Molecular Docking and Simulation Analysis of Cyclopeptides as Anticancer Agents

Background: Cancer is a leading cause of death for people worldwide, in addition to the rise in mortality rates attributed to the Covid epidemic. This allows scientists to do additional research. Here, we have selected Integerrimide A, cordy heptapeptide, and Oligotetrapeptide as the three cyclic proteins that will be further studied and investigated in this context.Methods: Docking research was carried out using the protein complexes 1FKB and 1YET, downloaded from the PDB database and used in the docking investigations. Cyclopeptides have been reported to bind molecularly to human HSP90 (Heat shock protein) and FK506. It was possible to locate HSP90 in Protein Data Banks 1YET and 1FKB. HSP90 was retrieved from Protein Data Bank 1YET and 1FKB. Based on these findings, it is possible that the anticancer effects of Int A, Cordy, and Oligo substances could be due to their ability to inhibit the mTOR rapamycin binding domain and the HSP90 Geldanamycin binding domain via the mTOR and mTOR chaperone pathways. During the calculation, there were three stages: system development, energy reduction, and molecular dynamics (also known as molecular dynamics). Each of the three compounds demonstrated a binding affinity for mTOR's Rapamycin binding site that ranged from -6.80 to -9.20 Kcal/mol (FKB12).Results: An inhibition constant Ki of 181.05 nM characterized Cordy A with the highest binding affinity (-9.20 Kcal/mol). Among the three tested compounds, Cordy A was selected for MD simulation. HCT116 and B16F10 cell lines were used to test each compound's anticancer efficacy. Doxorubicin was used as a standard drug. The cytotoxic activity of substances Int A, Cordy A, and Oligo on HCT116 cell lines was found to be 77.65 μM, 145.36 μM, and 175.54 μM when compared to Doxorubicin 48.63 μM, similarly utilizing B16F10 cell lines was found to be 68.63 μM, 127.63 μM, and 139.11 μM to Doxorubicin 45.25 μM.Conclusion: Compound Cordy A was more effective than any other cyclic peptides tested in this investigation.

Interaction between target peptide and complementary peptide by bimolecular fluorescence complementary technology

[Objective] Using the bimolecular fluorescence complementation (BiFC) technology, the present experiment aimed to study the interaction relationship and localization of the target peptide and the complementary peptide based on the porcine epidemic diarrhea virus (PEDV) S protein receptor binding site peptide in living cells, so as to provide the foundation and theoretical support for the further use of the peptide in the detection of porcine epidemic diarrhea virus. [Method] The target peptide was designed according to the physical and chemical characteristics of the target protein, such as the amino acid composition, the type of charge, the ability to form intennolecular hydrogen bonds, the strength of polarity, and hydrophobicity;According to the amino acid composition of the target protein, a complementary peptide that interacted with it in theory was designed, and the target peptide and complementary peptide were predicted and analyzed by using bioinfonnatics tools;The target peptide and complementary peptide were inserted into the pBiFC-VC155 and pBiFC-VN173 vector, which was double digested by the EcoRI/XhoI and NotI/SalI, respectively, verified by enzyme digestion and sequencing, and then transfected into Vero cells to study the interaction between the target peptide and the complementary peptide, and the precise localization of BiFC complex in cells. [Result] Bioinfonnatics analysis showed that the target peptide and complementary peptide had hydrophilic and hydrophobic domains, respectively, and the hydrophilic domains were both positively and negatively charged, which could generate electrostatic attraction. The results of enzyme digestion and sequencing showed that the pBiFC-VC155-target peptide and pBiFC-VNI73-complementary peptide plasmids were successfully constructed;Cell transfection experiments showed that the target peptide and complementary peptide could form BiFC complexes in Vcro cells after co-transfection of recombinant plasmids, indicating that they could interact with each other;Indirect immuttolluorescence assay confirmed that the BiFC complex was mainly distributed in the nucleus. [Conclusion] It was confirmed that the peptide designed based on the PEW/ S protein receptor binding site can interact with each other in living cells, demonstrating the feasibility of the peptide for detection.

