In silico analysis of RNA-dependent RNA polymerase of the SARS-CoV-2 and therapeutic potential of existing antiviral drugs

Computational investigation of novel synthetic analogs of C-1'β substituted remdesivir against RNA-dependent RNA-polymerase of SARS-CoV-2

Remdesivir, a C-nucleotide prodrug binds to the viral RNA-dependent-RNA polymerase (RdRp) and inhibits the viral replication by terminating RNA transcription prematurely. It is reported in literature that interaction between the C-1'β-CN moiety of Remdesivir (RDV) and the Ser861 residue in RdRp enzyme, causes a delayed chain termination during the RNA replication process and is one of the important aspect of its mechanism of action. In the pursuance of increasing the biological activity of RDV and enhancing the SAR studies, against RNA viruses, we have designed its fourteen C1'β substituted analogs, 10 -23 bearing 4/5-membered heterocyclic rings. The docking and 100 ns molecular dynamics (MD) simulations of 10-23 to the RdRp protein (PDB ID: 7L1F) revealed important interactions between 2',3'-diol, oxo group of phosphoramidate, nitrogen residues of heterocyclic rings of synthetic molecules with Arg555, Arg553, Ser759, Cys622, Asn691, Asp623 amino acid residues of protein. The docking score of 2-ethylbutyl ((S)-(((2R,3S,4R,5R)-5-(4-aminopyrrolo[2,1-f][1,2,4]triazin-7-yl)-3,4-dihydroxy-5-(1H-1,2,3-triazol-4-yl)tetrahydrofuran-2-yl)methoxy)(phenoxy)phosphoryl)-L-alaninate, 11 was found to be the higher than RDV among 14 new compounds i.e. -5.20 kcal/mol. Out of 3 compounds, 10, 12 and 13 submitted for MD simulations and Molecular Mechanics Poisson-Boltzmann Surface Area (MMPBSA) analysis, trifluoro-oxadiazole derivative, 13 showed higher binding energy as compared to Remdesivir. The predicted ADMET properties of 14 compounds showed their potential for being drug candidates. The present study suggests that substitution at the C1'β position by 4/5-membered rings plays an important role in the interactions between nucleoside/tide and target protein.

Generic model to unravel the deeper insights of viral infections: an empirical application of evolutionary graph coloring in computational network biology

PURPOSE

Graph coloring approach has emerged as a valuable problem-solving tool for both theoretical and practical aspects across various scientific disciplines, including biology. In this study, we demonstrate the graph coloring's effectiveness in computational network biology, more precisely in analyzing protein-protein interaction (PPI) networks to gain insights about the viral infections and its consequences on human health. Accordingly, we propose a generic model that can highlight important hub proteins of virus-associated disease manifestations, changes in disease-associated biological pathways, potential drug targets and respective drugs. We test our model on SARS-CoV-2 infection, a highly transmissible virus responsible for the COVID-19 pandemic. The pandemic took significant human lives, causing severe respiratory illnesses and exhibiting various symptoms ranging from fever and cough to gastrointestinal, cardiac, renal, neurological, and other manifestations.

METHODS

To investigate the underlying mechanisms of SARS-CoV-2 infection-induced dysregulation of human pathobiology, we construct a two-level PPI network and employed a differential evolution-based graph coloring (DEGCP) algorithm to identify critical hub proteins that might serve as potential targets for resolving the associated issues. Initially, we concentrate on the direct human interactors of SARS-CoV-2 proteins to construct the first-level PPI network and subsequently applied the DEGCP algorithm to identify essential hub proteins within this network. We then build a second-level PPI network by incorporating the next-level human interactors of the first-level hub proteins and use the DEGCP algorithm to predict the second level of hub proteins.

