SARS-CoV-2 ORF8 dimerization and binding mode analysis with class I MHC: computational approaches to identify COVID-19 inhibitors.

SARS-CoV-2 encodes eight accessory proteins, one of which, ORF8, has a poorly conserved sequence with SARS-CoV and its role in viral pathogenicity has recently been identified. ORF8 in SARS-CoV-2 has a unique functional feature that allows it to form a dimer structure linked by a disulfide bridge between Cys20 and Cys20 (S-S). This study provides structural characterization of natural mutant variants as well as the identification of potential drug candidates capable of binding directly to the interchain disulfide bridge. The lead compounds reported in this work have a tendency to settle in the dimeric interfaces by direct interaction with the disulfide bridge. These molecules may disturb the dimer formation and may have an inhibition impact on its potential functional role in host immune evasion and virulence pathogenicity. This work provides detailed insights on the sequence and structural variability through computational mutational studies, as well as potent drug candidates with the ability to interrupt the intermolecular disulfide bridge formed between Cys20 and Cys20. Furthermore, the interactions of ORF8 peptides complexed with MHC-1 is studied, and the binding mode reveals that certain ORF8 peptides bind to MHC-1 in a manner similar to other viral peptides. Overall, this study is a narrative of various computational approaches used to provide detailed structural insights into SARS-CoV-2 ORF8 interchain disulfide bond disruptors.

Intrinsically disordered proteins in viral pathogenesis and infections.

Disordered proteins serve a crucial part in many biological processes that go beyond the capabilities of ordered proteins. A large number of virus-encoded proteins have extremely condensed proteomes and genomes, which results in highly disordered proteins. The presence of these IDPs allows them to rapidly adapt to changes in their biological environment and play a significant role in viral replication and down-regulation of host defense mechanisms. Since viruses undergo rapid evolution and have a high rate of mutation and accumulation in their proteome, IDPs' insights into viruses are critical for understanding how viruses hijack cells and cause disease. There are many conformational changes that IDPs can adopt in order to interact with different protein partners and thus stabilize the particular fold and withstand high mutation rates. This chapter explains the molecular mechanism behind viral IDPs, as well as the significance of recent research in the field of IDPs, with the goal of gaining a deeper comprehension of the essential roles and functions played by viral proteins.

Viral hijacking mechanism in humans through protein-protein interactions.

Numerous viruses have evolved mechanisms to inhibit or alter the host cell's apoptotic response as part of their coevolution with their hosts. The analysis of virus-host protein interactions require an in-depth understanding of both the viral and host protein structures and repertoires, as well as evolutionary mechanisms and pertinent biological facts. Throughout the course of a viral infection, there is constant battle for binding between virus and cellular proteins. Exogenous interfaces facilitating viral-host interactions are well known for constantly targeting and suppressing endogenous interfaces mediating intraspecific interactions, such as viral-viral and host-host connections. In these interactions, the protein-protein interactions (PPIs), are mostly shown as networks (protein interaction networks, PINs), with proteins represented as nodes and their interactions represented as edges. Host proteins with a higher degree of connectivity are more likely to interact with viral proteins. Due to technical advancements, three-dimensional interactions may now be visualized computationally utilizing molecular modeling and cryo-EM approaches. The uniqueness of viral domain repertoires, their evolution, and their activities during viral infection make viruses fascinating models for research. This chapter aims to provide readers a complete picture of the viral hijacking mechanism in protein-protein interactions.

Immunological insights of selectins in human disease mechanism.

Selectin enzymes are glycoproteins and are an important adhesion molecule in the mammalian immune system, especially in the inflammatory response and the healing process of tissues. Selectins play an important role in a variety of biological processes, including the rolling of leukocytes in endothelial cells, a process known as the adhesion cascade. It has recently been discovered and reported that the selectin mechanism plays a role in cancer and thrombosis disease. This process begins with non-covalent interactions-based selectin-ligand binding and the glycans play a role as a connector between cancer cells and the endothelium in this process. The selectin mechanism is critical for the immune system, but it is also involved in disease mechanisms, earning the selectins the nickname "Selectins-The Two Dr. Jekyll and Mr. Hyde Faces". As a result, the drug for selectins should have a multifaceted role and be a dynamic molecule that targets the disease mechanism specifically. This chapter explores the role of selectins in the disease mechanism at the mechanism level that provides the impact for identifying the selectin inhibitors. Overall, this chapter provides the molecular level insights on selectins, their ligands, involvement in normal and disease mechanisms.

