Dual computational and biological assessment of some promising nucleoside analogs against the COVID-19-Omicron variant

An in-silico investigation based on molecular simulations of novel and potential brain-penetrant GluN2B NMDA receptor antagonists as anti-stroke therapeutic agents

GluN2B-induced activation of NMDA receptors plays a key function in central nervous system (CNS) disorders, including Parkinson, Alzheimer, and stroke, as it is strongly involved in excitotoxicity, which makes selective NMDA receptor antagonists one of the potential therapeutic agents for the treatment of neurodegenerative diseases, especially stroke. The present study aims to examine a structural family of thirty brain-penetrating GluN2B N-methyl-D-aspartate (NMDA) receptor antagonists, using virtual computer-assisted drug design (CADD) to discover highly candidate drugs for ischemic strokes. Initially, the physicochemical and ADMET pharmacokinetic properties confirmed that C13 and C22 compounds were predicted as non-toxic inhibitors of CYP2D6 and CYP3A4 cytochromes, with human intestinal absorption (HIA) exceeding 90%, and designed to be as efficient central nervous system (CNS) agents due to the highest probability to cross the blood-brain barrier (BBB). Compared to ifenprodil, a co-crystallized ligand complexed with the transport protein encoded as 3QEL.pdb, we have noticed that C13 and C22 chemical compounds were defined by good ADME-Toxicity profiles, meeting Lipinski, Veber, Egan, Ghose, and Muegge rules. The molecular docking results indicated that C22 and C13 ligands react specifically with the amino acid residues of the NMDA receptor subunit GluN1 and GluN2B. These intermolecular interactions produced between the candidate drugs and the targeted protein in the B chain remain stable over 200 nanoseconds of molecular dynamics simulation time. In conclusion, C22 and C13 ligands are highly recommended as anti-stroke therapeutic drugs due to their safety and molecular stability towards NMDA receptors.Communicated by Ramaswamy H. Sarma.

Comprehensive bioinformatics analysis of structural and functional consequences of deleterious missense mutations in the human QDPR gene

Quinonoid dihydropteridine reductase (QDPR) is an enzyme that regulates tetrahydrobiopterin (BH4), a cofactor for enzymes involved in neurotransmitter synthesis and blood pressure regulation. Reduced QDPR activity can cause dihydrobiopterin (BH2) accumulation and BH4 depletion, leading to impaired neurotransmitter synthesis, oxidative stress, and increased risk of Parkinson's disease. A total of 10,236 SNPs were identified in the QDPR gene, with 217 being missense SNPs. Over 18 different sequence-based and structure-based tools were employed to assess the protein's biological activity, with several computational tools identifying deleterious SNPs. Additionally, the article provides detailed information about the QDPR gene and protein structure and conservation analysis. The results showed that 10 mutations were harmful and linked to brain and central nervous system disorders, and were predicted to be oncogenic by Dr. Cancer and CScape. Following conservation analysis, the HOPE server was used to analyse the effect of six selected mutations (L14P, V15G, G23S, V54G, M107K, G151S) on the protein structure. Overall, the study provides insights into the biological and functional impact of nsSNPs on QDPR activity and the potential induced pathogenicity and oncogenicity. In the future, research can be conducted to systematically evaluate QDPR gene variation through clinical studies, investigate mutation prevalence across different geographical regions, and validate computational results with conclusive experiments.Communicated by Ramaswamy H. Sarma.

K1K2NN: A novel multi-label classification approach based on neighbors for predicting COVID-19 drug side effects

