Predictions for COVID-19 with deep learning models of LSTM, GRU and Bi-LSTM.

COVID-19, responsible of infecting billions of people and economy across the globe, requires detailed study of the trend it follows to develop adequate short-term prediction models for forecasting the number of future cases. In this perspective, it is possible to develop strategic planning in the public health system to avoid deaths as well as managing patients. In this paper, proposed forecast models comprising autoregressive integrated moving average (ARIMA), support vector regression (SVR), long shot term memory (LSTM), bidirectional long short term memory (Bi-LSTM) are assessed for time series prediction of confirmed cases, deaths and recoveries in ten major countries affected due to COVID-19. The performance of models is measured by mean absolute error, root mean square error and r2_score indices. In the majority of cases, Bi-LSTM model outperforms in terms of endorsed indices. Models ranking from good performance to the lowest in entire scenarios is Bi-LSTM, LSTM, GRU, SVR and ARIMA. Bi-LSTM generates lowest MAE and RMSE values of 0.0070 and 0.0077, respectively, for deaths in China. The best r2_score value is 0.9997 for recovered cases in China. On the basis of demonstrated robustness and enhanced prediction accuracy, Bi-LSTM can be exploited for pandemic prediction for better planning and management.

Utilizing Siamese 4D-AlzNet and Transfer Learning to Identify Stages of Alzheimer's Disease.

Alzheimer's disease (AD) is the general form of dementia, leading to a progressive neurological disorder characterized by memory loss due to brain cell damage. Artificial Intelligence (AI) assists in the early identification and prediction of AD patients, determining future risks and benefits for radiologists and doctors to save time and cost. Since deep learning (DL) approaches work well with massive datasets and have recently become helpful for AD detection, there remains an area for improvement in automating detection performance. Present approaches somehow addressed the challenges of limited annotated data samples for binary classification. This contrasts with prior state-of-the-art techniques, which were constrained by their incapacity to capture abstract-level information. In this paper, we proposed a Siamese 4D-AlzNet model comprised of four parallel convolutional neural network (CNN) streams (Five CNN layer blocks) and customized transfer learning models (Frozen VGG-19, Frozen VGG-16, and customized AlexNet). Siamese 4D-AlzNet was vertically and horizontally stored, and the spatial features were passed to the final layer for classification. For experiments, T1-weighted MRI images comprised of four distinct subject classes, normal control (NC), mild cognitive impairment (MCI), late mild cognitive impairment (LMCI), and AD, have been employed. Our proposed models achieved outstanding accuracy, with a remarkable 95.05% accuracy distinguishing between normal and AD subjects. The performance across remaining binary class pairs consistently exceeded 90%. We thoroughly compared our model with the latest methods using the same dataset as our reference. Our proposed model improved NC-AD and MCI-AD classification accuracy by 2% 7%.

3-Way hybrid analysis using clinical and magnetic resonance imaging for early diagnosis of Alzheimer's disease.

Alzheimer's is a progressive neurodegenerative disorder that leads to cognitive impairment and ultimately death. To select the most effective treatment options, it is crucial to diagnose and classify the disease early, as current treatments can only delay its progression. However, previous research on Alzheimer's disease (AD) has had limitations, such as inaccuracies and reliance on a small, unbalanced binary dataset. In this study, we aimed to evaluate the early stages of AD using three multiclass datasets: OASIS, EEG, and ADNI MRI. The research consisted of three phases: pre-processing, feature extraction, and classification using hybrid learning techniques. For the OASIS and ADNI MRI datasets, we computed the mean RGB value and used an averaging filter to enhance the images. We balanced and augmented the dataset to increase its size. In the case of the EEG dataset, we applied a band-pass filter for digital filtering to reduce noise and also balanced the dataset using random oversampling. To extract and classify features, we utilized a hybrid technique consisting of four algorithms: AlexNet-MLP, AlexNet-ETC, AlexNet-AdaBoost, and AlexNet-NB. The results showed that the AlexNet-ETC hybrid algorithm achieved the highest accuracy rate of 95.32% for the OASIS dataset. In the case of the EEG dataset, the AlexNet-MLP hybrid algorithm outperformed other approaches with the highest accuracy of 97.71%. For the ADNI MRI dataset, the AlexNet-MLP hybrid algorithm achieved an accuracy rate of 92.59%. Comparing these results with the current state of the art demonstrates the effectiveness of our findings.

