Computational drug repurposing study elucidating simultaneous inhibition of entry and replication of novel corona virus by Grazoprevir

Exploring the rare variants associated with Type 2 Diabetes Mellitus in Indian population and its disease-drug association studies: an in-silico approach

The diversified eating habits and religious culture of Indian population may be one of the reasons they largely contribute to the global diabetes burden. In the present investigation, an in-silico approach was carried out to explore hub genes in the Indian population with Type 2 Diabetes Mellitus (T2DM) that are scantily reported in the GWAS catalogue and probable potential anti-diabetic drugs from plants. This computational approach unwrapped LEP (leptin) as the hub gene among 170 genes analyzed with 14 non-synonymous single nucleotide polymorphisms (nsSNPs) with MAF < 0.01. The mutation of the LEP gene leads to a decrease in leptin concentration, which increases the risk of obesity and T2DM. According to the DUET webserver, 11 of 14 mutations examined were found to destabilize the LEP protein. Among 14, four barely reported LEP variants rs781301976 (I45N), rs776443424 (S52F), rs200915360 (D76Y), and rs1191666811 (D162N) were unzipped to be associated with T2DM, which may be the probable potential drug targets. The virtual screening revealed Vescalagin as having the highest binding energy among 336 natural compounds. Molecular docking of Vescalagin depicted higher binding energy (-9.0 kcal/mol) against mutated LEP [rs200915360 (D76Y)] compared to wild (-8.9 kcal/mol) and LEP-Metformin complexes. The trajectory analysis of MD simulations revealed that Vescalagin was more effective than Metformin in stabilizing the system. The present study suggests that the associations of the investigated nsSNPs in LEP [rs200915360 (D76Y)] and others can be key factors in the predominant role of T2DM morbidity in the Indian population that can be used as potential markers and drug targets for T2DM therapeutics.Communicated by Ramaswamy H. Sarma.

Genetic heterogeneity and respiratory chain enzyme analysis in pediatric Indian patients with mitochondrial disorder: Report of novel variants in POLG1 gene and their functional implication using molecular dynamic simulation

Mitochondrial disorders are a heterogeneous group of disorders caused by mutations in the mitochondrial DNA or in nuclear genes encoding the mitochondrial proteins and subunits. Polymerase Gamma (POLG) is a nuclear gene and mutation in the POLG gene are one of the major causes of inherited mitochondrial disorders. In this study, 15 pediatric patients, with a wide spectrum of clinical phenotypes were screened using blood samples (n = 15) and muscle samples (n = 4). Respiratory chain enzyme analysis in the muscle samples revealed multi-complex deficiencies with Complex I deficiency present in (1/4) patients, Complex II (2/4), Complex III (3/4) and Complex IV (2/4) patients. Multiple large deletions were observed in 4/15 patients using LR-PCR. Whole exome sequencing (WES) revealed a compound heterozygous mutation consisting of a POLG1 novel variant (NP_002684.1:p.Trp261X) and a missense variant (NP_002684.1:p. Leu304Arg) in one patient and another patient harboring a novel homozygous POLG1 variant (NP_002684.1:p. Phe750Val). These variants (NP_002684.1:p. Leu304Arg) and (NP_002684.1:p. Phe750Val) and their interactions with DNA were modelled using molecular docking and molecular dynamics (MD) simulation studies. The protein conformation was analyzed as root mean square deviation (RMSD), root mean square fluctuation (RMSF) which showed local fluctuations in the mutants compared to the wildtype. However, Solvent Accessible Surface Area (SASA) significantly increased for NP_002684.1:p.Leu304Arg and decreased in NP_002684.1:p.Phe750Val mutants. Further, Contact Order analysis indicated that the Aromatic-sulfur interactions were destabilizing in the mutants. Overall, these in-silico analysis has revealed a destabilizing mutations suggesting pathogenic variants in POLG1 gene.