Evaluation the use of ramipril and candesartan for COVID-19 inpatiens with hypertension comorbid at RSUD dr. moewardi

Hypertension is the most common comorbid in patients infected by COVID-19. Drugs often given for the treatment of hypertension, namely ramipril, and candesartan, are thought to increase the development of COVID-19 because the angiotensin-converting enzyme inhibitors (ACEI) and angiotensin receptor blockers (ARBs) increase the expression of ACE-2, which is the binding site of SARS-CoV-2. This study aims to analyze the effect of using ramipril and candesartan on healing in hospitalized patients with COVID-19 with hypertension at RSUD dr. Moewardi in 2020. This study was conducted retrospectively. The results were presented descriptively and used medical record data of hospitalized patients with COVID-19 with comorbid hypertension at RSUD dr. Moewardi in 2020. Using ramipril and candesartan affects patient recovery in terms of length of stay (LOS), symptom relief, and RT-PCR test results. Analysis of the research data using SPSS with Spearman correlation test. The Spearman correlation test showed that there was a fairly strong relationship between the use of ramipril and candesartan in patients with COVID-19 with comorbid hypertension on LOS, symptom relief, and RT-PCR test results. The study results show that using ramipril and candesartan can improve the recovery of patients with COVID-19 with comorbid hypertension. ACEI and ARB drugs have not been proven to worsen the patient's condition so their use can be continued.

Structural and Computational Studies of Cobalt(II) and Copper(II) Complexes with Aromatic Heterocyclic Ligand

Two coordination complexes, a cobalt(II) complex tris(1,10-phenanthroline)-cobalt perchlorate hydrate, [Co(phen)3]·(ClO4)2·H2O(1), and a copper(II) complex tris(1,10-phenanthroline)-copper perchlorate 4-bromo-2-{[(naphthalene-1-yl)imino]methyl}phenol hydrate, [Cu(phen)3]·(ClO4)2·HL·[O] (2), [where, phen = 1,10-phenathroline as aromatic heterocyclic ligand, HL = 4-bromo-2-((Z)-(naphthalene-4-ylimino) methyl) phenol] have been synthesized and structurally characterized. Single crystal X-ray analysis of both complexes has revealed the presence of a distorted octahedral geometry around cobalt(II) and copper(II) ions. density functional theory (DFT)-based quantum chemical calculations were performed on the cationic complex [Co(phen)3]2+ and copper(II) complex [Cu(phen)3]2+ to get the structure property relationship. Hirshfeld surface and 2-D fingerprint plots have been explored in the crystal structure of both the metal complexes. To find potential SARS-CoV-2 drug candidates, both the complexes were subjected to molecular docking calculations with SARS-CoV-2 virus (PDB ID: 7BQY and 7C2Q). We have found stable docked structures where docked metal chelates could readily bound to the SARS-CoV-2 Mpro. The molecular docking calculations of the complex (1) into the 7C2Q-main protease of SARS-CoV-2 virus revealed the binding energy of −9.4 kcal/mol with a good inhibition constant of 1.834 µM, while complex (2) exhibited the binding energy of −9.0 kcal/mol, and the inhibition constant of 1.365 µM at the inhibition binding site of receptor protein. Overall, our in silico studies explored the potential role of cobalt(II) complex (1), and copper(II) complex (2) complex as the viable and alternative therapeutic solution for SARS-CoV-2.

