in this work can be further utilized to explore the protein diversity for identifying novel ABPs with broad-spectrum activity, especially against MDR bacterial pathogens.

Accelerating the discovery of antifungal peptides using deep temporal convolutional networks.

The application of machine intelligence in biological sciences has led to the development of several automated tools, thus enabling rapid drug discovery. Adding to this development is the ongoing COVID-19 pandemic, due to which researchers working in the field of artificial intelligence have acquired an active interest in finding machine learning-guided solutions for diseases like mucormycosis, which has emerged as an important post-COVID-19 fungal complication, especially in immunocompromised patients. On these lines, we have proposed a temporal convolutional network-based binary classification approach to discover new antifungal molecules in the proteome of plants and animals to accelerate the development of antifungal medications. Although these biomolecules, known as antifungal peptides (AFPs), are part of an organism's intrinsic host defense mechanism, their identification and discovery by traditional biochemical procedures is arduous. Also, the absence of a large dataset on AFPs is also a considerable impediment in building a robust automated classifier. To this end, we have employed the transfer learning technique to pre-train our model on antibacterial peptides. Subsequently, we have built a classifier that predicts AFPs with accuracy and precision of 94%. Our classifier outperforms several state-of-the-art models by a considerable margin. The results of its performance were proven as statistically significant using the Kruskal-Wallis H test, followed by a post hoc analysis performed using the Tukey honestly significant difference (HSD) test. Furthermore, we identified potent AFPs in representative animal (Histatin) and plant (Snakin) proteins using our model. We also built and deployed a web app that is freely available at https://tcn-afppred.anvil.app/ for the identification of AFPs in protein sequences.

Antimicrobial and anti-viral effects of selenium nanoparticles and selenoprotein based strategies: COVID-19 and beyond.

Deficiency of selenium (Se) has been described in a significant number of COVID-19 patients having a higher incidence of mortality, which makes it a pertinent issue to be addressed clinically for effective management of the COVID-19 pandemic. Se nanoparticles (SeNPs) provide a unique option for managing the havoc caused by the COVID-19 pandemic. SeNPs possess promising anti-inflammatory and anti-fibrotic effects by virtue of their nuclear factor kappa-light-chain-stimulator of activated B cells (NFκB), mitogen-activated protein kinase (MAPKs), and transforming growth factor-beta (TGF-ß) modulatory activity. In addition, SeNPs possess remarkable immunomodulatory effects, making them a suitable option for supplementation with a much lower risk of toxicity compared to their elemental counterpart. Further, SeNPs have been shown to curtail viral and microbial infections, thus, making it a novel means to halt viral growth. In addition, it can be administered in the form of aerosol spray, direct injection, or infused thin-film transdermal patches to reduce the spread of this highly contagious viral infection. Moreover, a considerable decrease in the expression of selenoprotein along with enhanced expression of IL-6 in COVID-19 suggests a potential association among selenoprotein expression and COVID-19. In this review, we highlight the unique antimicrobial and antiviral properties of SeNPs and the immunomodulatory potential of selenoproteins. We provide the rationale behind their potentially interesting properties and further exploration in the context of microbial and viral infections. Further, the importance of selenoproteins and their role in maintaining a successful immune response along with their association to Se status is summarized.

A comprehensive overview of CRISPR/Cas 9 technology and application thereof in drug discovery.

Clustered Regularly Interspaced Short Palindromic Repeat (CRISPR)-Cas technology possesses revolutionary potential to positively affect various domains of drug discovery. It has initiated a rise in the area of genetic engineering and its advantages range from classical science to translational medicine. These genome editing systems have given a new dimension to our capabilities to alter, detect and annotate specified gene sequences. Moreover, the ease, robustness and adaptability of the CRISPR/Cas9 technology have led to its extensive utilization in research areas in such a short period of time. The applications include the development of model cell lines, understanding disease mechanisms, discovering disease targets, developing transgenic animals and plants, and transcriptional modulation. Further, the technology is rapidly growing; hence, an overlook of progressive success is crucial. This review presents the current status of the CRISPR-Cas technology in a tailor-made format from its discovery to several advancements for drug discovery alongwith future trends associated with possibilities and hurdles including ethical concerns.