Short-term solar energy forecasting: Integrated computational intelligence of LSTMs and GRU.

Problems with erroneous forecasts of electricity production from solar farms create serious operational, technological, and financial challenges to both Solar farm owners and electricity companies. Accurate prediction results are necessary for efficient spinning reserve planning as well as regulating inertia and power supply during contingency events. In this work, the impact of several climatic conditions on solar electricity generation in Amherst. Furthermore, three machine learning models using Lasso Regression, ridge Regression, ElasticNet regression, and Support Vector Regression, as well as deep learning models for time series analysis include long short-term memory, bidirectional LSTM, and gated recurrent unit along with their variants for estimating solar energy generation for every five-minute interval on Amherst weather power station. These models were evaluated using mean absolute error root means square error, mean square error, and mean absolute percentage error. It was observed that horizontal solar irradiance and water saturation deficiency had a highly proportional relationship with Solar PV electricity generation. All proposed machine learning models turned out to perform well in predicting electricity generation from the analyzed solar farm. Bi-LSTM has performed the best among all models with 0.0135, 0.0315, 0.0012, and 0.1205 values of MAE, RMSE, MSE, and MAPE, respectively. Comparison with the existing methods endorses the use of our proposed RNN variants for higher efficiency, accuracy, and robustness. Multistep-ahead solar energy prediction is also carried out by exploiting hybrids of LSTM, Bi-LSTM, and GRU.

Dental image enhancement network for early diagnosis of oral dental disease.

Intelligent robotics and expert system applications in dentistry suffer from identification and detection problems due to the non-uniform brightness and low contrast in the captured images. Moreover, during the diagnostic process, exposure of sensitive facial parts to ionizing radiations (e.g., X-Rays) has several disadvantages and provides a limited angle for the view of vision. Capturing high-quality medical images with advanced digital devices is challenging, and processing these images distorts the contrast and visual quality. It curtails the performance of potential intelligent and expert systems and disincentives the early diagnosis of oral and dental diseases. The traditional enhancement methods are designed for specific conditions, and network-based methods rely on large-scale datasets with limited adaptability towards varying conditions. This paper proposed a novel and adaptive dental image enhancement strategy based on a small dataset and proposed a paired branch Denticle-Edification network (Ded-Net). The input dental images are decomposed into reflection and illumination in a multilayer Denticle network (De-Net). The subsequent enhancement operations are performed to remove the hidden degradation of reflection and illumination. The adaptive illumination consistency is maintained through the Edification network (Ed-Net). The network is regularized following the decomposition congruity of the input data and provides user-specific freedom of adaptability towards desired contrast levels. The experimental results demonstrate that the proposed method improves visibility and contrast and preserves the edges and boundaries of the low-contrast input images. It proves that the proposed method is suitable for intelligent and expert system applications for future dental imaging.

An integrative reverse vaccinology, immunoinformatic, docking and simulation approaches towards designing of multi-epitopes based vaccine against monkeypox virus.

Monkeypox is a viral zoonotic disease that is caused by the monkeypox virus (MPXV) and is mainly transmitted to human through close contact with an infected person, animal, or fomites which is contaminated by the virus. In the present research work, reverse vaccinology and several other bioinformatics and immunoinformatics tools were utilized to design multi-epitopes-based vaccine against MPXV by exploring three probable antigenic extracellular proteins: cupin domain-containing protein, ABC transporter ATP-binding protein and DUF192 domain-containing protein. Both cellular and humoral immunity induction were the main concerning qualities of the vaccine construct, hence from selected proteins both B and T-cells epitopes were predicted. Antigenicity, allergenicity, toxicity, and water solubility of the predicted epitopes were assessed and only probable antigenic, non-allergic, non-toxic and good water-soluble epitopes were used in the multi-epitopes vaccine construct. The developed vaccine was found to be potentially effective against MPXV and to be highly immunogenic, cytokine-producing, antigenic, non-toxic, non-allergenic, and stable. Additionally, to increase stability and expression efficiency in the host E. coli, disulfide engineering, codon adaptation, and in silico cloning were employed. Molecular docking and other biophysical approaches were utilized to evaluate the binding mode and dynamic behavior of the vaccine construct with TLR-2, TLR-4, and TLR-8. The outcomes of the immune simulation demonstrated that both B and T cells responded more strongly to the vaccination component. The detailed in silico analysis concludes that the proposed vaccine will induce a strong immune response against MPXV infection, making it a promising target for additional experimental trials.Communicated by Ramaswamy H. Sarma.