RESULTS

We first identify the potential crucial hub proteins associated with SARS-CoV-2 infection at different levels. Through comprehensive analysis, we then investigate the cellular localization, interactions with other viral families, involvement in biological pathways and processes, functional attributes, gene regulation capabilities as transcription factors, and their associations with disease-associated symptoms of these identified hub proteins. Our findings highlight the significance of these hub proteins and their intricate connections with disease pathophysiology. Furthermore, we predict potential drug targets among the hub proteins and identify specific drugs that hold promise in preventing or treating SARS-CoV-2 infection and its consequences.

CONCLUSION

Our generic model demonstrates the effectiveness of DEGCP algorithm in analyzing biological PPI networks, provides valuable insights into disease biology, and offers a basis for developing novel therapeutic strategies for other viral infections that may cause future pandemic.

Comparative Genome Analysis of Old World and New World TYLCV Reveals a Biasness toward Highly Variable Amino Acids in Coat Protein

Begomoviruses, belonging to the family Geminiviridae and the genus Begomovirus, are DNA viruses that are transmitted by whitefly Bemisia tabaci (Gennadius) in a circulative persistent manner. They can easily adapt to new hosts and environments due to their wide host range and global distribution. However, the factors responsible for their adaptability and coevolutionary forces are yet to be explored. Among BGVs, TYLCV exhibits the broadest range of hosts. In this study, we have identified variable and coevolving amino acid sites in the proteins of Tomato yellow leaf curl virus (TYLCV) isolates from Old World (African, Indian, Japanese, and Oceania) and New World (Central and Southern America). We focused on mutations in the coat protein (CP), as it is highly variable and interacts with both vectors and host plants. Our observations indicate that some mutations were accumulating in Old World TYLCV isolates due to positive selection, with the S149N mutation being of particular interest. This mutation is associated with TYLCV isolates that have spread in Europe and Asia and is dominant in 78% of TYLCV isolates. On the other hand, the S149T mutation is restricted to isolates from Saudi Arabia. We further explored the implications of these amino acid changes through structural modeling. The results presented in this study suggest that certain hypervariable regions in the genome of TYLCV are conserved and may be important for adapting to different host environments. These regions could contribute to the mutational robustness of the virus, allowing it to persist in different host populations.

Disruption of Irisin Dimerization by FDA-Approved Drugs: A Computational Repurposing Approach for the Potential Treatment of Lipodystrophy Syndromes

In this paper, we present the development of a computer-based repurposing approach to identify FDA-approved drugs that are potentially able to interfere with irisin dimerization. It has been established that altered levels of irisin dimers are a pure hallmark of lipodystrophy (LD) syndromes. Accordingly, the identification of compounds capable of slowing down or precluding the irisin dimers' formation could represent a valuable therapeutic strategy in LD. Combining several computational techniques, we identified five FDA-approved drugs with satisfactory computational scores (iohexol, XP score = -7.70 kcal/mol, SP score = -5.5 kcal/mol, ΔG_bind = -61.47 kcal/mol, ΔG_bind (average) = -60.71 kcal/mol; paromomycin, XP score = -7.23 kcal/mol, SP score = -6.18 kcal/mol, ΔG_bind = -50.14 kcal/mol, ΔG_bind (average) = -49.13 kcal/mol; zoledronate, XP score = -6.33 kcal/mol, SP score = -5.53 kcal/mol, ΔG_bind = -32.38 kcal/mol, ΔG_bind (average) = -29.42 kcal/mol; setmelanotide, XP score = -6.10 kcal/mol, SP score = -7.24 kcal/mol, ΔG_bind = -56.87 kcal/mol, ΔG_bind (average) = -62.41 kcal/mol; and theophylline, XP score = -5.17 kcal/mol, SP score = -5.55 kcal/mol, ΔG_bind = -33.25 kcal/mol, ΔG_bind (average) = -35.29 kcal/mol) that are potentially able to disrupt the dimerization of irisin. For this reason, they deserve further investigation to characterize them as irisin disruptors. Remarkably, the identification of drugs targeting this process can offer novel therapeutic opportunities for the treatment of LD. Furthermore, the identified drugs could provide a starting point for a repositioning approach, synthesizing novel analogs with improved efficacy and selectivity against the irisin dimerization process.