Betulinic acid attenuates lung injury by modulation of inflammatory cytokine response in experimentally-induced polymicrobial sepsis in mice.

Sepsis commonly progresses to acute lung injury (ALI), an inflammatory lung disease with high morbidity and mortality. Septic ALI is characterized by excessive production of proinflammatory mediators. It remained refractory to present therapies and new therapies need to be developed to improve further clinical outcomes. Betulinic acid (BA), a pentacyclic lupane group triterpenoid has been shown to have anti-inflammatory activities in many studies. However, its therapeutic efficacy in polymicrobial septic ALI is yet unknown. Therefore, we investigated the effects of BA on septic ALI using cecal ligation and puncture (CLP) model in mice. Vehicle or BA (3, 10, and 30mg/kg) was administered intraperitoneally, 3 times (0, 24 and 48h) before CLP and CLP was done on 49(th)h of the study. Survival rate was observed till 120h post CLP. Lung tissues were collected for analysis by sacrificing mice 18h post CLP. BA at 10 and 30mg/kg dose significantly reduced sepsis-induced mortality and lung injury as implied by attenuated lung histopathological changes, decreased protein and neutrophils infiltration. BA also decreased lung NF-κB expression, cytokine, intercellular adhesion molecule-1, monocyte chemoattractant protein-1 and matrix metalloproteinase-9 levels. These evidences suggest that, the protective effects of BA on lungs are associated with defending action against inflammatory response and BA could be a potential modulatory agent of inflammation in sepsis-induced ALI.

Daidzein pretreatment improves survival in mouse model of sepsis.

BACKGROUND: The aim of the present study was to assess the effect of seven days daidzein pretreatment in cecal ligation and puncture (CLP) model of sepsis. METHODS: We assessed the survival benefit of daidzein and its effect on lung injury in CLP-induced sepsis in mice and determined the bacterial load in peritoneal fluid, blood, and lung homogenates. Tumor necrosis factor α (TNF-α) and corticosterone levels were measured by enzyme-linked immunosorbent assay; relative mRNA expression was estimated by real-time polymerase chain reaction, and standard biochemical techniques were used to measure nitrite level, myeloperoxidase activity, and vascular permeability. RESULTS: Daidzein pretreatment for seven days at a dose of 1 mg/kg body weight subcutaneously increased the survival time of septic mice. Daidzein decreased the bacterial load in peritoneal fluid, blood, and lungs, reduced the tumor necrosis factor α and nitrite level in plasma, and partially suppressed lung injury by reducing vascular permeability and myeloperoxidase activity in septic mice. Further, it restored the relative mRNA expressions of inducible nitric oxide synthase, glucocorticoid receptor α, and glucocorticoid receptor ß genes in septic lungs were restored by daidzein pretreatment. CONCLUSIONS: Daidzein pretreatment for 7 d in sepsis increased the survival time in mice, which may be relate to decrease in bacterial load, anti-inflammatory effect, and protection from lung injury.

A novel framework based on explainable AI and genetic algorithms for designing neurological medicines.

The advent of the fourth industrial revolution, characterized by artificial intelligence (AI) as its central component, has resulted in the mechanization of numerous previously labor-intensive activities. The use of in silico tools has become prevalent in the design of biopharmaceuticals. Upon conducting a comprehensive analysis of the genomes of many organisms, it has been discovered that their tissues can generate specific peptides that confer protection against certain diseases. This study aims to identify a selected group of neuropeptides (NPs) possessing favorable characteristics that render them ideal for production as neurological biopharmaceuticals. Until now, the construction of NP classifiers has been the primary focus, neglecting to optimize these characteristics. Therefore, in this study, the task of creating ideal NPs has been formulated as a multi-objective optimization problem. The proposed framework, NPpred, comprises two distinct components: NSGA-NeuroPred and BERT-NeuroPred. The former employs the NSGA-II algorithm to explore and change a population of NPs, while the latter is an interpretable deep learning-based model. The utilization of explainable AI and motifs has led to the proposal of two novel operators, namely p-crossover and p-mutation. An online application has been deployed at https://neuropred.anvil.app for designing an ideal collection of synthesizable NPs from protein sequences.

TRPV4 channel activation leads to endothelium-dependent relaxation mediated by nitric oxide and endothelium-derived hyperpolarizing factor in rat pulmonary artery.