COVID-19, a novel ailment, has received comparatively fewer drugs for its treatment. Side Effects (SE) of a COVID-19 drug could cause long-term health issues. Hence, SE prediction is essential in COVID-19 drug development. Efficient models are also needed to predict COVID-19 drug SE since most existing research has proposed many classifiers to predict SE for diseases other than COVID-19. This work proposes a novel classifier based on neighbors named K1 K2 Nearest Neighbors (K1K2NN) to predict the SE of the COVID-19 drug from 17 molecules' descriptors and the chemical 1D structure of the drugs. The model is implemented based on the proposition that chemically similar drugs may be assigned similar drug SE, and co-occurring SE may be assigned to chemically similar drugs. The K1K2NN model chooses the first K1 neighbors to the test drug sample by calculating its similarity with the train drug samples. It then assigns the test sample with the SE label having the majority count on the SE labels of these K1 neighbor drugs obtained through a voting mechanism. The model then calculates the SE-SE similarity using the Jaccard similarity measure from the SE co-occurrence values. Finally, the model chooses the most similar K2 SE neighbors for those SE determined by the K1 neighbor drugs and assigns these SE to that test drug sample. The proposed K1K2NN model has showcased promising performance with the highest accuracy of 97.53% on chemical 1D drug structure and outperforms the state-of-the-art multi-label classifiers. In addition, we demonstrate the successful application of the proposed model on gene expression signature datasets, which aided in evaluating its performance and confirming its accuracy and robustness.

The effectiveness of novel oral antiviral treatment for non-hospitalized high-risk patients with COVID-19 during predominance of omicron XBB subvariants

OBJECTIVES

This study investigated the association between nirmatrelvir plus ritonavir (NMV-r) or molnupiravir and the outcomes of non-hospitalized high-risk patients with COVID-19 during Omicron XBB subvariants.

METHODS

The retrospective cohort study used the TriNetX US collaborative network to identify non-hospitalized high-risk adult patients with COVID-19 between 1 February 2023, and 31 August 2023. Propensity score matching (PSM) was used to match patients receiving NMV-r or MOV (the study group) with those not receiving antivirals (the control group).

RESULTS

Using PSM, two cohorts of 17,654 patients each with balanced baseline characteristics were identified. During the follow-up period, the study group had a lower risk of all-cause hospitalization, or death (3.2% [n = 564] versus 3.8% [n = 669]; HR, 0.796; 95% confidence interval [CI], 95% CI, 0.712-0.891). Compared with the control group, the study group had a significantly lower risk of all-cause hospitalization (3.1% vs. 3.4%; HR, 0.847; 95% CI, 0.754-0.950) and mortality (0.1% vs. 0.4%; HR, 0.295; 95% CI, 0.183-0.476).

CONCLUSION

The use of novel oral antiviral including NMV-r or MOV can be associated with a lower risk of all-cause hospitalization, or death in non-hospitalized high-risk patients with COVID-19 during Omicron XBB wave.

Explainable artificial intelligence-assisted virtual screening and bioinformatics approaches for effective bioactivity prediction of phenolic cyclooxygenase-2 (COX-2) inhibitors using PubChem molecular fingerprints

Cyclooxygenase-2 (COX-2) inhibitors are nonsteroidal anti-inflammatory drugs that treat inflammation, pain and fever. This study determined the interaction mechanisms of COX-2 inhibitors and the molecular properties needed to design new drug candidates. Using machine learning and explainable AI methods, the inhibition activity of 1488 molecules was modelled, and essential properties were identified. These properties included aromatic rings, nitrogen-containing functional groups and aliphatic hydrocarbons. They affected the water solubility, hydrophobicity and binding affinity of COX-2 inhibitors. The binding mode, stability and ADME properties of 16 ligands bound to the Cyclooxygenase active site of COX-2 were investigated by molecular docking, molecular dynamics simulation and MM-GBSA analysis. The results showed that ligand 339,222 was the most stable and effective COX-2 inhibitor. It inhibited prostaglandin synthesis by disrupting the protein conformation of COX-2. It had good ADME properties and high clinical potential. This study demonstrated the potential of machine learning and bioinformatics methods in discovering COX-2 inhibitors.

SARS-CoV-2 RNA-Dependent RNA Polymerase Follows Asynchronous Translocation Pathway for Viral Transcription and Replication

Translocation is one essential step for the SARS-CoV-2 RNA-dependent RNA polymerase (RdRp) to exert viral replication and transcription. Although cryo-EM structures of SARS-CoV-2 RdRp are available, the molecular mechanisms of dynamic translocation remain elusive. Herein, we constructed a Markov state model based on extensive molecular dynamics simulations to elucidate the translocation dynamics of the SARS-CoV-2 RdRp. We identified two intermediates that pinpoint the rate-limiting step of translocation and characterize the asynchronous movement of the template-primer duplex. The 3'-terminal nucleotide in the primer strand lags behind due to the uneven distribution of protein-RNA interactions, while the translocation of the template strand is delayed by the hurdle residue K500. Even so, the two strands share the same "ratchet" to stabilize the polymerase in the post-translocation state, suggesting a Brownian-ratchet model. Overall, our study provides intriguing insights into SARS-CoV-2 replication and transcription, which would open a new avenue for drug discoveries.