Pharmacoinformatics approach for type 2 diabetes mellitus therapeutics using phytocompounds from Costus genus: an in-silico investigation

Type 2 Diabetes Mellitus (T2DM), as a significant health concern globally, particularly in India, underscoring the vital need for effective therapeutics. Current drug therapies for T2DM may have limitations, leading researchers to explore natural products as alternatives. In this study. We have investigated the anti-diabetic compounds from the Costus genus, known as the insulin plant, which is abundant in southern India. The bioinformatics tools and software used for in-silico analysis to identify potential therapeutic compounds and hub genes associated with T2DM in the Indian population that could cut short the in-vitro and in-vivo experimental approaches in near future. The systematic review and combinatorial in-silico analysis revealed IGF2BP2, INS and TCF as the key targets that are associated with T2DM. The compounds stigmasterol, cycloartenol, and diosgenone were explored to be potent among all the 38 phytocompounds from genus Costus with binding energies -8.48, -10.07, and -10.31 kcal/mol against IGF2BP2, INS and TCF. The molecular dynamics (MD) simulation studies of these complexes demonstrated stable and consistent dynamic behavior, particularly in the INS-cycloartenol, IGF2BP2-stigmasterol and TCF7L2-diosgenone complexes. The identified compounds and associated targets represent potential candidates for T2DM therapeutics in the Indian population. The pharmacoinformatics approach presented in the study could streamline the drug discovery process by prioritizing compounds for further experimental validation.Communicated by Ramaswamy H. Sarma.

Targeting RdRp of SARS-CoV-2 with De Novo Molecule Generation

Viruses are known for their extremely high mutation rates, allowing them to evade both the human immune system and many forms of standard medicine. Despite this, the RNA dependent RNA polymerase (RdRp) of the RNA viruses has been largely conserved, and any significant mutation of this protein is unlikely. The recent COVID-19 pandemic presents a need for therapeutics. We have designed a de novo drug design algorithm that generates strong binding ligands from scratch, based on only the structure of the target protein's receptor. In this paper, we applied our method to target SARS-CoV-2 RdRp and generated several de novo molecules. We then chose some drug molecules based on the structural similarity to some of our strongest binding de novo molecules. Subsequently, we showed, using rigorous all-atom explicit-water free energy calculations in near-microsecond time scales using state-of-the-art well-tempered metadynamics simulations, that some of our de novo generated ligands bind more strongly to RdRp than the recent FDA approved drug remdesivir in its active form, remdesivir triphosphate (RTP). We elucidated the binding mechanism for some of the top binders and compared it with RTP. We believe that this work will be useful both by presenting lead structures for RdRp inhibition and by delivering key insights into the residues of the protein potentially involved in the binding/unbinding of these small molecule drugs, leading to more targeted studies in the future.

Docking and molecular dynamic simulations of Mithramycin-A and Tolfenamic acid against Sp1 and survivin

Therapeutic targeting of Sp1 transcription factor and survivin, are studied in various cancers due to their consistent overexpression. These markers result in poorer cancer prognoses and their downregulation has been investigated as an effective treatment approach. Mithramycin-A and Tolfenamic acid are two drugs with innate anti-cancer properties and are suggested to be able to target Sp1 through GC/GT DNA binding interference, however in-depth binding and mechanistic studies are lacking. Through docking analysis, we investigated Mithramycin-A and Tolfenamic acid in terms of their specific binding interactions with Sp1 and survivin. Through further molecular dynamics simulations including Root Mean Square (RMS) Fluctuation and RMS Deviation, rGYr, and H-bond analysis, we identified critical residues involved in drug interactions with each protein in question. We show Mithramycin-A as the superior binding candidate to each protein and found that it exhibited stronger binding with Sp1, and then survivin. Subsequent molecular dynamics simulations followed the same trend as initial binding energy calculations and showed crucial amino acids involved in each Mithramycin-A-protein complex. Our findings warrant further investigation into Mithramycin-A and its specific interaction with Sp1 and their downstream targets giving a better understanding of Mithramycin-A and its potential as an effective cancer treatment.

Drug Repositioning: A Monetary Stratagem to Discover a New Application of Drugs

Drug repurposing, also referred to as drug repositioning or drug reprofiling, is a scientific approach to the detection of any new application for an already approved or investigational drug. It is a useful policy for the invention and development of new pharmacological or therapeutic applications of different drugs. The strategy has been known to offer numerous advantages over developing a completely novel drug for certain problems. Drug repurposing has numerous methodologies that can be categorized as target-oriented, drug-oriented, and problem-oriented. The choice of the methodology of drug repurposing relies on the accessible information about the drug molecule and like pharmacokinetic, pharmacological, physicochemical, and toxicological profile of the drug. In addition, molecular docking studies and other computer-aided methods have been known to show application in drug repurposing. The variation in dosage for original target diseases and novel diseases presents a challenge for researchers of drug repurposing in present times. The present review critically discusses the drugs repurposed for cancer, covid-19, Alzheimer's, and other diseases, strategies, and challenges of drug repurposing. Moreover, regulatory perspectives related to different countries like the United States (US), Europe, and India have been delineated in the present review.