The purpose of the present study was to characterize TRPV4 channels in the rat pulmonary artery and examine their role in endothelium-dependent relaxation. Tension, Real-Time polymerase chain reaction (Real-Time PCR) and Western blot experiments were conducted on left and right branches of the main pulmonary artery from male Wistar rats. TRPV4 channel agonist GSK1016790A (GSK) caused concentration-related robust relaxation (Emax 88.6±5.5%; pD2 8.7±0.2) of the endothelium-intact pulmonary artery. Endothelium-denudation nearly abolished the relaxation (Emax 5.6±1.3%) to GSK. TRPV4 channel selective antagonist HC067047 significantly attenuated GSK-induced relaxation (Emax 56.2±6.6% vs. control Emax 87.9±3.3%) in endothelium-intact vessels, but had no effect on either ACh-induced endothelium-dependent or SNP-induced endothelium-independent relaxations. GSK-induced relaxations were markedly inhibited either in the presence of NO synthase inhibitor L-NAME (Emax 8.5±2.7%) or sGC inhibitor ODQ (Emax 28.1±5.9%). A significant portion (Emax 30.2±4.4%) of endothelium-dependent relaxation still persisted in the combined presence of L-NAME and cyclooxygenase inhibitor indomethacin. This EDHF-mediated relaxation was sensitive to inhibition by 60mM K(+) depolarizing solution or K(+) channel blockers apamin (SKCa; KCa2.3) and TRAM-34 (IKCa; KCa3.1). GSK (10(-10)-10(-7)M) caused either modest decrease or increase in the basal tone of endothelium-intact or denuded rings, respectively. We found a greater abundance (>1.5 fold) of TRPV4 mRNA and protein expressions in endothelium-intact vs. denuded vessels, suggesting the presence of this channel in pulmonary endothelial and smooth muscle cells as well. The present study demonstrated that NO and EDHF significantly contributed to TRPV4 channel-mediated endothelium-dependent relaxation of the rat pulmonary artery.

A separable temporal convolutional networks based deep learning technique for discovering antiviral medicines.

An alarming number of fatalities caused by the COVID-19 pandemic has forced the scientific community to accelerate the process of therapeutic drug discovery. In this regard, the collaboration between biomedical scientists and experts in artificial intelligence (AI) has led to a number of in silico tools being developed for the initial screening of therapeutic molecules. All living organisms produce antiviral peptides (AVPs) as a part of their first line of defense against invading viruses. The Deep-AVPiden model proposed in this paper and its corresponding web app, deployed at https://deep-avpiden.anvil.app , is an effort toward discovering novel AVPs in proteomes of living organisms. Apart from Deep-AVPiden, a computationally efficient model called Deep-AVPiden (DS) has also been developed using the same underlying network but with point-wise separable convolutions. The Deep-AVPiden and Deep-AVPiden (DS) models show an accuracy of 90% and 88%, respectively, and both have a precision of 90%. Also, the proposed models were statistically compared using the Student's t-test. On comparing the proposed models with the state-of-the-art classifiers, it was found that they are much better than them. To test the proposed model, we identified some AVPs in the natural defense proteins of plants, mammals, and fishes and found them to have appreciable sequence similarity with some experimentally validated antimicrobial peptides. These AVPs can be chemically synthesized and tested for their antiviral activity.

Computational guided identification of novel potent inhibitors of N-terminal domain of nucleocapsid protein of severe acute respiratory syndrome coronavirus 2.

The Coronavirus Disease 2019, caused by the severe acute respiratory syndrome coronavirus 2 is an exceptionally contagious disease that leads to global epidemics with elevated mortality and morbidity. There are currently no efficacious drugs targeting coronavirus disease 2019, therefore, it is an urgent requirement for the development of drugs to control this emerging disease. Owing to the importance of nucleocapsid protein, the present study focuses on targeting the N-terminal domain of nucleocapsid protein from severe acute respiratory syndrome coronavirus 2 to identify the potential compounds by computational approaches such as pharmacophore modeling, virtual screening, docking and molecular dynamics. We found three molecules (ZINC000257324845, ZINC000005169973 and ZINC000009913056), which adopted a similar conformation as guanosine monophosphate (GMP) within the N-terminal domain active site and exhibiting high binding affinity (>-8.0 kcalmol-1). All the identified compounds were stabilized by hydrogen bonding with Arg107, Tyr111 and Arg149 of N-terminal domain. Additionally, the aromatic ring of lead molecules formed π interactions with Tyr109 of N-terminal domain. Molecular dynamics and Molecular mechanic/Poisson-Boltzmann surface area results revealed that N-terminal domain - ligand(s) complexes are less dynamic and more stable than N-terminal domain - GMP complex. As the identified compounds share the same corresponding pharmacophore properties, therefore, the present results may serve as a potential lead for the development of inhibitors against severe acute respiratory syndrome coronavirus 2. Communicated by Ramaswamy H. Sarma.

