A reverse docking approach to explore the anticancer potency of natural compounds by interfering metastasis and angiogenesis.

Angiogenesis, which results in the formation of new blood and lymph vessels, is required to serve metastatic cancer progression. Cancer medications may target these two interconnected pathways. Phytocompounds have emerged as promising options for treating cancer. In this study, we used a reverse docking strategy to find new candidate molecules for cancer treatment that target both pathways. Following a literature study, the important cancer-causing proteins vascular endothelial growth factor D (VEGF-D) and basic fibroblast growth factor (bFGF) for angiogenesis and matrix metalloproteinase-2 (MMP-2) and matrix metalloproteinase-9 (MMP-9) for the metastatic pathway were targeted. Protein Data Bank was used to retrieve the structures of chosen proteins. 22 significant plant metabolites were identified as having anticancer activity. To determine the important protein binding residues, active site prediction was used. Using Lenvatinib and Withaferin A as reference ligands, the binding affinity of certain proteins for plant metabolites was determined by docking analysis. Homoharringtonine and viniferin, both have higher binding affinities when compared to reference ligands, with docking scores of -180.96 and -180.36 against the protein MMP-9, respectively. Moreover, Viniferin showed the highest binding affinity with both MMP-9 and MMP-2 proteins, which were then subjected to a 100-ns molecular dynamic simulation. where they were found to be significantly stable. In pharmacoinformatics investigations, the majority of our compounds were found to be non-toxic for the host. In this study, we suggested natural substances as cutting-edge anticancer treatments that target both angiogenesis and metastasis, which may aid in accelerating drug development and identifying viable therapeutic candidates.Communicated by Ramaswamy H. Sarma.

Immunoinformatics for Novel Multi-Epitope Vaccine Development in Canine Parvovirus Infections.

Canine parvovirus (CPV-2) is one of the most important pathogens of dogs of all ages, causing pandemic infections that are characterized by fatal hemorrhagic enteritis. The CPV-2 vaccine is recommended as a core vaccine for pet animals. Despite the intensive practice of active immunization, CPV-2 remains a global threat. In this study, a multi-epitope vaccine against CPV-2 was designed, targeting the highly conserved capsid protein (VP2) via in silico approaches. Several immunoinformatics methods, such as epitope screening, molecular docking, and simulation were used to design a potential vaccine construct. The partial protein sequences of the VP2 gene of CPV-2 and protein sequences retrieved from the NCBI were screened to predict highly antigenic proteins through antigenicity, trans-membrane-topology screening, an allergenicity assessment, and a toxicity analysis. Homologous VP2 protein sequences typically linked to the disease were identified using NCBI BLAST, in which four conserved regions were preferred. Overall, 10 epitopes, DPIGGKTGI, KEFDTDLKP, GTDPDDVQ, GGTNFGYIG, GTFYFDCKP, NRALGLPP, SGTPTN, LGLPPFLNSL, IGGKTG, and VPPVYPN, were selected from the conserved regions to design the vaccine construct. The molecular docking demonstrated the higher binding affinity of these epitopes with dog leukocyte antigen (DLA) molecules. The selected epitopes were linked with Salmonella enterica flagellin FliC adjuvants, along with the PADRE sequence, by GGS linkers to construct a vaccine candidate with 272 nucleotides. The codon adaptation and in silico cloning showed that the generated vaccine can be expressed by the E. coli strain, K12, and the sequence of the vaccine construct showed no similarities with dog protein. Our results suggest that the vaccine construct might be useful in preventing canine parvoviral enteritis (CPE) in dogs. Further in vitro and in vivo experiments are needed for the validation of the vaccine candidate.

A review on computational studies and bioinformatics analysis of potential drugs against monkeypox virus.

Monkeypox, a viral disease that is caused by monkeypox virus and occurs mainly in central and western Africa. However, recently it is spreading worldwide and took the focus of the scientific world towards it. Therefore, we made an attempt to cluster all the related information that may make it easy for the researchers to get the information easily and carry out their research smoothly to find prophylaxis against this emerging virus. There are very few researches found available on monkeypox. Almost all the studies were focused on smallpox virus and the recommended vaccines and therapeutics for monkeypox virus were originally developed for smallpox virus. Though these are recommended for emergency cases, they are not fully effective and specific against monkeypox. For this, here we also took the help of bioinformatics tools to screen potential drug candidates against this growing burden. Some potential antiviral plant metabolites, inhibitors and available drugs were scrutinized that can block the essential survival proteins of this virus. All the compounds Amentoflavone, Pseudohypericin, Adefovirdipiboxil, Fialuridin, Novobiocin and Ofloxacin showed elite binding efficiency with suitable ADME properties and Amentoflavone and Pseudohypericin showed stability in MD simulation study indicating their potency as probable drugs against this emerging virus.Communicated by Ramaswamy H. Sarma.