Artificial intelligence and machine learning approaches for drug design: challenges and opportunities for the pharmaceutical industries.

The global spread of COVID-19 has raised the importance of pharmaceutical drug development as intractable and hot research. Developing new drug molecules to overcome any disease is a costly and lengthy process, but the process continues uninterrupted. The critical point to consider the drug design is to use the available data resources and to find new and novel leads. Once the drug target is identified, several interdisciplinary areas work together with artificial intelligence (AI) and machine learning (ML) methods to get enriched drugs. These AI and ML methods are applied in every step of the computer-aided drug design, and integrating these AI and ML methods results in a high success rate of hit compounds. In addition, this AI and ML integration with high-dimension data and its powerful capacity have taken a step forward. Clinical trials output prediction through the AI/ML integrated models could further decrease the clinical trials cost by also improving the success rate. Through this review, we discuss the backend of AI and ML methods in supporting the computer-aided drug design, along with its challenge and opportunity for the pharmaceutical industry. From the available information or data, the AI and ML based prediction for the high throughput virtual screening. After this integration of AI and ML, the success rate of hit identification has gained a momentum with huge success by providing novel drugs.

Structural Understanding of SARS-CoV-2 Drug Targets, Active Site Contour Map Analysis and COVID-19 Therapeutics.

The pandemic, caused by Severe Acute Respiratory Syndrome Coronavirus 2 (SARSCoV- 2), is responsible for multiple worldwide lockdowns, an economic crisis, and a substantial increase in hospitalizations for viral pneumonia along with respiratory failure and multiorgan dysfunctions. Recently, the first few vaccines were approved by World Health Organization (WHO) and can eventually save millions of lives. Even though, few drugs are used in emergency like Remdesivir and several other repurposed drugs, still there is no approved drug for COVID-19. The coronaviral encoded proteins involved in host-cell entry, replication, and host-cell invading mechanism are potential therapeutic targets. This perspective review provides the molecular overview of SARS-CoV-2 life cycle for summarizing potential drug targets, structural insights, active site contour map analyses of those selected SARS-CoV-2 protein targets for drug discovery, immunology, and pathogenesis.

High-Throughput Screening and Quantum Mechanics for Identifying Potent Inhibitors Against Mac1 Domain of SARS-CoV-2 Nsp3.

SARS-CoV-2 encodes the Mac1 domain within the large nonstructural protein 3 (Nsp3), which has an ADP-ribosylhydrolase activity conserved in other coronaviruses. The enzymatic activity of Mac1 makes it an essential virulence factor for the pathogenicity of coronavirus (CoV). They have a regulatory role in counteracting host-mediated antiviral ADP-ribosylation, which is unique part of host response towards viral infections. Mac1 shows highly conserved residues in the binding pocket for the mono and poly ADP-ribose. Therefore, SARS-CoV-2 Mac1 enzyme is considered as an ideal drug target and inhibitors developed against them can possess a broad antiviral activity against CoV. ADP-ribose-1 phosphate bound closed form of Mac1 domain is considered for screening with large database of ZINC. XP docking and QPLD provides strong potential lead compounds, that perfectly fits inside the binding pocket. Quantum mechanical studies expose that, substrate and leads have similar electron donor ability in the head regions, that allocates tight binding inside the substrate-binding pocket. Molecular dynamics study confirms the substrate and new lead molecules presence of electron donor and acceptor makes the interactions tight inside the binding pocket. Overall binding phenomenon shows both substrate and lead molecules are well-adopt to bind with similar binding mode inside the closed form of Mac1.

Structure-Based Virtual Screening Reveals Ibrutinib and Zanubrutinib as Potential Repurposed Drugs against COVID-19.