In-silico functional and structural annotation of hypothetical protein from Klebsiella pneumonia: A potential drug target.

Klebsiella pneumonia is known to cause several nosocomial infections in immunocompromised patients. It has developed resistance against a broad range of presently available antibiotics, resulting in high mortality rates in patients and declared an urgent threat. Therefore, exploration of possible novel drug targets against this opportunistic bacteria needs to be undertaken. In the present study, we performed an extensive in-silico analysis for functional and structural annotation and characterized HP CP995_08280 from K. pneumonia as a drug target and aimed to identify potent drug candidates. The functional and structural studies using several bioinformatics tools and databases predicted that HP CP995_08280 is a cytosolic protein that belongs to the ß-lactamase family and shares structural similarity with FmtA protein from Staphylococcus aureus (PDB ID: 5ZH8). The structure of HP CP995_08280 was successfully modeled followed by structure-based virtual screening, docking, molecular dynamics, and Molecular mechanic/Poisson-Boltzmann surface area (MMPBSA) were performed to identify the potential compounds. We have found five potent antibacterial molecules, namely BDD 24083171, BDD 24085737, BDE 25098678, BDE 33638819, and BDE 33672484, which exhibited high binding affinity (>-7.5 kcal/mol) and were stabilized by hydrogen bonding and hydrophobic interactions with active site residues (Ser42, Lys45, Tyr126, and Asp128) of protein. Molecular dynamics and MMPBSA revealed that HP CP995_08280 - ligand(s) complexes were less dynamic and more stable than native HP CP995_08280. Hence, the present study may serve as a potential lead for developing inhibitors against drug-resistant Klebsiella pneumonia.

Drug-Repurposing Approach To Combat Staphylococcus aureus: Biomolecular and Binding Interaction Study.

Staphylococcus aureus is considered as one of the most widespread bacterial pathogens and continues to be a prevalent cause of mortality and morbidity across the globe. FmtA is a key factor linked with methicillin resistance in S. aureus. Consequently, new antibacterial compounds are crucial to combat S. aureus resistance. Here, we present the virtual screening of a set of compounds against the available crystal structure of FmtA. The findings indicate that gemifloxacin, paromomycin, streptomycin, and tobramycin were the top-ranked potential drug molecules based on the binding affinity. Furthermore, these drug molecules were analyzed with molecular dynamics simulations, which showed that the identified molecules formed highly stable FmtA-inhibitor(s) complexes. Molecular mechanics Poisson-Boltzmann surface area and quantum mechanics/molecular mechanics calculations suggested that the active site residues (Ser127, Lys130, Tyr211, and Asp213) of FmtA are crucial for the interaction with the inhibitor(s) to form stable protein-inhibitor(s) complexes. Moreover, fluorescence- and isothermal calorimetry-based binding studies showed that all the molecules possess dissociation constant values in the micromolar scale, revealing a strong binding affinity with FmtAΔ80, leading to stable protein-drug(s) complexes. The findings of this study present potential beginning points for the rational development of advanced, safe, and efficacious antibacterial agents targeting FmtA.

Gene therapy: Comprehensive overview and therapeutic applications.