Investigating structural features of dimeric SARS-CoV-2 Mpro catalytic site with bound covalent ligands at physiological temperature

The SARS-CoV-2 main protease (Mpro) plays an important role in the viral transcription and replication of the SARS-CoV-2 virus that is causing the Covid-19 pandemic worldwide. Therefore, it represents a very attractive target for drug development for treatment of this disease. It is a cysteine protease because it has in the active site the catalytic dyad composed of cysteine (C145) and histidine (H41). The catalytic site represents the binding site for inhibitors, many of them bind to Mpro with a covalent bond. In this research, structural and physiochemical characteristics of the Mpro binding site are investigated when the ligand 11a is covalently and non-covalently bound. All-atom molecular dynamics (MD) simulations were run for 500 ns at physiological temperature (310 K). It is found that conformations of both the Mpro protein and the ligand are stable during the simulation with covalently bound complex showing stronger stability. When the ligand is covalently bound (its final state), residues that stably interact with the ligand are H41, C145, H163, H164 and E166. The optimal conformation of these residues is stabilized also via the Hbond interactions with the catalytic water present in the Mpro binding site. In the case of the non-covalently bound ligand (state before the covalent bond is formed), the binding site residues retain their conformations similar to the covalent binding site, and they still form Hbonds with the catalytic water, except H41. This residue, instead, adopts a different conformation and looses the Hbond with the catalytic water, leaving more freedom to move to the ligand. We hypothesize that H41 could play a role in guiding the ligand to the optimal position for final covalent bonding. Further analyses are in process to check this hypothesis. These results represent an important basis for studying drug candidates against SARS-CoV-2 by means of computer aided drug design.

How to Discover Antiviral Drugs Quickly

Molecular-dynamics simulations together with virtual high-throughput screening provide a means of quick evaluation of existing drugs for antiviral activity. The authors explain how these methods serve in the quest for drugs to treat Covid-19.

Identifying promising druggable binding sites and their flexibility to target the receptor-binding domain of SARS-CoV-2 spike protein.

The spike protein of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is crucial for viral infection. The interaction of its receptor-binding domain (RBD) with the human angiotensin-converting enzyme 2 (ACE2) protein is required for the virus to enter the host cell. We identified RBD binding sites to block its function with inhibitors by combining the protein structural flexibility with machine learning analysis. Molecular dynamics simulations were performed on unbound or ACE2-bound RBD conformations. Pockets estimation, tracking and druggability prediction were performed on a large sample of simulated RBD conformations. Recurrent druggable binding sites and their key residues were identified by clustering pockets based on their residue similarity. This protocol successfully identified three druggable sites and their key residues, aiming to target with inhibitors for preventing ACE2 interaction. One site features key residues for direct ACE2 interaction, highlighted using energetic computations, but can be affected by several mutations of the variants of concern. Two highly druggable sites, located between the spike protein monomers interface are promising. One weakly impacted by only one Omicron mutation, could contribute to stabilizing the spike protein in its closed state. The other, currently not affected by mutations, could avoid the activation of the spike protein trimer.

Development of Machine Learning Regression Model for COVID-19 Drug Target Prediction

There is a perennial need to identify novel, effective therapeutic agents to combat rising infections. Recently, prediction of therapeutic targets to decrease the impact of COVID-19 has posed an urgent challenge requiring innovative solutions. Successful identification of novel drug-target combinations may greatly facilitate drug development. To meet this need, we developed a COVID-19 drug target prediction model using machine learning approaches to quickly identify drug candidates for 18 COVID-19 protein targets. Specifically, we analyzed the performance of three prediction models to predict drug-target docking scores, which represents the strength of interactions between ligands and proteins. Docking scores were predicted for 300,457 molecules on 18 different COVID-19 related protein docking targets. Our proposed approach achieved a competitive performance with mathrm{R}-{2}=0.69,MAE=0.285, MSE=0.627. In addition, we identify chemical structures associated with stronger binding affinities across target binding sites. We believe our work could potentially save pharmaceutical companies significant resources, especially during the early stages of drug development. © 2022 IEEE.