Cheminformatics-based discovery of new organoselenium compounds with potential for the treatment of cutaneous and visceral leishmaniasis

Leishmaniasis affects more than 12 million humans globally and a further 1 billion people are at risk in leishmaniasis endemic areas. The lack of a vaccine for leishmaniasis coupled with the limitations of existing anti-leishmanial therapies prompted this study. Cheminformatic techniques are widely used in screening large libraries of compounds, studying protein-ligand interactions, analysing pharmacokinetic properties, and designing new drug molecules with great speed, accuracy, and precision. This study was undertaken to evaluate the anti-leishmanial potential of some organoselenium compounds by quantitative structure-activity relationship (QSAR) modeling, molecular docking, pharmacokinetic analysis, and molecular dynamic (MD) simulation. The built QSAR model was validated (R2train = 0.8646, R2test = 0.8864, Q2 = 0.5773) and the predicted inhibitory activity (pIC50) values of the newly designed compounds were higher than that of the template (Compound 6). The new analogues (6a, 6b, and 6c) showed good binding interactions with the target protein (Pyridoxal kinase, PdxK) while also presenting excellent drug-likeness and pharmacokinetic profiles. The results of density functional theory, MD simulation, and molecular mechanics generalized Born surface area (MM/GBSA) analyses suggest the favourability and stability of protein-ligand interactions of the new analogues with PdxK, comparing favourably well with the reference drug (Pentamidine). Conclusively, the newly designed compounds could be synthesized and tested experimentally as potential anti-leishmanial drug molecules.Communicated by Ramaswamy H. Sarma.

Identification of novel dual acting ligands targeting the adenosine A2A and serotonin 5-HT1A receptors

GPCRs are a family of transmembrane receptors that are profoundly linked to various neurological disorders, among which is Parkinson's disease (PD). PD is the second most ubiquitous neurological disorder after Alzheimer's disease, characterized by the depletion of dopamine in the central nervous system due to the impairment of dopaminergic neurons, leading to involuntary movements or dyskinesia. The current standard of care for PD is Levodopa, a dopamine precursor, yet the chronic use of this agent can exacerbate motor symptoms. Recent studies have investigated the effects of combining A2AR antagonist and 5-HT1A agonist on dyskinesia and motor complications in animal models of PD. It has been proved that the drug combination has significantly improved involuntary movements while maintaining motor activity, highlighting as a result new lines of therapy for PD treatments, through the regulation of both receptors. Using a combination of ligand-based pharmacophore modelling, virtual screening, and molecular dynamics simulation, this study intends on identifying potential dual-target compounds from IBScreen. Results showed that the selected models displayed good enrichment metrics with a near perfect receiver operator characteristic (ROC) and Area under the accumulation curve (AUAC) values, signifying that the models are both specific and sensitive. Molecular docking and ADMET analysis revealed that STOCK2N-00171 could be potentially active against A2AR and 5-HT1A. Post-MD analysis confirmed that the ligand exhibits a stable behavior throughout the simulation while maintaining crucial interactions. These results imply that STOCK2N-00171 can serve as a blueprint for the design of novel and effective dual-acting ligands targeting A2AR and 5-HT1A.Communicated by Ramaswamy H. Sarma.

Current understanding of nucleoside analogs inhibiting the SARS-CoV-2 RNA-dependent RNA polymerase

Since the outbreak of the COVID-19 pandemic, severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) RNA-dependent RNA polymerase (RdRp) has become a main target for antiviral therapeutics due to its essential role in viral replication and transcription. Thus, nucleoside analogs structurally resemble the natural RdRp substrate and hold great potential as inhibitors. Until now, extensive experimental investigations have been performed to explore nucleoside analogs to inhibit the RdRp, and concerted efforts have been made to elucidate the underlying molecular mechanisms further. This review begins by discussing the nucleoside analogs that have demonstrated inhibition in the experiments. Second, we examine the current understanding of the molecular mechanisms underlying the action of nucleoside analogs on the SARS-CoV-2 RdRp. Recent findings in structural biology and computational research are presented through the classification of inhibitory mechanisms. This review summarizes previous experimental findings and mechanistic investigations of nucleoside analogs inhibiting SARS-CoV-2 RdRp. It would guide the rational design of antiviral medications and research into viral transcriptional mechanisms.