In Silico Analysis Determining the Binding Interactions of NAD(P)H: Quinone Oxidoreductase 1 and Resveratrol via Docking and Molecular Dynamic Simulations

Objective

NAD(P)H: Quinone oxidoreductase1 (NQO1) plays a crucial role in cellular defense against oxidative stress. Overexpression of NQO1 is linked to various cancer pathways. Despite its potential, the actual mechanisms to inhibit NQO1 and increase the efficacy of standard therapeutic options are not yet established. Resveratrol is an anti-cancer polyphenol found in dietary products and red wine. The objective of this investigation is to employ in silico methods to explore how resveratrol interacts with NQO1.

Materials and Methods

Docking analysis of resveratrol against NQO1 was performed using Glide. The most efficiently docked complex was characterized and analyzed by measuring intermolecular (IM) hydrogen (H)-bonds and binding energy values, additional hydrophobic, and electrostatic interactions. IM interaction between complexed protein and compound was demonstrated using LigPlot+ and the Schrödinger ligand interaction module. Molecular dynamics tools were employed to examine the physical movement of molecules to evaluate how macromolecular structures relate to their functions.

Results

The results of this investigation depicted a strong affinity of resveratrol against NQO1 followed by MD simulations (NQO1-resveratrol complex-binding energy: -2.847kcal/mol). Resveratrol's robust binding affinity through docking and molecular dynamic simulations highlights a significant change around 90 ns. The H-bonds number was inversely linked with the resveratrol-NQO1 complex stability. The NQO1-Resveratrol complex displayed dynamic motion, as revealed by porcupine projections, indicating alterations in its movement and flexibility.

Conclusion

The present in silico analysis suggests a possible alteration in resveratrol's orientation in the protein binding pocket. The findings encourage further investigation, including validation using in vitro and in vivo assays.

Diaryl ether derivative inhibits GPX4 expression levels to induce ferroptosis in thyroid cancer cells

Papillary thyroid carcinoma contributes to about 80% of the total thyroid cancer cases. BRAFV600E is a frequently occurring mutation in PTCs. Although several BRAF inhibitors are available, many thyroid cancer patients acquire resistance to BRAF inhibitors. Therefore, new targets and drugs need to be identified as therapies. Ferroptosis is a recently discovered type of cell death, and inhibiting glutathione peroxidase 4 (GPX4) using small molecules was found to trigger ferroptosis. But it is unknown whether inhibiting GPX4 renders thyroid cancer cells susceptible to ferroptosis. To identify novel GPX4 inhibitors, we focused on our previously reported cohort of diaryl ether and dibenzoxepine molecules. In this study, we asked whether diaryl ether and dibenzoxepine derivatives trigger ferroptosis in thyroid cancer cells. To answer this question, we screened diaryl ether and dibenzoxepine derivatives in cell-based assays and performed mechanism of action studies. We found that a diaryl ether derivative, 16 decreased thyroid cell proliferation and triggered ferroptosis by inhibiting GPX4 expression levels. Molecular modeling and dynamics simulations showed that 16 binds to the active site of GPX4. Upon deciphering the mode of 16-induced ferroptosis, we found that 16 treatments decrease mitochondrial polarization and reduce mitochondrial respiration similar to a ferroptosis inducer, RSL3. We conclude that the diaryl ether derivative, 16 inhibits GPX4 expression levels to induce ferroptosis in thyroid cancer cells. Based on our observations, we suggest that 16 can be lead-optimized and developed as a ferroptosis-inducing agent to treat thyroid cancers.