Coronavirus disease (COVID)-19 is the leading global health threat to date caused by a severe acute respiratory syndrome coronavirus (SARS-CoV-2). Recent clinical trials reported that the use of Bruton's tyrosine kinase (BTK) inhibitors to treat COVID-19 patients could reduce dyspnea and hypoxia, thromboinflammation, hypercoagulability and improve oxygenation. However, the mechanism of action remains unclear. Thus, this study employs structure-based virtual screening (SBVS) to repurpose BTK inhibitors acalabrutinib, dasatinib, evobrutinib, fostamatinib, ibrutinib, inositol 1,3,4,5-tetrakisphosphate, spebrutinib, XL418 and zanubrutinib against SARS-CoV-2. Molecular docking is conducted with BTK inhibitors against structural and nonstructural proteins of SARS-CoV-2 and host targets (ACE2, TMPRSS2 and BTK). Molecular mechanics-generalized Born surface area (MM/GBSA) calculations and molecular dynamics (MD) simulations are then carried out on the selected complexes with high binding energy. Ibrutinib and zanubrutinib are found to be the most potent of the drugs screened based on the results of computational studies. Results further show that ibrutinib and zanubrutinib could exploit different mechanisms at the viral entry and replication stage and could be repurposed as potential inhibitors of SARS-CoV-2 pathogenesis.

Microsecond MD Simulation and Multiple-Conformation Virtual Screening to Identify Potential Anti-COVID-19 Inhibitors Against SARS-CoV-2 Main Protease.

The recent pandemic outbreak of COVID-19, caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), raised global health and economic concerns. Phylogenetically, SARS-CoV-2 is closely related to SARS-CoV, and both encode the enzyme main protease (Mpro/3CLpro), which can be a potential target inhibiting viral replication. Through this work, we have compiled the structural aspects of Mpro conformational changes, with molecular modeling and 1-µs MD simulations. Long-scale MD simulation resolves the mechanism role of crucial amino acids involved in protein stability, followed by ensemble docking which provides potential compounds from the Traditional Chinese Medicine (TCM) database. These lead compounds directly interact with active site residues (His41, Gly143, and Cys145) of Mpro, which plays a crucial role in the enzymatic activity. Through the binding mode analysis in the S1, S1', S2, and S4 binding subsites, screened compounds may be functional for the distortion of the oxyanion hole in the reaction mechanism, and it may lead to the inhibition of Mpro in SARS-CoV-2. The hit compounds are naturally occurring compounds; they provide a sustainable and readily available option for medical treatment in humans infected by SARS-CoV-2. Henceforth, extensive analysis through molecular modeling approaches explained that the proposed molecules might be promising SARS-CoV-2 inhibitors for the inhibition of COVID-19, subjected to experimental validation.

Computational studies reveal piperine, the predominant oleoresin of black pepper (Piper nigrum) as a potential inhibitor of SARS-CoV-2 (COVID-19)

In this study, we screened 26 bioactive compounds present in various spices for activity against SARS-CoV-2 using molecular docking. Results showed that piperine, present in black pepper had a high binding affinity (-7.0 kCal/mol) than adenosine monophosphate (-6.4 kCal/mol) towards the RNA-binding pocket of the nucleocapsid. Molecular dynamics simulation of the docked complexes confirmed the stability of piperine docked to nucleocapsid protein as a potential inhibitor of the RNA-binding site. Therefore, piperine seems to be potential candidate to inhibit the packaging of RNA in the nucleocapsid and thereby inhibiting the viral proliferation. This study suggests that consumption of black pepper may also help to combat SARS-CoV-2 directly through possible antiviral effects, besides its immunomodulatory functions.

Targeting COVID-19 (SARS-CoV-2) main protease through active phytochemicals of ayurvedic medicinal plants - Withania somnifera (Ashwagandha), Tinospora cordifolia (Giloy) and Ocimum sanctum (Tulsi) - a molecular docking study.