A transfer learning approach for multiclass classification of Alzheimer's disease using MRI images.

Alzheimer's is an acute degenerative disease affecting the elderly population all over the world. The detection of disease at an early stage in the absence of a large-scale annotated dataset is crucial to the clinical treatment for the prevention and early detection of Alzheimer's disease (AD). In this study, we propose a transfer learning base approach to classify various stages of AD. The proposed model can distinguish between normal control (NC), early mild cognitive impairment (EMCI), late mild cognitive impairment (LMCI), and AD. In this regard, we apply tissue segmentation to extract the gray matter from the MRI scans obtained from the Alzheimer's Disease National Initiative (ADNI) database. We utilize this gray matter to tune the pre-trained VGG architecture while freezing the features of the ImageNet database. It is achieved through the addition of a layer with step-wise freezing of the existing blocks in the network. It not only assists transfer learning but also contributes to learning new features efficiently. Extensive experiments are conducted and results demonstrate the superiority of the proposed approach.

The Screening of Phytochemicals Against NS5 Polymerase to Treat Zika Virus Infection: Integrated Computational Based Approach.

BACKGROUND: The recent Zika Virus (ZIKV) outbreak provides a spur for new, efficient, and safe anti-Zika Virus agents. RNA-dependent RNA polymerase (RdRp) is critical amongst the seven non-structural proteins for viral replication and considered an attractive drug target. METHODS: In this study, molecular docking approach was used to rationally screen the library of 5000 phytochemicals to find inhibitors against NS5 RdRp. LigX tool was used to analyze the 2D plots of receptor-ligand interactions. The top-ranked compounds were then subjected to in-silico pharmacokinetic study. RESULTS: The compounds namely Polydatin, Dihydrogenistin, Liquiritin, Rhapontin and Cichoriin were successfully bound inside the pocket of NS5 RdRp. Polydatin was the leading phytochemical that showed high docking score -18.71 (kcal/mol) and bonding interaction at the active-site of NS5 RdRp. They were subjected to analyze drug-like properties that further reinforced their validation and showed that they have more capability to attach with the receptor as compared to SOFOSBUVIR control drug. MD simulation of the top two complexes was performed and the simulated complexes showed stability and ligands were kept within the bonding pocket. CONCLUSION: The study might facilitate the development of a natural and cost-effective drug against ZIKV. Further validation, however, is necessary to confirm its effectiveness and its biocompatibility.

Proteome based mapping and molecular docking revealed DnaA as a potential drug target against Shigella sonnei.

Shigella sonnei is one of the major causes of diarrhea and remained a critical microbe responsible for higher morbidity and mortality rates resulting from dysentery every year across the world. Antibiotic therapy of Shigella diseases plays a critical role in decreasing the prevalence as well as the fatality rate of this infection. However, the management of these diseases remains challenging, owing to the overall increase in resistance against many antimicrobials. The situation necessitates the rapid development of effective and feasible S. sonnei treatments. In the present study, the subtractive genomics approach was utilized to find the potential drug targets for S. sonnei strain Ss046. Various tools of bioinformatics were implemented to remove the human-specific homologous and pathogen-specific paralogous sequences from the bacterial proteome. Then, metabolic pathway and subcellular location analysis were performed of essential bacterial proteins to describe their role in various cellular processes. Only one essential protein i-e Chromosomal replication initiator protein DnaA was found in the proteome of the pathogen that could be used as a potent target for designing new drugs. 3D structure prediction of DnaA protein was carried out using Phyre 2. Molecular docking of 5000 phytochemicals was performed against DnaA to identify four top-ranked phytochemicals (Riccionidin A, Dothistromin, Fustin, and Morin) based on scoring functions and interaction with the active site. This study suggests that these phytochemicals could be used as antibacterial drugs to treat S. sonnei infections in the future. To confirm their efficacy and evaluate their drug potency, further in vitro analyses are required.

Designing of a multi-epitopes-based peptide vaccine against rift valley fever virus and its validation through integrated computational approaches.