Thiazolidinedione-triazole conjugates: design, synthesis and probing of the α-amylase inhibitory potential

Aim: The primary objective of this investigation was the synthesis, spectral interpretation and evaluation of the α-amylase inhibition of rationally designed thiazolidinedione-triazole conjugates (7a-7aa). Materials & methods: The designed compounds were synthesized by stirring a mixture of thiazolidine-2,4-dione, propargyl bromide, cinnamaldehyde and azide derivatives in polyethylene glycol-400. The α-amylase inhibitory activity of the synthesized conjugates was examined by integrating in vitro and in silico studies. Results: The investigated derivatives exhibited promising α-amylase inhibitory activity, with IC50 values ranging between 0.028 and 0.088 μmol ml-1. Various computational approaches were employed to get detailed information about the inhibition mechanism. Conclusion: The thiazolidinedione-triazole conjugate 7p, with IC50 = 0.028 μmol ml-1, was identified as the best hit for inhibiting α-amylase.

Bioprospecting phytochemicals of Rosmarinus officinalis L. for targeting SARS-CoV-2 main protease (Mpro): a computational study

CONTEXT

The persistent spread of highly contagious COVID-19 disease is one of the deadliest occurrences in the history of mankind. Despite the distribution of numerous efficacious vaccines and their extensive usage, the perpetual effectiveness of immunization is being catechized. Therefore, discovering an alternative therapy to control and prevent COVID-19 infections has become a top priority. The main protease (M^pro) plays a key role in viral replication, making it an intriguing pharmacological target for SARS-CoV-2.

METHODS

In this context, virtual screening of thirteen bioactive polyphenols and terpenoids of Rosmarinus officinalis L. was performed using several computational modules including molecular docking, ADMET, drug-likeness characteristics, and molecular dynamic simulation to predict the potential inhibitors against SARS-CoV-2 M^pro (PDB: 6LU7). The results suggest that apigenin, betulinic acid, luteolin, carnosol, and rosmarinic acid may emerge as potential inhibitors of SARS-CoV-2 with acceptable drug-likeness, pharmacokinetics, ADMET characteristics, and binding interactions comparable with remdesivir and favipiravir. These findings imply that some of the active components of Rosmarinus officinalis L. can serve as an effective antiviral source for the development of therapeutics for SARS-CoV-2 infection.

Predictive profiling of gram-negative antibiotics in CagA oncoprotein inactivation: a molecular dynamics simulation approach

Gastric cancer (GC) is the fifth most prevalent form of cancer worldwide. CagA - positive Helicobacter pylori infects more than 60% of the human population. Moreover, chronic infection of CagA-positive H. pylori can directly affect GC incidence. In the current study, we have repurposed FDA-approved antibiotics that are viable alternatives to current regimens and can potentially be used as combination therapy against the CagA of H. pylori. The 100 FDA-approved gram negative antibiotics were screened against CagA protein using the AutoDock 4.2 tool. Further, top nine compounds were selected based on higher binding affinity with CagA. The trajectory analysis of MD simulations reflected that binding of these drugs with CagA stabilizes the system. Nonetheless, atomic density map and principal component analysis also support the notion of stable binding of antibiotics to the protein. The residues ASP96, GLN100, PRO184, and THR185 of compound cefpiramide, doxycycline, delafloxacin, metacycline, oxytetracycline, and ertapenem were involved in the binding with CagA protein. These residues are crucial for the CagA that aids in entry or pathogenesis of the bacterium. The screened FDA-approved antibiotics have a potential druggability to inhibit CagA and reduce the progression of H. pylori borne diseases.

Parsing structural fragments of thiazolidin-4-one based α-amylase inhibitors: A combined approach employing in vitro colorimetric screening and GA-MLR based QSAR modelling supported by molecular docking, molecular dynamics simulation and ADMET studies

α-Amylase (EC.3.2.1.1) is a ubiquitous digestive endoamylase. The abrupt rise in blood glucose levels due to the hydrolysis of carbohydrates by α-amylase at a faster rate is one of the main reasons for type 2 diabetes. The inhibitors prevent the action of digestive enzymes, slowing the digestion of carbs and eventually assisting in the management of postprandial hyperglycemia. In the course of developing α-amylase inhibitors, we have screened 2-aryliminothiazolidin-4-one based analogs for their in vitro α-amylase inhibitory potential and employed various in silico approaches for the detailed exploration of the bioactivity. The DNSA bioassay revealed that compounds 5c, 5e, 5h, 5j, 5m, 5o and 5t were more potent than the reference drug (IC₆₀ value = 22.94 ± 0.24 μg mL^-1). The derivative 5o with -NO₂ group at both the rings was the most potent analog with an IC₆₀ value of 19.67 ± 0.20 μg mL^-1 whereas derivative 5a with unsubstituted aromatic rings showed poor inhibitory potential with an IC₆₀ value of 33.40 ± 0.15 μg mL^-1. The reliable QSAR models were developed using the QSARINS software. The high value of R²_ext = 0.9632 for model IM-9 showed that the built model can be applied to predict the α-amylase inhibitory activity of the untested molecules. A consensus modelling approach was also employed to test the reliability and robustness of the developed QSAR models. Molecular docking and molecular dynamics were employed to validate the bioassay results by studying the conformational changes and interaction mechanisms. A step further, these compounds also exhibited good ADMET characteristics and bioavailability when tested for in silico pharmacokinetics prediction parameters.