Subtractive proteomics analysis to uncover the potent drug targets for distinctive drug design of Candidaauris.

Candida auris is a serious health concern of the current world that possesses a serious global health threat and is emerging at a high rate. Available antifungal drugs are failing to combat this pathogen as they are growing resistant to those drugs and some strains have already shown resistance to all three available antifungal drugs in the market. Hence, finding alternative therapies is essential for saving lives from this enemy. To make the development of new treatments easier, we conducted some in silico study of this pathogen to discover possible targets for drug design and also recommended some possible metabolites to test in vivo circumstances. The complete proteome of the representative strain was retrieved, and the duplicate, non-essential, human homologous, non-metabolic, and druggable proteins were then eliminated. As a result, out of a total of 5441 C. auris proteins, we were able to isolate three proteins (XP 028890156.1, XP 028891672.1, and XP 028891858.1) that are crucial for the pathogen's survival as well as host-non-homolog, metabolic, and unrelated proteins to the human microbiome. Their subcellular locations and interactions with a large number of proteins (10 proteins) further point to them being good candidates for therapeutic targets. Following in silico docking of 29 putative antifungals of plant origin against the three proteins we chose, Caledonixanthone E, Viniferin, Glaucine, and Jatrorrhizine were discovered to be the most effective means of inhibiting those proteins since they displayed higher binding affinities (ranging from -28.97 kcal/mol to -51.99 kcal/mol) than the control fluconazole (which ranged between -28.84 kcal/mol and -41.15 kcal/mol). According to the results of MD simulations and MM-PBSA calculations, Viniferin and Caledonixanthone E are the most effective ligands for the proteins XP 028890156.1, XP 028891672.1, and XP 028891858.1. Furthermore, they were predicted to be safe and also showed proper ADME properties.

An In silico Approach towards Finding the Cancer-Causing Mutations in Human MET Gene.

Mesenchymal-epithelial transition (MET) factor is a proto-oncogene encoding tyrosine kinase receptor with hepatocyte growth factor (HGF) or scatter factor (SF). It is found on the human chromosome number 7 and regulates the diverse cellular mechanisms of the human body. The impact of mutations occurring in the MET gene is demonstrated by their detrimental effects on normal cellular functions. These mutations can change the structure and function of MET leading to different diseases such as lung cancer, neck cancer, colorectal cancer, and many other complex syndromes. Hence, the current study focused on finding deleterious non-synonymous single nucleotide polymorphisms (nsSNPs) and their subsequent impact on the protein's structure and functions, which may contribute to the emergence of cancers. These nsSNPs were first identified utilizing computational tools like SIFT, PROVEAN, PANTHER-PSEP, PolyPhen-2, I-Mutant 2.0, and MUpro. A total of 45359 SNPs of MET gene were accumulated from the database of dbSNP, and among them, 1306 SNPs were identified as non-synonymous or missense variants. Out of all 1306 nsSNPs, 18 were found to be the most deleterious. Moreover, these nsSNPs exhibited substantial effects on structure, binding affinity with ligand, phylogenetic conservation, secondary structure, and post-translational modification sites of MET, which were evaluated using MutPred2, RaptorX, ConSurf, PSIPRED, and MusiteDeep, respectively. Also, these deleterious nsSNPs were accompanied by changes in properties of MET like residue charge, size, and hydrophobicity. These findings along with the docking results are indicating the potency of the identified SNPs to alter the structure and function of the protein, which may lead to the development of cancers. Nonetheless, Genome-wide association study (GWAS) studies and experimental research are required to confirm the analysis of these nsSNPs.

Identification of conserved miRNAs and their targets in Jatropha curcas: an in silico approach.