Computational Analysis of Receptor-Binding Domains of SARS-CoV-2 to Reveal the Mechanism of Immune Escape

Covid-19 has become a world pandemic for years. With the appearance of mutations, immune escape has become a problem, reducing the effectiveness of vaccines and antibodies. To reveal the mechanism of immune escape, we analyze the geometrical properties of the receptor-binding domain in the SARS-CoV-2 spike protein, which plays a vital role in the immune reaction. Several important variants are taken as examples, and the wild type model is prepared as a reference. The computational method is applied to simulate the behaviors of the models, and alpha shape algorithm is employed to extract geometrical data of the protein surface. Average moving distance of the surface atoms is used to quantify their activity. Our results show that the mutations changed the properties of the protein. The variants have different distributions of active sites, which may change the specific antigenicity and influence the binding abilities of drugs and antibodies. This study explains the mechanism of immune escape of SARS-CoV-2, and provides a geometrical method to find potential new target sites for the design of drugs and vaccines. © 2022 IEEE.

DapNet-HLA: Adaptive dual-attention mechanism network based on deep learning to predict non-classical HLA binding sites.

Human leukocyte antigen (HLA) plays a vital role in immunomodulatory function. Studies have shown that immunotherapy based on non-classical HLA has essential applications in cancer, COVID-19, and allergic diseases. However, there are few deep learning methods to predict non-classical HLA alleles. In this work, an adaptive dual-attention network named DapNet-HLA is established based on existing datasets. Firstly, amino acid sequences are transformed into digital vectors by looking up the table. To overcome the feature sparsity problem caused by unique one-hot encoding, the fused word embedding method is used to map each amino acid to a low-dimensional word vector optimized with the training of the classifier. Then, we use the GCB (group convolution block), SENet attention (squeeze-and-excitation networks), BiLSTM (bidirectional long short-term memory network), and Bahdanau attention mechanism to construct the classifier. The use of SENet can make the weight of the effective feature map high, so that the model can be trained to achieve better results. Attention mechanism is an Encoder-Decoder model used to improve the effectiveness of RNN, LSTM or GRU (gated recurrent neural network). The ablation experiment shows that DapNet-HLA has the best adaptability for five datasets. On the five test datasets, the ACC index and MCC index of DapNet-HLA are 4.89% and 0.0933 higher than the comparison method, respectively. According to the ROC curve and PR curve verified by the 5-fold cross-validation, the AUC value of each fold has a slight fluctuation, which proves the robustness of the DapNet-HLA. The codes and datasets are accessible at https://github.com/JYY625/DapNet-HLA.

Identification of Drug Combination Therapies for SARS-CoV-2: A Molecular Dynamics Simulations Approach.

Purpose: The development of effective treatments for coronavirus infectious disease 19 (COVID-19) caused by SARS-Coronavirus-2 was hindered by the little data available about this virus at the start of the pandemic. Drug repurposing provides a good strategy to explore approved drugs' possible SARS-CoV-2 antiviral activity. Moreover, drug synergism is essential in antiviral treatment due to improved efficacy and reduced toxicity. In this work, we studied the effect of approved and investigational drugs on one of SARS-CoV-2 essential proteins, the main protease (Mpro), in search of antiviral treatments and/or drug combinations. Methods: Different possible druggable sites of Mpro were identified and screened against an in-house library of more than 4000 chemical compounds. Molecular dynamics simulations were carried out to explore conformational changes induced by different ligands' binding. Subsequently, the inhibitory effect of the identified compounds and the suggested drug combinations on the Mpro were established using a 3CL protease (SARS-CoV-2) assay kit. Results: Three potential inhibitors in three different binding sites were identified; favipiravir, cefixime, and carvedilol. Molecular dynamics simulations predicted the synergistic effect of two drug combinations: favipiravir/cefixime, and favipiravir/carvedilol. The in vitro inhibitory effect of the predicted drug combinations was established on this enzyme. Conclusion: In this work, we could study one of the promising SARS-CoV-2 viral protein targets in searching for treatments for COVID-19. The inhibitory effect of several drugs on Mpro was established in silico and in vitro assays. Molecular dynamics simulations showed promising results in predicting the synergistic effect of drug combinations.