Bats-associated beta-coronavirus detection and characterization: First report from Pakistan

Bats remains as reservoirs for highly contagious and pathogenic viral families including the Coronaviridae, Filoviridae, Paramyxoviruses, and Rhabdoviridae. Spill over of viral species (SARS-CoV, MERS-CoV & SARS-CoV2) from bats (as a possible potential reservoirs) have recently caused worst outbreaks. Early detection of viral species of pandemic potential in bats is of great importance. We detected beta coronaviruses in the studied bats population (positive samples from Rousettus leschenaultia) and performed the evolutionary analysis, amino acid sequence alignment, and analysed the 3-Dimentional protein structure. We detected the coronaviruses for the first time in bats from Pakistan. Our analysis based on RdRp partial gene sequencing suggest that the studied viral strains are closely related to MERS-CoV-like viruses as they exhibit close structure similarities (with few substitutions) and also observed a substitution in highly conserved SDD in the palm subdomain of motif C to ADD, when compared with earlier reported viral strains. It could be concluded from our study that coronaviruses are circulating among the bat's population in Pakistan. Based on the current findings, we suggest large scale screening procedures of bat virome across the country to detect potential pathogenic viral species.

Inspection on the Mechanism of SARS-CoV-2 Inhibition by Penciclovir: A Molecular Dynamic Study

In recent years, humanity has had to face a critical pandemic due to SARS-CoV-2. In the rapid search for effective drugs against this RNA-positive virus, the repurposing of already existing nucleotide/nucleoside analogs able to stop RNA replication by inhibiting the RNA-dependent RNA polymerase enzyme has been evaluated. In this process, a valid contribution has been the use of in silico experiments, which allow for a rapid evaluation of the possible effectiveness of the proposed drugs. Here we propose a molecular dynamic study to provide insight into the inhibition mechanism of Penciclovir, a nucleotide analog on the RNA-dependent RNA polymerase enzyme. Besides the presented results, in this article, for the first time, molecular dynamic simulations have been performed considering not only the RNA-dependent RNA polymerase protein, but also its cofactors (fundamental for RNA replication) and double-strand RNA.

Butein as a potential binder of human ACE2 receptor for interfering with SARS-CoV-2 entry: a computer-aided analysis

Natural products have been included in our dietary supplements and have been shown to have numerous therapeutic properties. With the looming danger of many zoonotic agents and novel emerging pathogens mainly of viral origin, many researchers are launching various clinical trials, testing these compounds for their antiviral activity. The present work deals with some of the available natural compounds from the literature that have demonstrated activity in counteracting pathogen infections. Accordingly, we screened, using in silico methods, this subset of natural compounds for searching potential drug candidates able to interfere in the recognition of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) spike protein and its target human angiotensin-converting enzyme 2 (hACE2) receptor, leading to the viral entry. Disrupting that recognition is crucial for slowing down the entrance of viral particles into host cells. The selected group of natural products was examined, and their interaction profiles against the host cell target protein ACE2 were studied at the atomic level. Based on different computer-based procedures including molecular docking, physicochemical property evaluation, and molecular dynamics, butein was identified as a potential hit molecule able to bind the hACE2 receptor. The results indicate that herbal compounds can be effective for providing possible therapeutics for treating and managing coronavirus disease 2019 (COVID-19) infection.

Methodology-Centered Review of Molecular Modeling, Simulation, and Prediction of SARS-CoV-2

Despite tremendous efforts in the past two years, our understanding of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), virus-host interactions, immune response, virulence, transmission, and evolution is still very limited. This limitation calls for further in-depth investigation. Computational studies have become an indispensable component in combating coronavirus disease 2019 (COVID-19) due to their low cost, their efficiency, and the fact that they are free from safety and ethical constraints. Additionally, the mechanism that governs the global evolution and transmission of SARS-CoV-2 cannot be revealed from individual experiments and was discovered by integrating genotyping of massive viral sequences, biophysical modeling of protein-protein interactions, deep mutational data, deep learning, and advanced mathematics. There exists a tsunami of literature on the molecular modeling, simulations, and predictions of SARS-CoV-2 and related developments of drugs, vaccines, antibodies, and diagnostics. To provide readers with a quick update about this literature, we present a comprehensive and systematic methodology-centered review. Aspects such as molecular biophysics, bioinformatics, cheminformatics, machine learning, and mathematics are discussed. This review will be beneficial to researchers who are looking for ways to contribute to SARS-CoV-2 studies and those who are interested in the status of the field.