Strategic analyses to identify key structural features of antiviral/antimalarial compounds for their binding interactions with 3CLpro, PLpro and RdRp of SARS-CoV-2: in silico molecular docking and dynamic simulation studies

Severe acute respiratory syndrome coronavirus (SARS-CoV-2), a novel member of the betacoronavirus family is a single-stranded RNA virus that has spread worldwide prompting the World Health Organization to declare a global pandemic. This creates an alarming situation and generates an urgent need to develop innovative therapeutic agents. In this context, an in silico molecular docking and molecular dynamics (MD) simulation study on the existing 58 antiviral and antimalarial compounds was performed on 3CLpro, PLpro and RdRp SARS-CoV-2 proteins. The antiviral compounds are best fitted in the binding pockets and interact more profoundly with the amino acid residues compared to antimalarial compounds. An HIV protease inhibitor, saquinavir showed a good dock score and binding free energy with varied binding interactions against 3CLpro and PLpro. While, adefovir, a nucleotide HBV DNA polymerase inhibitor exhibited good dock score and binding interactions against RdRp. Although, the antimalarial compounds showed relatively less dock score but were found to be crucial in displaying essential binding interactions with these proteins. The MD simulation runs for 100 ns on 3CLpro-saquinavir, PLpro-saquinavir and RdRp-adefovir complexes using Desmond revealed fairly stable nature of interactions. This study helped in understanding the key interactions of the vital functionalities that provide a concrete base to develop lead molecules effective against SARS-CoV-2.

Structural basis of Omicron immune evasion: A comparative computational study

Background

The vaccines used against SARS-CoV-2 by now have been able to develop some neutralising antibodies in the vaccinated population and their effectiveness has been challenged by the emergence of the new strains with numerous mutations in the spike protein of SARS-CoV-2. Since S protein is the major immunogenic protein of the virus which contains Receptor Binding Domain (RBD) that interacts with the human Angiotensin-Converting Enzyme 2 (ACE2) receptors, any mutations in this region should affect the neutralisation potential of the antibodies leading to the immune evasion. Several variants of concern of the virus have emerged so far, amongst which the most critical are Delta and recently reported Omicron. In this study, we have mapped and reported mutations on the modelled RBD and evaluated binding affinities of various human antibodies with it.

Method

Docking and molecular dynamics simulation studies have been used to explore the effect of mutations on the structure of RBD and RBD-antibody interaction.

Results

These analyses show that the mutations mostly at the interface of a nearby region lower the binding affinity of the antibody by ten to forty percent, with a downfall in the number of interactions formed as a whole. It implies the generation of immune escape variants.

Conclusions

Notable mutations and their effect was characterised that explain the structural basis of antibody efficacy in Delta and a compromised neutralisation effect for the Omicron variant. Thus, our results pave the way for robust vaccine design that can be effective for many variants.

Drugs repurposing for SARS-CoV-2: new insight of COVID-19 druggability

INTRODUCTION

The ongoing epidemic of severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) creates a massive panic worldwide due to the absence of effective medicines. Developing a new drug or vaccine is time-consuming to pass safety and efficacy testing. Therefore, repurposing drugs have been introduced to treat COVID-19 until effective drugs are developed.

AREA COVERED

A detailed search of repurposing drugs against SARS-CoV-2 was carried out using the PubMed database, focusing on articles published 2020 years onward. A different class of drugs has been described in this article to target hosts and viruses. Based on the previous pandemic experience of SARS-CoV and MERS, several antiviral and antimalarial drugs are discussed here. This review covers the failure of some repurposed drugs that showed promising activity in the earlier CoV-pandemic but were found ineffective against SARS-CoV-2. All these discussions demand a successful drug development strategy for screening and identifying an effective drug for better management of COVID-19. The drug development strategies described here will serve a new scope of research for academicians and researchers.