Site-selective generation of lanthanoid binding sites on proteins using 4-fluoro-2,6-dicyanopyridine

The paramagnetism of a lanthanoid tag site-specifically installed on a protein provides a rich source of structural information accessible by nuclear magnetic resonance (NMR) and electron paramagnetic resonance (EPR) spectroscopy. Here we report a lanthanoid tag for selective reaction with cysteine or selenocysteine with formation of a (seleno)thioether bond and a short tether between the lanthanoid ion and the protein backbone. The tag is assembled on the protein in three steps, comprising (i) reaction with 4-fluoro-2,6-dicyanopyridine (FDCP);(ii) reaction of the cyano groups withα-cysteine, penicillamine or β-cysteine to complete the lanthanoid chelating moiety;and (iii) titration with a lanthanoid ion. FDCP reacts much faster with selenocysteine than cysteine, opening a route for selective tagging in the presence of solvent-exposed cysteine residues. Loaded with Tb3+ and Tm3+ ions, pseudocontact shifts were observed in protein NMR spectra, confirming that the tag delivers good immobilisation of the lanthanoid ion relative to the protein, which was also manifested in residual dipolar couplings. Completion of the tag with different 1,2-aminothiol compounds resulted in different magnetic susceptibility tensors. In addition, the tag proved suitable for measuring distance distributions in double electron–electron resonance experiments after titration with Gd3+ ions.

Identification of hACE2-interacting sites in SARS-CoV-2 spike receptor binding domain for antiviral drugs screening.

The key structure of the interface between the spike protein of Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) and human angiotensin-converting enzyme 2 (hACE2) acts as an essential switch for cell entry by the virus and drugs targets. However, this is largely unknown. Here, we tested three peptides of spike receptor binding domain (RBD) and found that peptide 391-465 aa is the major hACE2-interacting sites in SARS-CoV-2 spike RBD. We then identified essential amino acid residues (403R, 449Y, 454R) of peptide 391-465 aa that were critical for the interaction between the RBD and hACE2. Additionally, a pseudotyped virus containing SARS-CoV-2 spike with individual mutation (R454G, Y449F, R403G, N439I, or N440I) was determined to have very low infectivity compared with the pseudotyped virus containing the wildtype (WT) spike from reference strain Wuhan 1, respectively. Furthermore, we showed the key amino acids had the potential to drug screening. For example, molecular docking (Docking) and infection assay showed that Cephalosporin derivatives can bind with the key amino acids to efficiently block infection of the pseudoviruses with wild type spike or new variants. Moreover, Cefixime inhibited live SARS-CoV-2 infection. These results also provide a novel model for drug screening and support further clinical evaluation and development of Cephalosporin derivatives as novel, safe, and cost-effective drugs for prevention/treatment of SARS-CoV-2.

Potential of Reverse Transcription Loop-Mediated Isothermal Amplification (RT-LAMP) as a Simple Rapid Molecular Diagnostic Tool to Detect SARS-CoV-2 Nucleocapsid Gene

Introduction: The Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2), identified in December of 2019, is the cause of the coronavirus disease 2019 (COVID-19). Due to the high reproductive rate of the virus, the best way to slow down the spread is to identify and isolate patients at the early stage of infections. The current diagnostic methods are either too expensive, slow or have low accuracy. Variants of SARS-CoV-2 with mutations at the primer binding sites may cause evasion of polymerase chain reaction (PCR) detection using current primers. Reverse transcription loop-mediated isothermal amplification (RT-LAMP) has potential as a rapid molecular test that is easy to conduct. Methods: LAMP primers were design based on the highly conserved regions of the SARS-CoV-2 Nucleocapsid (N) gene. RT-LAMP assays were conducted using an optimized Bst 3.0 polymerase protocol on T7 RNA polymerase synthesized RNA template. The LAMP sensitivity assay was tested on 1:10 serial diluted pJET1.2 vector with SARSCoV2 N gene inserts. A specificity test was conducted by running the test on plasmids containing SARS-CoV and MERS-CoV N genes. The results were visualised via gel electrophoresis, SYBR Green staining and Lateral Flow Dipstick (LFD). Results: The optimized protocol is sensitive enough to detect SARS-CoV-2 genetic material within 10 minutes but is most sensitive at 30 minutes. Additionally, it is specific to only the genetic materials of SARS-CoV-2. Furthermore, an LFD with multiple test lines was successful for multiplexed LAMP reactions with different genic regions of the virus. Conclusion: The multiplexed LFD-LAMP is potentially a simple yet specific and sensitive method of rapid molecular diagnostics of COVID-19.