Since its discovery, the Rift Valley Fever virus (RVFV) has been the source of numerous outbreaks in the Arab Peninsulas and Africa, wreaking havoc on humans and animals. The lack of therapeutics or licensed human vaccines limits the options for controlling RVFV outbreaks. Therefore, RVFV has been prioritized for rapid research and innovation of prevention strategies to control and prevent its outbreaks. The purpose of this study was to design a multi-epitope-based peptide vaccine (MEBPV) against RVFV. Bioinformatics approaches were used to design a potent MEBPV that can potentially activate both CD8+ and CD4+ T-cell immune responses, and several computational tools were employed to investigate its biological activities. Three antigenic proteins (Nucleocapsid (N), Glycoprotein C (GC), and Glycoprotein N (GN)) from the RVFV were chosen and potential immunogenic T- and B -cell epitopes were predicted from them. Based on in silico analysis, a MEBPV based on highly scored T and B-cell epitopes (6 CTL, 5 HTL, and 4 LBL) combined with linkers and adjuvants was developed. The finest predicted model was used for docking studies with Toll-like receptors (TLR3 and TLR8) and MHC molecules (MHC I and MHC II) after predicting and analyzing the tertiary structure of MEBPV. The designed MEBPV was then tested for stability with TLR3 and TLR8 receptors using molecular dynamics (MD) simulation and MMGBSA analysis. The MEBPV -TLR3, MEBPV -TLR8, MEBPV-MHC I and MEBPV -MHC II docked models were found stable during simulation time in MD and MMGBSA studies. In silico analysis revealed that the constructed vaccine could elicit both cell-mediated and humoral immune responses simultaneously. The proposed MEBPV could be a strong candidate against RVFV, but it will need to be tested in the laboratory to guarantee its safety and immunogenicity.

Proteome based mapping and reverse vaccinology techniques to contrive multi-epitope based subunit vaccine (MEBSV) against Streptococcus pyogenes.

Streptococcus pyogenes is a root cause of human infection like pharyngitis, tonsillitis, scarlet fever, impetigo, and respiratory tract infections. About 11 million individuals in the US suffer from pharyngitis every year. Unfortunately, no vaccine against S. pyogenes is available yet. The purpose of this study is to create a multiepitope-based subunit vaccine (MEBSV) targeting S. pyogenes top four highly antigenic proteins by using a combination of immunological techniques and molecular docking to tackle term group A streptococcal (GAS) infections. T-cell (HTL & CTL), B-cell, and IFN-γ of target proteins were forecasted and epitopes having high antigenic properties being selected for subsequent research. For designing of final vaccine, 5LBL, 9CTL, and 4HTL epitopes were joined by the KK, AAY, and GPGPG linkers. To enhance the immune response, the N-end of the vaccine was linked by adjuvant (Cholera enterotoxin subunit B) with a linker named EAAAK. With the addition of adjuvants and linkers, the construct size was 421 amino acids. IFN-γ and B-cell epitopes illustrate that the modeled construct is optimized for cell-mediated immune or humoral responses. The developed MEBSV structure was assessed to be highly antigenic, non-toxic, and non-allergenic. Moreover, disulphide engineering further enhanced the stability of the final vaccine protein. Molecular docking of the MEBSV with toll-like receptor 4 (TLR4) was conducted to check the vaccine's compatibility with the receptor. Besides, in-silico cloning has been carried out for credibility validation and proper expression of vaccine construct. These findings suggested that the multi-epitope vaccine produced might be a potential immunogenic against Group A streptococcus infections but further experimental testing is required to validate this study.

Structural Elucidation of Rift Valley Fever Virus L Protein towards the Discovery of Its Potential Inhibitors.

Rift valley fever virus (RVFV) is the causative agent of a viral zoonosis that causes a significant clinical burden in domestic and wild ruminants. Major outbreaks of the virus occur in livestock, and contaminated animal products or arthropod vectors can transmit the virus to humans. The viral RNA-dependent RNA polymerase (RdRp; L protein) of the RVFV is responsible for viral replication and is thus an appealing drug target because no effective and specific vaccine against this virus is available. The current study reported the structural elucidation of the RVFV-L protein by in-depth homology modeling since no crystal structure is available yet. The inhibitory binding modes of known potent L protein inhibitors were analyzed. Based on the results, further molecular docking-based virtual screening of Selleckchem Nucleoside Analogue Library (156 compounds) was performed to find potential new inhibitors against the RVFV L protein. ADME (Absorption, Distribution, Metabolism, and Excretion) and toxicity analysis of these compounds was also performed. Besides, the binding mechanism and stability of identified compounds were confirmed by a 50 ns molecular dynamic (MD) simulation followed by MM/PBSA binding free energy calculations. Homology modeling determined a stable multi-domain structure of L protein. An analysis of known L protein inhibitors, including Monensin, Mycophenolic acid, and Ribavirin, provide insights into the binding mechanism and reveals key residues of the L protein binding pocket. The screening results revealed that the top three compounds, A-317491, Khasianine, and VER155008, exhibited a high affinity at the L protein binding pocket. ADME analysis revealed good pharmacodynamics and pharmacokinetic profiles of these compounds. Furthermore, MD simulation and binding free energy analysis endorsed the binding stability of potential compounds with L protein. In a nutshell, the present study determined potential compounds that may aid in the rational design of novel inhibitors of the RVFV L protein as anti-RVFV drugs.