COVID-19 (Coronavirus disease 2019) is a transmissible disease initiated and propagated through a new virus strain SARS-CoV-2 (Severe Acute Respiratory Syndrome Coronavirus-2) since 31st December 2019 in Wuhan city of China and the infection has outspread globally influencing millions of people. Here, an attempt was made to recognize natural phytochemicals from medicinal plants, in order to reutilize them against COVID-19 by the virtue of molecular docking and molecular dynamics (MD) simulation study. Molecular docking study showed six probable inhibitors against SARS-CoV-2 Mpro (Main protease), two from Withania somnifera (Ashwagandha) (Withanoside V [10.32 kcal/mol] and Somniferine [9.62 kcal/mol]), one from Tinospora cordifolia (Giloy) (Tinocordiside [8.10 kcal/mol]) and three from Ocimum sanctum (Tulsi) (Vicenin [8.97 kcal/mol], Isorientin 4'-O-glucoside 2″-O-p-hydroxybenzoagte [8.55 kcal/mol] and Ursolic acid [8.52 kcal/mol]). ADMET profile prediction showed that the best docked phytochemicals from present work were safe and possesses drug-like properties. Further MD simulation study was performed to assess the constancy of docked complexes and found stable. Hence from present study it could be suggested that active phytochemicals from medicinal plants could potentially inhibit Mpro of SARS-CoV-2 and further equip the management strategy against COVID-19-a global contagion. HighlightsHolistic approach of Ayurvedic medicinal plants to avenge against COVID-19 pandemic.Active phytoconstituents of Ayurvedic medicinal plants Withania somnifera (Ashwagandha), Tinospora cordifolia (Giloy) and Ocimum sanctum (Tulsi) predicted to significantly hinder main protease (Mpro or 3Clpro) of SARS-CoV-2.Through molecular docking and molecular dynamic simulation study, Withanoside V, Somniferine, Tinocordiside, Vicenin, Ursolic acid and Isorientin 4'-O-glucoside 2″-O-p-hydroxybenzoagte were anticipated to impede the activity of SARS-CoV-2 Mpro.Drug-likeness and ADMET profile prediction of best docked compounds from present study were predicted to be safe, drug-like compounds with no toxicity.Communicated by Ramaswamy H. Sarma.

Shape-based Machine Learning Models for the Potential Novel COVID-19 Protease Inhibitors Assisted by Molecular Dynamics Simulation.

BACKGROUND: The vast geographical expansion of novel coronavirus and an increasing number of COVID-19 affected cases have overwhelmed health and public health services. Artificial Intelligence (AI) and Machine Learning (ML) algorithms have extended their major role in tracking disease patterns, and in identifying possible treatments. OBJECTIVE: This study aims to identify potential COVID-19 protease inhibitors through shape-based Machine Learning assisted by Molecular Docking and Molecular Dynamics simulations. METHODS: 31 Repurposed compounds have been selected targeting the main coronavirus protease (6LU7) and a machine learning approach was employed to generate shape-based molecules starting from the 3D shape to the pharmacophoric features of their seed compound. Ligand-Receptor Docking was performed with Optimized Potential for Liquid Simulations (OPLS) algorithms to identify highaffinity compounds from the list of selected candidates for 6LU7, which were subjected to Molecular Dynamic Simulations followed by ADMET studies and other analyses. RESULTS: Shape-based Machine learning reported remdesivir, valrubicin, aprepitant, and fulvestrant as the best therapeutic agents with the highest affinity for the target protein. Among the best shape-based compounds, a novel compound identified was not indexed in any chemical databases (PubChem, Zinc, or ChEMBL). Hence, the novel compound was named 'nCorv-EMBS'. Further, toxicity analysis showed nCorv-EMBS to be suitable for further consideration as the main protease inhibitor in COVID-19. CONCLUSION: Effective ACE-II, GAK, AAK1, and protease 3C blockers can serve as a novel therapeutic approach to block the binding and attachment of the main COVID-19 protease (PDB ID: 6LU7) to the host cell and thus inhibit the infection at AT2 receptors in the lung. The novel compound nCorv- EMBS herein proposed stands as a promising inhibitor to be evaluated further for COVID-19 treatment.

Bacterial protein azurin and derived peptides as potential anti-SARS-CoV-2 agents: insights from molecular docking and molecular dynamics simulations.

The current pandemic SARS-CoV-2 has wreaked havoc in the world, and neither drugs nor vaccine is available for the treatment of this disease. Thus, there is an immediate need for novel therapeutics that can combat this deadly infection. In this study, we report the therapeutic assessment of azurin and its peptides: p18 and p28 against the viral structural S-protein and non-structural 3CLpro and PLpro proteins. Among the analyzed complexes, azurin docked relatively well with the S2 domain of S-protein compared to the other viral proteins. The derived peptide p18 bound to the active site domain of the PLpro protein; however, in other complexes, lesser interactions were recorded. The second azurin derived peptide p28, fared the best among the docked proteins. p28 interacted with all the three viral proteins and the host ACE-2 receptor by forming several electrostatic and hydrogen bonds with the S-protein, 3CLpro, and PLpro. MD simulations indicated that p28 exhibited a strong affinity to S-protein and ACE-2 receptor, indicating a possibility of p28 as a protein-protein interaction inhibitor. Our data suggest that the p28 has potential as an anti-SARS-CoV-2 agent and can be further exploited to establish its validity in the treatment of current and future SARS-CoV crisis.Communicated by Ramaswamy H. Sarma.