In-silico screening of Acacia pennata and Bridelia retusa reveals pinocembrin-7-O-β-D-glucopyranoside as a promising β-lactamase inhibitor to combat antibiotic resistance

The β-lactamase of Pseudomonas aeruginosa is known to degrade β-lactam antibiotics such as penicillins, cephalosporins, monobactams, and carbapenems. With the discovery of an extended-spectrum β-lactamase in a clinical isolate of P. aeruginosa, the bacterium has become multi-drug resistant. In this study, we aim to identify new β-lactamase inhibitors by virtually screening a total of 43 phytocompounds from two Indian medicinal plants. In the molecular docking studies, pinocembrin-7-O-β-D-glucopyranoside (P7G) (-9.6 kcal/mol) from Acacia pennata and ellagic acid (EA) (-9.2 kcal/mol) from Bridelia retusa had lower binding energy than moxalactam (-8.4 kcal/mol). P7G and EA formed 5 (Ser62, Asn125, Asn163, Thr209, and Ser230) and 4 (Lys65, Ser123, Asn125, and Glu159) conventional hydrogens bonds with the active site residues. 100 ns MD simulations revealed that moxalactam and P7G (but not EA) were able to form a stable complex. The binding free energy calculations further revealed that P7G (-59.6526 kcal/mol) formed the most stable complex with β-lactamase when compared to moxalactam (-46.5669 kcal/mol) and EA (-28.4505 kcal/mol). The HOMO-LUMO and other DFT parameters support the stability and chemical reactivity of P7G at the active site of β-lactamase. P7G passed all the toxicity tests and bioavailability tests indicating that it possesses drug-likeness. Among the studied compounds, we identified P7G of A. pennata as the most promising phytocompound to combat antibiotic resistance by potentially inhibiting the β-lactamase of P. aeruginosa.Communicated by Ramaswamy H. Sarma.

Molecular dynamic and bioinformatic studies of metformin-induced ACE2 phosphorylation in the presence of different SARS-CoV-2 S protein mutations

The SARS-CoV-2 infection activates host kinases and causes high phosphorylation in both the host and the virus. There were around 70 phosphorylation sites found in SARS-CoV-2 viral proteins. Besides, almost 15,000 host phosphorylation sites were found in SARS-CoV-2-infected cells. COVID-19 is thought to enter cells via the well-known receptor Angiotensin-Converting Enzyme 2 (ACE2) and the serine protease TMPRSS2. Substantially, the COVID-19 infection doesn't induce phosphorylation of the ACE2 receptor at Serin-680(s680). Metformin's numerous pleiotropic properties and extensive use in medicine including COVID-19, have inspired experts to call it the "aspirin of the twenty-first century". Metformin's impact on COVID-19 has been verified in clinical investigations via ACE2 receptor phosphorylation at s680. In the infection of COVID-19, sodium-dependent transporters including the major neutral amino acid (B0AT1) is regulated by ACE2. The structure of B0AT1 complexing with the COVID-19 receptor ACE2 enabled significant progress in the creation of mRNA vaccines. We aimed to study the impact of the interaction of the phosphorylation form of ACE2-s680 with wild-type (WT) and different mutations of SARS-CoV-2 infection such as delta, omicron, and gamma (γ) on their entrance of host cells as well as the regulation of B0AT1by the SARS-CoV-2 receptor ACE2. Interestingly, compared to WT SARS-CoV-2, ACE2 receptor phosphorylation at s680 produces conformational alterations in all types of SARS-CoV-2. Furthermore, our results showed for the first time that this phosphorylation significantly influences ACE2 sites K625, K676, and R678, which are key mediators for ACE2-B0AT1 complex.

Post-COVID-19 effect on biochemical parameters in children: Should we take heed?