BACKGROUND: MicroRNAs (miRNAs) are small endogenous RNAs with an approximate length of 18-22 nucleotides and involved in the regulation of gene expression in transcriptional or post-transcriptional levels. They were found to be associated with leaf morphogenesis, flowering time, vegetative phase change, and response to environmental cues in plants, where they act as a critical regulatory factor. The nature of high conservancy of plant miRNAs within the plant species made it possible to detect the conserved miRNAs by computational approaches. Expressed Sequence Tags (EST) based comparative genomic approaches provide advantages over wet lab approaches as it is convenient, easy to carry out and less time consuming. EST-based in silico approach can unravel new conserved miRNAs in plants, even when the complete genome sequence is not available. RESULTS: To identify the novel miRNAs, a total of 46,865 ESTs from Jatropha curcas were searched for homology to all available 6746 mature miRNAs of plant eudicotyledons. Finally, we ended up with 12 novel miRNAs in Jatropha that range from 18 to 19 nucleotides where their respective precursor miRNAs had 54.11-71.76% (A + U) content. The putative miRNAs belong to 12 individual miRNA family and most of them have higher (A + U) content ranging from 47.36 to 77.77% than their respective miRNA homologs. Many of the target genes by the newly identified miRNAs were associated with plant growth and development, stress response, defense and hormone signaling, and oil synthesis pathways. CONCLUSION: These findings have the potential to speed up miRNA identification and expand our understanding of miRNA functions in J. curcas.

Machine Learning-Based Respiration Rate and Blood Oxygen Saturation Estimation Using Photoplethysmogram Signals.

The continuous monitoring of respiratory rate (RR) and oxygen saturation (SpO2) is crucial for patients with cardiac, pulmonary, and surgical conditions. RR and SpO2 are used to assess the effectiveness of lung medications and ventilator support. In recent studies, the use of a photoplethysmogram (PPG) has been recommended for evaluating RR and SpO2. This research presents a novel method of estimating RR and SpO2 using machine learning models that incorporate PPG signal features. A number of established methods are used to extract meaningful features from PPG. A feature selection approach was used to reduce the computational complexity and the possibility of overfitting. There were 19 models trained for both RR and SpO2 separately, from which the most appropriate regression model was selected. The Gaussian process regression model outperformed all the other models for both RR and SpO2 estimation. The mean absolute error (MAE) for RR was 0.89, while the root-mean-squared error (RMSE) was 1.41. For SpO2, the model had an RMSE of 0.98 and an MAE of 0.57. The proposed system is a state-of-the-art approach for estimating RR and SpO2 reliably from PPG. If RR and SpO2 can be consistently and effectively derived from the PPG signal, patients can monitor their RR and SpO2 at a cheaper cost and with less hassle.

Lightweight End-to-End Deep Learning Solution for Estimating the Respiration Rate from Photoplethysmogram Signal.

Respiratory ailments are a very serious health issue and can be life-threatening, especially for patients with COVID. Respiration rate (RR) is a very important vital health indicator for patients. Any abnormality in this metric indicates a deterioration in health. Hence, continuous monitoring of RR can act as an early indicator. Despite that, RR monitoring equipment is generally provided only to intensive care unit (ICU) patients. Recent studies have established the feasibility of using photoplethysmogram (PPG) signals to estimate RR. This paper proposes a deep-learning-based end-to-end solution for estimating RR directly from the PPG signal. The system was evaluated on two popular public datasets: VORTAL and BIDMC. A lightweight model, ConvMixer, outperformed all of the other deep neural networks. The model provided a root mean squared error (RMSE), mean absolute error (MAE), and correlation coefficient (R) of 1.75 breaths per minute (bpm), 1.27 bpm, and 0.92, respectively, for VORTAL, while these metrics were 1.20 bpm, 0.77 bpm, and 0.92, respectively, for BIDMC. The authors also showed how fine-tuning a small subset could increase the performance of the model in the case of an out-of-distribution dataset. In the fine-tuning experiments, the models produced an average R of 0.81. Hence, this lightweight model can be deployed to mobile devices for real-time monitoring of patients.

A Review of Biomaterials and Associated Performance Metrics Analysis in Pre-Clinical Finite Element Model and in Implementation Stages for Total Hip Implant System.