Structure-based virtual screening and molecular dynamics simulation of SARS-CoV-2 Guanine-N7 methyltransferase (nsp14) for identifying antiviral inhibitors against COVID-19.

The recent pandemic caused by Severe Acute Respiratory Syndrome Coronavirus-2 (SARS-CoV-2) calls the whole world into a medical emergency. For tackling Coronavirus Disease 2019 (COVID-19), researchers from around the world are swiftly working on designing and identifying inhibitors against all possible viral key protein targets. One of the attractive drug targets is guanine-N7 methyltransferase which plays the main role in capping the 5'-ends of viral genomic RNA and sub genomic RNAs, to escape the host's innate immunity. We performed homology modeling and molecular dynamic (MD) simulation, in order to understand the molecular architecture of Guanosine-P3-Adenosine-5',5'-Triphosphate (G3A) binding with C-terminal N7-MTase domain of nsp14 from SARS-CoV-2. The residue Asn388 is highly conserved in present both in N7-MTase from SARS-CoV and SARS-CoV-2 and displays a unique function in G3A binding. For an in-depth understanding of these substrate specificities, we tried to screen and identify inhibitors from the Traditional Chinese Medicine (TCM) database. The combination of several computational approaches, including screening, MM/GBSA, MD simulations, and PCA calculations, provides the screened compounds that readily interact with the G3A binding site of homology modeled N7-MTase domain. Compounds from this screening will have strong potency towards inhibiting the substrate-binding and efficiently hinder the viral 5'-end RNA capping mechanism. We strongly believe the final compounds can become COVID-19 therapeutics, with huge international support.[Formula: see text]The focus of this study is to screen for antiviral inhibitors blocking guanine-N7 methyltransferase (N7-MTase), one of the key drug targets involved in the first methylation step of the SARS-CoV-2 RNA capping mechanism. Compounds binding the substrate-binding site can interfere with enzyme catalysis and impede 5'-end cap formation, which is crucial to mimic host RNA and evade host cellular immune responses. Therefore, our study proposes the top hit compounds from the Traditional Chinese Medicine (TCM) database using a combination of several computational approaches.Communicated by Ramaswamy H. Sarma.

Identification of new anti-nCoV drug chemical compounds from Indian spices exploiting SARS-CoV-2 main protease as target.

The 2019-novel coronavirus (nCoV) has caused a global health crisis by causing coronavirus disease-19 (COVID-19) pandemic in the human population. The unavailability of specific vaccines and anti-viral drug for nCoV, science demands sincere efforts in the field of drug design and discovery for COVID-19. The novel coronavirus main protease (SARS-CoV-2 Mpro) play a crucial role during the disease propagation, and hence SARS-CoV-2 Mpro represents as a drug target for the drug discovery. Herein, we have applied bioinformatics approach for screening of chemical compounds from Indian spices as potent inhibitors of SARS-CoV-2 main protease (PDBID: 6Y84). The structure files of Indian spices chemical compounds were taken from PubChem database or Zinc database and screened by molecular docking, by using AutoDock-4.2, MGLTools-1.5.6, Raccoon virtual screening tools. Top 04 hits based on their highest binding affinity were analyzed. Carnosol exhibited highest binding affinity -8.2 Kcal/mol and strong and stable interactions with the amino acid residues present on the active site of SARS-CoV-2 Mpro. Arjunglucoside-I (-7.88 Kcal/mol) and Rosmanol (-7.99 Kcal/mol) also showed a strong and stable binding affinity with favourable ADME properties. These compounds on MD simulations for 50 ns shows strong hydrogen-bonding interactions with the protein active site and remains stable inside the active site. Our virtual screening results suggest that these small chemical molecules can be used as potential inhibitors against SARS-CoV-2 Mpro and may have an anti-viral effect on nCoV. However, further validation and investigation of these inhibitors against SARS-CoV-2 main protease are needed to claim their candidacy for clinical trials.Communicated by Ramaswamy H. Sarma.