EXPERT OPINION

Repurposed drugs have been used since COVID-19 to eradicate disease propagation. Drugs found effective for MERS and SARS may not be effective against SARS-CoV-2. Drug libraries and artificial intelligence are helpful tools to screen and identify different molecules targeting viruses or hosts.

Mechanistic insights of key host proteins and potential repurposed inhibitors regulating SARS-CoV-2 pathway

The emergence of pandemic situations originated from severe acute respiratory syndrome (SARS)‐CoV‐2 and its new variants created worldwide medical emergencies. Due to the non‐availability of efficient drugs and vaccines at these emergency hours, repurposing existing drugs can effectively treat patients critically infected by SARS‐CoV‐2. Finding a suitable repurposing drug with inhibitory efficacy to a host‐protein is challenging. A detailed mechanistic understanding of the kinetics, (dis)association pathways, key protein residues facilitating the entry–exit of the drugs with targets are fundamental in selecting these repurposed drugs. Keeping this target as the goal of the paper, the potential repurposing drugs, Nafamostat, Camostat, Silmitasertib, Valproic acid, and Zotatifin with host‐proteins HDAC2, CSK22, eIF4E2 are studied to elucidate energetics, kinetics, and dissociation pathways. From an ensemble of independent simulations, we observed the presence of single or multiple dissociation pathways with varying host‐proteins‐drug systems and quantitatively estimated the probability of unbinding through these specific pathways. We also explored the crucial gateway residues facilitating these dissociation mechanisms. Interestingly, the residues we obtained for HDAC2 and CSK22 are also involved in the catalytic activity. Our results demonstrate how these potential drugs interact with the host machinery and the specific target residues, showing involvement in the mechanism. Most of these drugs are in the preclinical phase, and some are already being used to treat severe COVID‐19 patients. Hence, the mechanistic insight presented in this study is envisaged to support further findings of clinical studies and eventually develop efficient inhibitors to treat SARS‐CoV‐2.

Artificial Neural Network-Based Study Predicts GS-441524 as a Potential Inhibitor of SARS-CoV-2 Activator Protein Furin: a Polypharmacology Approach

Furin, a pro-protein convertase, plays a significant role as a biological scissor in bacterial, viral, and even mammalian substrates which in turn decides the fate of many viral and bacterial infections along with the numerous ailments caused by cancer, diabetes, inflammations, and neurological disorders. In the wake of the current pandemic caused by the virus SARS-CoV-2, furin has become the center of attraction for researchers as the spike protein contains a polybasic furin cleavage site. In the present work, we have searched for novel inhibitors against this interesting human target from FDA-approved antiviral. To enhance the selection of new inhibitors, we employed Kohonen's artificial neural network-based self-organizing maps for ligand-based virtual screening. Promising results were obtained which can help in drug repurposing and network pharmacology studies can address the errors generated due to promiscuity/polypharmacology. We found 15 existing FDA antiviral drugs having the potential to inhibit furin. Among these, six compounds have targets on important human proteins (LDLR, FCGR1A, PCK1, TLR7, DNA, and PNP). The role of these 15 drugs inhibiting furin can be established by studying further on patients infected with number of viruses including SARS-CoV-2. Here we propose two promising candidate FDA drugs GS-441524 and Grazoprevir (MK-5172) for repurposing as inhibitors of furin. The best results were observed with GS-441524.

Probing the Immune System Dynamics of the COVID-19 Disease for Vaccine Designing and Drug Repurposing Using Bioinformatics Tools

The pathogenesis of COVID-19 is complicated by immune dysfunction. The impact of immune-based therapy in COVID-19 patients has been well documented, with some notable studies on the use of anti-cytokine medicines. However, the complexity of disease phenotypes, patient heterogeneity and the varying quality of evidence from immunotherapy studies provide problems in clinical decision-making. This review seeks to aid therapeutic decision-making by giving an overview of the immunological responses against COVID-19 disease that may contribute to the severity of the disease. We have extensively discussed theranostic methods for COVID-19 detection. With advancements in technology, bioinformatics has taken studies to a higher level. The paper also discusses the application of bioinformatics and machine learning tools for the diagnosis, vaccine design and drug repurposing against SARS-CoV-2.