Gene therapy is the product of man's quest to eliminate diseases. Gene therapy has three facets namely, gene silencing using siRNA, shRNA and miRNA, gene replacement where the desired gene in the form of plasmids and viral vectors, are directly administered and finally gene editing based therapy where mutations are modified using specific nucleases such as zinc-finger nucleases (ZFNs), transcription activator-like effector nucleases (TALENs) and clustered regulatory interspaced short tandem repeats (CRISPR)/CRISPR-associated protein (Cas)-associated nucleases. Transfer of gene is either through transformation where under specific conditions the gene is directly taken up by the bacterial cells, transduction where a bacteriophage is used to transfer the genetic material and lastly transfection that involves forceful delivery of gene using either viral or non-viral vectors. The non-viral transfection methods are subdivided into physical, chemical and biological. The physical methods include electroporation, biolistic, microinjection, laser, elevated temperature, ultrasound and hydrodynamic gene transfer. The chemical methods utilize calcium- phosphate, DAE-dextran, liposomes and nanoparticles for transfection. The biological methods are increasingly using viruses for gene transfer, these viruses could either integrate within the genome of the host cell conferring a stable gene expression, whereas few other non-integrating viruses are episomal and their expression is diluted proportional to the cell division. So far, gene therapy has been wielded in a plethora of diseases. However, coherent and innocuous delivery of genes is among the major hurdles in the use of this promising therapy. Hence this review aims to highlight the current options available for gene transfer along with the advantages and limitations of every method.

Biomedical applications of polysaccharide nanoparticles for chronic inflammatory disorders: Focus on rheumatoid arthritis, diabetes and organ fibrosis.

Polysaccharides are biopolymers distinguished by their complex secondary structures executing various roles in microorganisms, plants, and animals. They are made up of long monomers of similar type or as a combination of other monomeric chains. Polysaccharides are considered superior as compared to other polymers due to their diversity in charge and size, biodegradability, abundance, bio-compatibility, and less toxicity. These natural polymers are widely used in designing of nanoparticles (NPs) which possess wide applications in therapeutics, diagnostics, delivery and protection of bioactive compounds or drugs. The side chain reactive groups of polysaccharides are advantageous for functionalization with nanoparticle-based conjugates or therapeutic agents such as small molecules, proteins, peptides and nucleic acids. Polysaccharide NPs show excellent pharmacokinetic and drug delivery properties, facilitate improved oral absorption, control the release of drugs, increases in vivo retention capability, targeted delivery, and exert synergistic effects. This review updates the usage of polysaccharides based NPs particularly cellulose, chitosan, hyaluronic acid, alginate, dextran, starch, cyclodextrins, pullulan, and their combinations with promising applications in diabetes, organ fibrosis and arthritis.

Structure-Based Identification of Potential Drugs Against FmtA of Staphylococcus aureus: Virtual Screening, Molecular Dynamics, MM-GBSA, and QM/MM.

Staphylococcus aureus is resistant to ß-lactam antibiotics and causes several skin diseases to life-threatening diseases. FmtA is found to be one of the main factors involved in methicillin resistance in S. aureus. FmtA exhibits an esterase activity that removes the D-Ala from teichoic acid. Teichoic acids played a significant role in cell wall synthesis, cell division, colonization, biofilm formation, virulence, antibiotic resistance, and pathogenesis. The virtual screening of drug molecules against the crystal structure of FmtA was performed and the binding affinities of top three molecules (ofloxacin, roflumilast, and furazolidone) were predicted using molecular docking. The presence of positive potential and electron affinity regions in screened drug molecules by DFT analysis illustrated that these molecules are reactive in nature. The protein-ligand complexes were subjected to molecular dynamics simulation. Molecular dynamics analysis such as RMSD, RMSF, Rg, SASA, PCA, and FEL results suggested that FmtA-drug(s) complexes are stable. MM-GBSA binding affinity and QM/MM results (ΔG, ΔH, and ΔS) revealed that active site residues (Ser127, Lys130, Tyr211, Asp213, and Asn343) of FmtA played an essential for the binding of the drug(s) to form a lower energy stable protein-ligand complexes. FmtAΔ42 was purified using cation exchange and gel filtration chromatography. Fluorescence spectroscopy and circular dichroism results showed that interactions of drugs with FmtAΔ42 affect the tertiary structure and increase the thermostability of the protein. The screened molecules need to be tested and could be further modified to develop the antimicrobial compounds against S. aureus.

Structure of dye-decolorizing peroxidase from Bacillus subtilis in complex with veratryl alcohol.