OMICRON-THE VARIANT OF CONCERN (VoC): A RAPID REVIEW

A new variant of coronavirus B.1.1.529. appeared on the scene, when discovered by the researchers in South Africa on Nov 24.2021. It is a heavily mutated variant of coronavirus discovered thus far with over 50+ mutations with 32 mutations over the spike protein itself. Spike proteins help the virus to bind to the bodily receptors of humans to gain entry inside. In comparison to the delta variant, which had nine mutations, it means that Omicron has better chances of evading the host immunity and is also more transmissible. And rightly so, it has been declared as a variant of concern(V°C) by the WHO. The presence of S-gene is of the determinants for the detection of the virus. But omicron seems to have missed this gene- being called as S-gene dropout or S-gene target failure (S- spike glycoprotein).

EXSCALATE: An Extreme-Scale Virtual Screening Platform for Drug Discovery Targeting Polypharmacology to Fight SARS-CoV-2

The social and economic impact of the COVID-19 pandemic demands a reduction of the time required to find a therapeutic cure. In this paper, we describe the EXSCALATE molecular docking platform capable to scale on an entire modern supercomputer for supporting extreme-scale virtual screening campaigns. Such virtual experiments can provide in short time information on which molecules to consider in the next stages of the drug discovery pipeline, and it is a key asset in case of a pandemic. The EXSCALATE platform has been designed to benefit from heterogeneous computation nodes and to reduce scaling issues. In particular, we maximized the accelerators’usage, minimized the communications between nodes, and aggregated the I/O requests to serve them more efficiently. Moreover, we balanced the computation across the nodes by designing an ad-hoc workflow based on the execution time prediction of each molecule. We deployed the platform on two HPC supercomputers, with a combined computational power of 81 PFLOPS, to evaluate the interaction between 70 billion of small molecules and 15 binding-sites of 12 viral proteins of SARS-CoV-2. The experiment lasted 60 hours and it performed more than one trillion ligand-pocket evaluations, setting a new record on the virtual screening scale. IEEE

In Silico Study of Alkaloids: Neferine and Berbamine Potentially Inhibit the SARS-CoV-2 RNA-Dependent RNA Polymerase

The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the virus that causes COVID-19, has been a global concern. While there have been some vaccines and drugs, the rapid emergence of variants due to mutations has threatened public health. As the de novo drug development process is expensive and time-consuming, repurposing existing antiviral drugs against SARS-CoV-2 is an alternative and promising approach to mitigate the current situation. Several studies have indicated that some natural products exhibit inhibitory activities against SARS-CoV-2. This study is aimed at analyzing the potential of natural alkaloids, using various computational tools, as drug candidates against SARS-CoV-2. The molecular docking analysis predicted that naturally occurring alkaloids can bind with RNA-dependent RNA-polymerase (RdRP). The QSAR analysis was conducted by using the way2drug/PASS online web resource, and the pharmacokinetics and toxicity properties of these alkaloids were predicted using pkCSM, SwissADME, and ProTox-II webserver. Among the different alkaloids studied, neferine and berbamine were repurposed as potential drug candidates based on their binding affinity and interactions with RdRP. Further, molecular dynamics simulation of 90 ns revealed the conformational stability of the neferine-RdRP complex.