The aim of this research is to analyze the potential impact of the COVID-19 infection on the serum biochemical concentration of children 6 months after recovery from the infection. The study included 72 children with a median age of 11 years. The case group consisted of 37 children who had contracted COVID-19 6 months prior to the analysis. They reported no other pre- or post-covid chronic or systemic diseases. The control group consisted of 35 children who had no prior record of COVID-19 infection. The analysis showed a substantial variation (P = 0.026) in the mean urea values (mmol/L) between the case group (4.513 ± 0.839) and the control group (5.425 ± 1.173). However, both groups' urea levels were within the normal range of their age group. No statistical differences were found analyzing the variations between the two groups in the levels of LDH, AST, ALT, BiliT, GGT, AlbBCG2, CRP, CK, AlKP, UA, Phos, Crea2, Gluc, Ca, Na, K, Cl, TP, TC, TG, and HDL (P > 0.05). The DMFT score was substantially greater (P < 0.002) in the infected team (5.38 ± 2.841) in comparison to the non-infected group (2.6 ± 2.257). The study indicates that COVID-19 infection does not leave biochemical alterations among children who did not have pre-existing conditions. The biochemical analysis suggests that children recover better than adults from COVID-19. Furthermore, it calls for investigating non-lethal COVID-19 infection as a tool to discover underlying conditions. The DMFT score shows a correlation between COVID-19 infection and caries. However, the nature of the correlation is yet to be investigated.

A Series of Adenosine Analogs as the First Efficacious Anti-SARS-CoV-2 Drugs against the B.1.1.529.4 Lineage: A Preclinical Repurposing Research Study

Given the rapid progression of the coronavirus disease 2019 (COVID-19) pandemic, an ultrafast response was urgently required to handle this major public crisis. To contain the pandemic, investments are required to develop diagnostic tests, prophylactic vaccines, and novel therapies. Lately, nucleoside analog (NA) antivirals topped the scene as top options for the treatment of COVID-19 caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infections. Meanwhile, the continuous generation of new lineages of the SARS-CoV-2 Omicron variant caused a new challenge in the persistent COVID-19 battle. Hitting the two crucial SARS-CoV-2 enzymes RNA-dependent RNA polymerase (RdRp) and 3'-to-5' exoribonuclease (ExoN) collectively together using only one single ligand is a very successful new approach to stop SARS-CoV-2 multiplication and combat COVID-19 irrespective of the SARS-CoV-2 variant type because RdRps and ExoNs are broadly conserved among all SARS-CoV-2 strains. Herein, the current comprehensive study investigated most NAs libraries, searching for the most ideal drug candidates expectedly able to perfectly act through this double tactic. Gradual computational filtration gave rise to six different promising NAs, which are riboprine, forodesine, tecadenoson, nelarabine, vidarabine, and maribavir, respectively. Further biological assessment proved for the first time, using the in vitro anti-RdRp/ExoN and anti-SARS-CoV-2 bioassays, that riboprine and forodesine, among all the six tested NAs, are able to powerfully inhibit the replication of the new virulent strains of SARS-CoV-2 with extremely minute in vitro anti-RdRp and anti-SARS-CoV-2 EC₅₀ values of about 0.22 and 0.49 μM for riboprine and about 0.25 and 0.73 μM for forodesine, respectively, surpassing both remdesivir and the new anti-COVID-19 drug molnupiravir. The prior in silico data supported these biochemical findings, suggesting that riboprine and forodesine molecules strongly hit the key catalytic pockets of the SARS-CoV-2 (Omicron variant) RdRp's and ExoN's main active sites. Additionally, the ideal pharmacophoric features of riboprine and forodesine molecules render them typical dual-action inhibitors of SARS-CoV-2 replication and proofreading, with their relatively flexible structures open for diverse types of chemical derivatization. In Brief, the current important results of this comprehensive study revealed the interesting repurposing potentials of, mainly, the two nucleosides riboprine and forodesine to effectively shut down the polymerase/exoribonuclease-RNA nucleotides interactions of the SARS-CoV-2 Omicron variant and consequently treat COVID-19 infections, motivating us to rapidly begin the two drugs' broad preclinical/clinical anti-COVID-19 bioevaluations, hoping to combine both drugs soon in the COVID-19 treatment protocols.