Role of Probiotics in the Management of COVID-19: A Computational Perspective

Coronavirus disease 2019 (COVID-19) was declared a pandemic at the beginning of 2020, causing millions of deaths worldwide. Millions of vaccine doses have been administered worldwide; however, outbreaks continue. Probiotics are known to restore a stable gut microbiota by regulating innate and adaptive immunity within the gut, demonstrating the possibility that they may be used to combat COVID-19 because of several pieces of evidence suggesting that COVID-19 has an adverse impact on gut microbiota dysbiosis. Thus, probiotics and their metabolites with known antiviral properties may be used as an adjunctive treatment to combat COVID-19. Several clinical trials have revealed the efficacy of probiotics and their metabolites in treating patients with SARS-CoV-2. However, its molecular mechanism has not been unraveled. The availability of abundant data resources and computational methods has significantly changed research finding molecular insights between probiotics and COVID-19. This review highlights computational approaches involving microbiome-based approaches and ensemble-driven docking approaches, as well as a case study proving the effects of probiotic metabolites on SARS-CoV-2.

Target-based drug repurposing against Candida albicans-A computational modeling, docking, and molecular dynamic simulations study

The emergence of multidrug-resistant strains of Candida albicans has become a global threat mostly due to co-infection with immune-compromised patients leading to invasive candidiasis. The life-threatening form of the disease can be managed quickly and effectively by drug repurposing. Thus, the study used in silico approaches to evaluate Food and Drug Administration (FDA) approved drugs against three drug targets-TRR1, TOM40, and YHB1. The tertiary structures of three drug targets were modeled, refined, and evaluated for their structural integrity based on PROCHECK, ERRAT, and PROSA. High-throughput virtual screening of FDA-approved drugs (8815), interaction analysis, and energy profiles had revealed that DB01102 (Arbutamine), DB01611 (Hydroxychloroquine), and DB09319 (Carindacillin) exhibited better binding affinity with TRR1, TOM40, and YHB1, respectively. Notably, the molecular dynamic simulation explored that Gln45, Thr119, and Asp288 of TRR1; Thr107 and Ser121 of TOM40; Arg193, Glu213, and Ser228 of YHB1 are crucial residues for stable drug-target interaction. Additionally, it also prioritized Arbutamine-TRR1 as the best drug-target complex based on MM-PBSA (-52.72 kcal/mol), RMSD (2.43 Å), and radius of gyration (-21.49 Å) analysis. In-depth, PCA results supported the findings of molecular dynamic simulations. Interestingly, the conserved region (>70%) among the TRR1 sequences from pathogenic Candida species indicated the effectiveness of Arbutamine against multiple species of Candida as well. Thus, the study dispenses new insight and enriches the understanding of developing an advanced technique to consider potential antifungals against C. albicans. Nonetheless, a detailed experimental validation is needed to investigate the efficacy of Arbutamin against life-threatening candidiasis.

Comparative assessment of RNA-dependent RNA polymerase (RdRp) inhibitors under clinical trials to control SARS-CoV2 using rigorous computational workflow

The devastating effect of SARS-CoV2 continues and the scientific community is pursuing to find the strategy to combat the spread of the virus. The approach is adapted to target this virus with medicine in combination with existing vaccines. For this, the medications that can specifically inhibit an enzyme essential for viral replication 'RNA-dependant-RNA polymerase (RdRp)' of SARS-CoV2 are being developed. RdRp is the enzyme commonly found in all RNA viruses but is absent in humans. There are in total 60 different RdRp inhibitors already under clinical trials for combating other RNA viruses, which are sought to even work for SARS-CoV2. These inhibitors are classified as nucleoside/nucleotide analogues and nonnucleoside/nonnucleotide analogues. In this study, all the known RdRp inhibitors were computationally targeted in the native form and their active form making the use of molecular docking, MM-GBSA and molecular dynamics (MD) simulations to find the top two of each nucleoside/nucleotide analogues and nonnucleoside/nonnucleotide analogues. The results showed ribavirin 5'-triphosphate and favipiravir ribonucleoside triphosphate (favipiravir-RTP) to be the top two nucleotide analogues while pimodivir and dihydropyrazolopyridinone analogue 8d were the top two nonnucleosides/non-nucleotide analogues.