Dye-decolorizing peroxidases (DyPs) are heme-containing peroxidases, which have promising application in biodegradation of phenolic lignin compounds and in detoxification of dyes. In this study, the crystal structure of BsDyP- veratryl alcohol (VA) complex delves deep into the binding of small substrate molecules within the DyP heme cavity. The biochemical analysis shows that BsDyP oxidizes the VA with a turnover number of 0.065 s-1, followed by the oxidation of 2,6-dimethoxyphenol (DMP) and guaiacol with a comparable turnover number (kcat) of 0.07 s-1 and 0.07 s-1, respectively. Moreover, biophysical and computational studies reveal the comparable binding affinity of substrates to BsDyP and produce lower-energy stable BsDyP-ligand(s) complexes. All together with our previous findings, we are providing a complete structural description of substrate-binding sites in DyP. The structural insight of BsDyP helps to modulate its engineering to enhance the activity towards the oxidation of a wide range of substrates.

Multifunctional inhibitors of SARS-CoV-2 by MM/PBSA, essential dynamics, and molecular dynamic investigations.

The ongoing COVID-19 pandemic demands a novel approach to combat and identify potential therapeutic targets. The SARS-CoV-2 infection causes a hyperimmune response followed by a spectrum of diseases. Limonoids are a class of triterpenoids known to prevent the release of IL-6, IL-15, IL-1α, IL-1ß via TNF and are also known to modulate PI3K/Akt/GSK-3ß, JNK1/2, MAPKp38, ERK1/2, and PI3K/Akt/mTOR signaling pathways and could help to avoid viral infection, persistence, and pathogenesis. The present study employs a computational approach of virtual screening and molecular dynamic (MD) simulations of such compounds against RNA-dependent RNA polymerase (RdRp), Main protease (Mpro), and Papain-like protease (PLpro) of SARS-CoV-2. MD simulation, Molecular Mechanics Poisson-Boltzmann Surface Area (MM/PBSA), and Essential dynamics revealed that the macromolecule-ligand complexes are stable with very low free energy of binding. Such compounds that could modulate both host responses and inhibit viral machinery could be beneficial in effectively controlling the global pandemic.

Emerging Role of PD-1/PD-L1 Inhibitors in Chronic Liver Diseases.

Programmed cell death protein 1 (PD-1)/PD-ligand (L)1, the immune checkpoint inhibitors have emerged as a promising strategy for the treatment of various diseases including chronic liver diseases (CLDs) such as hepatitis, liver injury and hepatocellular carcinoma (HCC). The role of PD-1/PD-L1 has been widely inspected in the treatment of viral hepatitis and HCC. PD-1 is known to play a crucial role in inhibiting immunological responses and stimulates self-tolerance by regulating the T-cell activity. Further, it promotes apoptosis of antigen-specific T-cells while preventing apoptosis of Treg cells. PD-L1 is a trans-membrane protein which is recognized as a co-inhibitory factor of immunological responses. Both, PD-1 and PD-L1 function together to downregulate the proliferation of PD-1 positive cells, suppress the expression of cytokines and stimulate apoptosis. Owing to the importance of PD-1/PD-L1 signaling, this review aims to summarize the potential of PD-1/PD-L1 inhibitors in CLDs along with toxicities associated with them. We have enlisted some of the important roles of PD-1/PD-L1 in CLDs, the clinically approved products and the pipelines of drugs under clinical evaluation.

Critical neurological features of COVID-19: Role of imaging methods and biosensors for effective diagnosis.

COVID-19 is a respiratory infection that has been declared as a global health crisis by the WHO. It mainly affects the respiratory system. Apart from respiratory system, it also affects other organs as well including the brain. Numerous emerging reports have demonstrated that the COVID-19 has detrimental effects on neurological functions, and can lead to severe impairment of the central nervous system (CNS). The neurological manifestations linked with COVID-19 include headache, anosmia, encephalitis, epileptic seizures, Guillain-Barre syndrome, stroke and intracerebral hemorrhage alongwith multiple others complications. The CNS related complications may be severe and are linked with poor diagnosis which may worsen the condition. Therefore, there is a need to precisely understand the neurological sequelae along with upcoming clinical outcomes. Here, we present a brief review of the neurological complications and symptoms associated with COVID-19 along with brain imaging findings. Further, we have discussed about the emerging biosensing approaches which may aid in rapid, precise and mass diagnosis of COVID-19.