Total hip replacement (THR) is a common orthopedic surgery technique that helps thousands of individuals to live normal lives each year. A hip replacement replaces the shattered cartilage and bone with an implant. Most hip implants fail after 10-15 years. The material selection for the total hip implant systems is a major research field since it affects the mechanical and clinical performance of it. Stress shielding due to excessive contact stress, implant dislocation due to a large deformation, aseptic implant loosening due to the particle propagation of wear debris, decreased bone remodeling density due to the stress shielding, and adverse tissue responses due to material wear debris all contribute to the failure of hip implants. Recent research shows that pre-clinical computational finite element analysis (FEA) can be used to estimate four mechanical performance parameters of hip implants which are connected with distinct biomaterials: von Mises stress and deformation, micromotion, wear estimates, and implant fatigue. In vitro, in vivo, and clinical stages are utilized to determine the hip implant biocompatibility and the unfavorable local tissue reactions to different biomaterials during the implementation phase. This research summarizes and analyses the performance of the different biomaterials that are employed in total hip implant systems in the pre-clinical stage using FEA, as well as their performances in in vitro, in vivo, and in clinical studies, which will help researchers in gaining a better understanding of the prospects and challenges in this field.

In-Silico Exploration of Plant Metabolites as Potential Remedies of Norovirus.

Research is still being carried out to develop specific medications or vaccinations to fight norovirus, a key contributor to foodborne illness. This study evaluated certain plant-based active chemicals as prospective candidates for such treatments using virtual screening techniques and other computer assessments. Twenty (20) plant metabolites were tested against the norovirus VP1, VP2, P48, and P22 protein domains using the molecular docking method. In terms of the lowest global binding energy, Asiatic acid, avicularin, guaijaverin, and curcumin exhibited the highest binding affinity with all selected proteins. Each viral protein's essential binding sites with the potential drugs and drug surface hotspots were uncovered. The ADMET (absorption, distribution, metabolism, excretion, and toxicity) analysis was used to further analyze the pharmacological profiles of the top candidates. According to the results, none of the substances showed any adverse consequences that would reduce their drug-like properties. According to the analysis of the toxicity pattern, no detectable tumorigenic, mutagenic, irritating, or reproductive effects of the compounds were discovered. However, among the top four alternatives, curcumin exhibited the highest levels of cytotoxicity and immunotoxicity. These discoveries may open the way for the development of effective norovirus therapies and safety measures. Due to the positive outcomes, we strongly propose more in vivo experiments for the experimental validation of our findings.

Scrutinizing Deleterious Nonsynonymous SNPs and Their Effect on Human POLD1 Gene.

POLD1 (DNA polymerase delta 1, catalytic subunit) is a protein-coding gene that encodes the large catalytic subunit (POLD1/p125) of the DNA polymerase delta (Polδ) complex. The consequence of missense or nonsynonymous SNPs (nsSNPs), which occur in the coding region of a specific gene, is the replacement of single amino acid. It may also change the structure, stability, and/or functions of the protein. Mutation in the POLD1 gene is associated with autosomal dominant predisposition to colonic adenomatous polyps, colon cancer, endometrial cancer (EDMC), breast cancer, and brain tumors. These de novo mutations in the POLD1 gene also result in autosomal dominant MDPL syndrome (mandibular hypoplasia, deafness, progeroid features, and lipodystrophy). In this study, genetic variations of POLD1 which may affect the structure and/or function were analyzed using different types of bioinformatics tools. A total of 17038 nsSNPs for POLD1 were collected from the NCBI database, among which 1317 were missense variants. Out of all missense nsSNPs, 28 were found to be deleterious functionally and structurally. Among these deleterious nsSNPs, 23 showed a conservation scale of >5, 2 were predicted to be associated with binding site formation, and one acted as a posttranslational modification site. All of them were involved in coil, extracellular structures, or helix formation, and some cause the change in size, charge, and hydrophobicity.

Inpatient COVID-19 Mortality Rates: What are the predictors?

ObjectiveThis study aims to investigate the relationship between registered nurses and hospital-based medical specialties staffing levels with inpatient COVID-19 mortality rates. MethodsWe rely on data from AHA Annual Survey Database, Area Health Resource File, and UnitedHealth Group Clinical Discovery Database. We use linear regression to analyze the association between hospital staffing levels and bed capacity with inpatient COVID-19 mortality rates from March 1, 2020, through December 31, 2020. ResultsHigher staffing levels of registered nurses, hospitalists, and emergency medicine physicians were associated with lower COVID-19 mortality rates. Moreover, a higher number of ICU and skilled nursing beds were associated with better patient outcomes. Hospitals located in urban counties with high infection rates had the worst patient mortality rates. ConclusionHigher staffing levels are associated with lower inpatient mortality rates for COVID-19 patients. A future assessment is needed to establish benchmarks on the minimum staffing levels for nursing and hospital-based medical specialties during pandemics.