Virtual screening and docking analysis of novel ligands for selective enhancement of tea (Camellia sinensis) flavonoids

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Tea-specific flavonoid biosynthetic pathway (FBP) was retrieved from KEGG.

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Putative ligands were predicted to enhance enzymes-substrate binding affinity.

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FBP genes showed moderately higher expression & relatively strong codon adaptation.

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Most of the genes were AT-rich and biased to A/U-ending synonymous codons.

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Mutational selection was determining the selective constraints on codon bias.

Flavour of tea is mainly contributed by a group of polyphenols – flavonoids. However, the content of flavonoid fluctuates seasonally and is found to be higher in the first flush of tea, when compared to the second flush. This disparity in the flavonoid content, and hence taste, incurs heavy economic losses to the tea plantation industry each harvest season. For our present study, four key product-specific enzymes (PAL, FNS, FLS and ANS) of the tea-specific flavonoid pathway were selected to perform molecular docking studies with specific virtually screened allosteric modulators. Results of docking analyses showed Naringenin, 2-Morpholin-4-ium-4-ylethanesulfonate, 6-C-Glucosylquercetin, 2-Oxoglutaric acid, 3,5,7,3′,4′-pentahydroxyflavone to be capable of improving the spontaneity of the enzyme-substrate reactions in terms of docking score, RMSD values, and non-covalent interactions (H-bond,hydrophobic interaction, Π-stacking, salt bridge, etc.). Further, the evolutionary relationship of tea flavonoid pathway enzymes was constructed and compared with related taxa. The codon usage-based of tea flavonoid biosynthetic genes indicated the non-biasness of their nucleotide composition. Overall this study will provide a direction towards putative ligand-dependent enhancement of flavonoid content, irrespective of seasonal variation.

Virtual Combinatorial Library Screening of Quinadoline B Derivatives against SARS-CoV-2 RNA-Dependent RNA Polymerase

The unprecedented global health threat of SARS-CoV-2 has sparked a continued interest in discovering novel anti-COVID-19 agents. To this end, we present here a computer-based protocol for identifying potential compounds targeting RNA-dependent RNA polymerase (RdRp). Starting from our previous study wherein, using a virtual screening campaign, we identified a fumiquinazolinone alkaloid quinadoline B (Q3), an antiviral fungal metabolite with significant activity against SARS-CoV-2 RdRp, we applied in silico combinatorial methodologies for generating and screening a library of anti-SARS-CoV-2 candidates with strong in silico affinity for RdRp. For this study, the quinadoline pharmacophore was subjected to structural iteration, obtaining a Q3-focused library of over 900,000 unique structures. This chemical library was explored to identify binders of RdRp with greater affinity with respect to the starting compound Q3. Coupling this approach with the evaluation of physchem profile, we found 26 compounds with significant affinities for the RdRp binding site. Moreover, top-ranked compounds were submitted to molecular dynamics to evaluate the stability of the systems during a selected time, and to deeply investigate the binding mode of the most promising derivatives. Among the generated structures, five compounds, obtained by inserting nucleotide-like scaffolds (1, 2, and 5), heterocyclic thiazolyl benzamide moiety (compound 3), and a peptide residue (compound 4), exhibited enhanced binding affinity for SARS-CoV-2 RdRp, deserving further investigation as possible antiviral agents. Remarkably, the presented in silico procedure provides a useful computational procedure for hit-to-lead optimization, having implications in anti-SARS-CoV-2 drug discovery and in general in the drug optimization process.