Compatibility of the Ligand Binding Sites in the Spike Glycoprotein of COVID-19 with those in the Aminopeptidase and the Caveolins 1, 2 Proteins

A novel severe viral pneumonia emerged in Wuhan city, China, in December 2019. The spike glycoprotein of the SARS-CoV-2 plays a crucial role in the viral entry to the host cell and eliciting a strong response for antibody-mediated neutralization in mice. Caveolins 1,2 are scaffolding proteins dovetailed as a co-stimulatory signal essential for T-cell receptor and activation. Aminopeptidase is a membrane protein acting as a receptor for human coronavirus within the S1 subunit of the spike glycoprotein. Vaccines for COVID-19 have become a priority for predisposition against the outbreak, so that our study aimed to find interaction sites between SP of SARS-CoV-2 and CAV1, CAV2, and AMPN. Methods: Amino acids motif search was employed to predict the possible CAV1, CAV2, and AMPN related interaction domains in the SARS-CoV-2 SP In silico analysis. Results: Interactions between proteins revealed 5 and16 residues. ZN ligand binding site is matched between AMPN and SARS- CoV-2 SP. HLA-A·74:01 allele is the best CTL epitope for SP. We identified seven B-cell epitopes specifically for SARS-CoV-2 SP. Conclusions: SARS-CoV-2 SP binding sites might be compatible with AMPN ligand binding sites. The limit score was detected for ligand binding sites of CAV1 and CAV2. Our findings might be critical for the further substantial study of vaccine production strategy.

Enhancement of Antibodies Neutralize Omicron variants in The Presence of Booster Vaccine Doses and Monoclonal Antibodies

The Omicron variant is quickly becoming the most common SARS-CoV-2 virus spreading throughout the world. To understand probable loss of protection against Omicron infection, it's critical to identify declines in viral neutralizing activity in serum of convalescent or vaccinated people. Antibodies against Omicron and various variants have been detected by scientists. These antibodies target non-evolving regions of the viral spike protein. A booster dose improves the quality and amount of the humoral immune response, which has been related to better protection against the disease's more severe signs. Vaccines and boosters must be provided promptly around the world to stop the virus from spreading.

Identification of vaccine targets & design of vaccine against SARS-CoV-2 coronavirus using computational and deep learning-based approaches

An unusual pneumonia infection, named COVID-19, was reported on December 2019 in China. It was reported to be caused by a novel coronavirus which has infected approximately 220 million people worldwide with a death toll of 4.5 million as of September 2021. This study is focused on finding potential vaccine candidates and designing an in-silico subunit multi-epitope vaccine candidates using a unique computational pipeline, integrating reverse vaccinology, molecular docking and simulation methods. A protein named spike protein of SARS-CoV-2 with the GenBank ID QHD43416.1 was shortlisted as a potential vaccine candidate and was examined for presence of B-cell and T-cell epitopes. We also investigated antigenicity and interaction with distinct polymorphic alleles of the epitopes. High ranking epitopes such as DLCFTNVY (B cell epitope), KIADYNKL (MHC Class-I) and VKNKCVNFN (MHC class-II) were shortlisted for subsequent analysis. Digestion analysis verified the safety and stability of the shortlisted peptides. Docking study reported a strong binding of proposed peptides with HLA-A*02 and HLA-B7 alleles. We used standard methods to construct vaccine model and this construct was evaluated further for its antigenicity, physicochemical properties, 2D and 3D structure prediction and validation. Further, molecular docking followed by molecular dynamics simulation was performed to evaluate the binding affinity and stability of TLR-4 and vaccine complex. Finally, the vaccine construct was reverse transcribed and adapted for E. coli strain K 12 prior to the insertion within the pET-28-a (+) vector for determining translational and microbial expression followed by conservancy analysis. Also, six multi-epitope subunit vaccines were constructed using different strategies containing immunogenic epitopes, appropriate adjuvants and linker sequences. We propose that our vaccine constructs can be used for downstream investigations using in-vitro and in-vivo studies to design effective and safe vaccine against different strains of COVID-19.