Genomic diversity and molecular dynamics interaction on mutational variances among RB domains of SARS-CoV-2 interplay drug inactivation.

The scientific community has been releasing whole genomic sequences of SARS-CoV-2 to facilitate the investigation of molecular features and evolutionary history. We retrieved 36 genomes of 18 prevalent countries of Asia, Europe and America for genomic diversity and mutational analysis. Besides, we studied mutations in the RBD regions of Spike (S) proteins to analyze the drug efficiency against these mutations. In this research, phylogenenetic analysis, evolutionary modeling, substitution pattern analysis, molecular docking, dynamics simulation, etc. were performed. The genomic sequences showed >99% similarity with the reference sequence of China.TN93 + G was predicted as a best nucleotide substitution model. It was revealed that effective transition from the co-existing SARS genome to the SARS-CoV-2 and a noticeable positive selection in the SARS-CoV-2 genomes occurred. Moreover, three mutations in RBD domain, Val/ Phe367, Val/ Leu 382 and Ala/ Val522, were discovered in the genomes from Netherland, Bangladesh and the USA, respectively. Molecular docking and dynamics study showed RBD with mutation Val/Leu382 had the lowest binding affinity with remdesivir. In conclusion, the SARS-CoV-2 genomes are similar, but multiple degrees of transitions and transversions occurred. The mutations cause a significant conformational change, which are needed to be investigated during drug and vaccine development.

The scenario of knowledge, attitude and practice of the Bangladeshi population towards thalassemia prevention: A nationwide study.

Thalassemia is one of the most common life-threatening yet preventable congenital hemoglobin disorders especially in South Asian regions like Bangladesh. It has become a rising public health concern for Bangladesh as 6-12% of the population are carriers and many of them are unaware of it. The purpose of the study is to inspect the knowledge and attitude towards thalassemia among the general people of Bangladesh. A cross-sectional survey was conducted in eight administrative regions of Bangladesh between January and October of 2020. A structured questionnaire was designed to collect information about thalassemia and socio-demographic characteristics. Multivariate logistic regression models were used to identify factors associated with knowledge of thalassemia. A p-value <0.05 was considered significant. Of the 1,248 participants, only 47.4% had heard of thalassemia. Half of the participants who heard about the disease had no idea that thalassemia was not a transfusion transmitted disease. Only 49.8% of participant correctly identified consanguineous marriages as an important risk factor. Majority of them knew that marriage between two carriers can lead to a child with thalassemia major. About 72.5% knew that blood tests are a diagnosis method to determine thalassemia. Among the socio-demographic variables, the level of education of the respondents was identified as an independent predictor for knowledge (p<0.05) on thalassemia. For example, graduate (aOR: 24.88; 95% CI: 6.238-99.232) or post-graduate (aOR: 33.18; 95% CI: 7.864-140.001) participants were more aware of thalassemia than non-graduates. However, about 68.2% of the participants showed a positive attitude towards premarital screening of themselves or their family members and 85.3% were willing to donate blood to thalassemia patients. The study shows that there is a need to disseminate the information on thalassemia since the knowledge gap is huge among people. These findings will strengthen the implementation of thalassemia major awareness through educational programs, health counseling, premarital screening and campaigning.

dPQL: a lossless distributed algorithm for generalized linear mixed model with application to privacy-preserving hospital profiling

Hospital profiling provides a quantitative comparison of health care providers for their quality of care regarding certain clinical outcomes. To implement hospital profiling, the generalized linear mixed model (GLMM) is usually used to fit clinical or administrative claims data, adjusting for the effects of covariates. For better generalizability, data across multiple hospitals, databases or networks are desired. However, due to the privacy regulation and the computation complexity of GLMM, a convenient distributed algorithm for hospital profiling is needed. In this paper, we develop a novel distributed Penalized Quasi Likelihood algorithm (dPQL) to fit GLMM, when only aggregated data, rather than the individual patient data are available across hospitals. The dPQL algorithm is based on a newly-developed distributed linear mixed model (DLMM) algorithm. This proposed dPQL algorithm is lossless, i.e. it obtains identical results as if the individual patient data are pooled from all hospitals. We demonstrate the usage of the dPQL algorithms by ranking 929 hospitals for COVID-19 mortality or referral to hospice in Asch, et al. 2020.

An efficient distributed algorithm with application to COVID-19 data from heterogeneous clinical sites

ObjectivesIntegrating electronic health records (EHR) data from several clinical sites offers great opportunities to improve estimation with a more general population compared to analyses based on a single clinical site. However, sharing patient-level data across sites is practically challenging due to concerns about maintaining patient privacy. The objective of this study is to develop a novel distributed algorithm to integrate heterogeneous EHR data from multiple clinical sites without sharing patient-level data. Materials and MethodsThe proposed distributed algorithm for binary regression can effectively account for between-site heterogeneity and is communication-efficient. Our method is built on a pairwise likelihood function in the extended Mantel-Haenszel regression, which is known to be statistically highly efficient. We construct a surrogate pairwise likelihood function through approximating the target pairwise likelihood by its surrogate. We show that the proposed surrogate pairwise likelihood leads to a consistent and asymptotically normal estimator by effective communication without sharing individual patient-level data. We study the empirical performance of the proposed method through a systematic simulation study and an application with data of 14,215 COVID-19 patients from 230 clinical sites at UnitedHealth Group Clinical Research Database. ResultsThe proposed method was shown to perform close to the gold standard approach under extensive simulation settings. When the event rate is <5%, the relative bias of the proposed estimator is 30% smaller than that of the meta-analysis estimator. The proposed method retained high accuracy across different sample sizes and event rates compared with meta-analysis. In the data evaluation, the proposed estimate has a relative bias <9% when the event rate is <1%, whereas the meta-analysis estimate has a relative bias at least 10% higher than that of the proposed method. ConclusionsOur simulation study and data application demonstrate that the proposed distributed algorithm provides an estimator that is robust to heterogeneity in event rates when effectively integrating data from multiple clinical sites. Our algorithm is therefore an effective alternative to both meta-analysis and existing distributed algorithms for modeling heterogeneous multi-site binary outcomes.

Estimating Blood Pressure from the Photoplethysmogram Signal and Demographic Features Using Machine Learning Techniques.

Hypertension is a potentially unsafe health ailment, which can be indicated directly from the blood pressure (BP). Hypertension always leads to other health complications. Continuous monitoring of BP is very important; however, cuff-based BP measurements are discrete and uncomfortable to the user. To address this need, a cuff-less, continuous, and noninvasive BP measurement system is proposed using the photoplethysmograph (PPG) signal and demographic features using machine learning (ML) algorithms. PPG signals were acquired from 219 subjects, which undergo preprocessing and feature extraction steps. Time, frequency, and time-frequency domain features were extracted from the PPG and their derivative signals. Feature selection techniques were used to reduce the computational complexity and to decrease the chance of over-fitting the ML algorithms. The features were then used to train and evaluate ML algorithms. The best regression models were selected for systolic BP (SBP) and diastolic BP (DBP) estimation individually. Gaussian process regression (GPR) along with the ReliefF feature selection algorithm outperforms other algorithms in estimating SBP and DBP with a root mean square error (RMSE) of 6.74 and 3.59, respectively. This ML model can be implemented in hardware systems to continuously monitor BP and avoid any critical health conditions due to sudden changes.

Immunoinformatics approaches for designing a novel multi epitope peptide vaccine against human norovirus (Norwalk virus).

Norovirus is known as a major cause of several acute gastroenteritis (AGE) outbreaks each year. A study was conducted to develop a unique multi epitope subunit vaccine against human norovirus by adopting reverse vaccinology approach. The entire viral proteome of Norwalk virus was retrieved and allowed for further in silico study to predict highly antigenic epitopes through antigenicity, transmembrane topology screening, allergenicity assessment, toxicity analysis, population coverage analysis and molecular docking approach. Capsid protein VP1 and protein VP2 were identified as most antigenic viral proteins which generated a plethora of antigenic epitopes. Physicochemical properties and secondary structure of the designed vaccine were assessed to ensure its thermostability, hydrophilicity, theoretical PI and structural behavior. Molecular docking analysis of the refined vaccine with different MHCs and human immune TLR8 receptor demonstrated higher binding interaction as well. Complexed structure of the modeled vaccine and TLR8 showed minimal deformability at molecular level. The designed construct was reverse transcribed and adapted for E. coli strain K12 prior to insertion within pET28a(+) vector for its heterologous cloning and expression, and sequence of vaccine constructs showed no similarity with human proteins. However, the study could initiate in vitro and in vivo studies regarding effective vaccine development against human norovirus.