Discovery of Rift Valley fever virus natural pan-inhibitors by targeting its multiple key proteins through computational approaches.

The Rift Valley fever virus (RVFV) is a zoonotic arbovirus and pathogenic to both humans and animals. Currently, no proven effective RVFV drugs or licensed vaccine are available for human or animal use. Hence, there is an urgent need to develop effective treatment options to control this viral infection. RVFV glycoprotein N (GN), glycoprotein C (GC), and nucleocapsid (N) proteins are attractive antiviral drug targets due to their critical roles in RVFV replication. In present study, an integrated docking-based virtual screening of more than 6000 phytochemicals with known antiviral activities against these conserved RVFV proteins was conducted. The top five hit compounds, calyxin C, calyxin D, calyxin J, gericudranins A, and blepharocalyxin C displayed optimal binding against all three target proteins. Moreover, multiple parameters from the molecular dynamics (MD) simulations and MM/GBSA analysis confirmed the stability of protein-ligand complexes and revealed that these compounds may act as potential pan-inhibitors of RVFV replication. Our computational analyses may contribute toward the development of promising effective drugs against RVFV infection.

Rational design of multimeric based subunit vaccine against Mycoplasma pneumonia: Subtractive proteomics with immunoinformatics framework.

Mycoplasma pneumoniae is the prevalent cause of acquired respiratory infections around the globe. A multi-epitope vaccine (MEV) must be developed to combat infections of M. pneumoniae because there is no specific disease-modifying treatment or vaccination is present. The objective of this research is to design a vaccine that targets M. pneumoniae top five highly antigenic proteins using a combination of immunological techniques and molecular docking. T-cell (HTL & CTL), B-cell, and IFN-γ of target proteins were forecasted and highly conservative epitopes were chosen for further study. For designing of final vaccine, 4LBL, 7CTL, and 5HTL epitopes were joined by linkers of KK, AAY, and GPGPG. The N-end of the vaccine was linked to an adjuvant (Cholera enterotoxin subunit B) with a linker named EAAAK to enhance immunogenicity. After the addition of adjuvants and linkers, the size of the construct was 395 amino acids. The epitopes of IFN-γ and B-cells illustrate that the model construct is optimized for cell-mediated immune or humoral responses. To ensure that the final design is safer and immunogenic, properties like non-allergens, antigenicity, and various physicochemical properties were evaluated. Molecular docking of the vaccine with the toll-like receptor 4 (TLR4) was conducted to check the compatibility of the vaccine with the receptor. Besides, in-silico cloning was utilized for validation of the credibility and proper expression of the vaccine. Furthermore, to confirm that the multi-epitope vaccine created is protective and immunogenic, this research requires experimental validation.

Implementation of Vaccinomics and In-Silico Approaches to Construct Multimeric Based Vaccine Against Ovarian Cancer.

One of the most common gynecologic cancers is ovarian cancer and ranked third after the other two most common cancers: cervical and uterine. The highest mortality rate has been observed in the case of ovarian cancer. To treat ovarian cancer, an immune-informatics approach was used to design a multi-epitope vaccine (MEV) structure. Epitopes prediction of the cancer testis antigens (NY-ESO-1), A-Kinase anchor protein (AKAP4), Acrosin binding protein (ACRBP), Piwi-like protein (PIWIL3), and cancer testis antigen 2 (LAGE-1) was done. Non-toxic, highly antigenic, non-allergenic, and overlapping epitopes were shortlisted for vaccine construction. Chosen T-cell epitopes displayed a robust binding attraction with their corresponding Human Leukocyte Antigen (HLA) alleles demonstrated 97.59% of population coverage. The vaccine peptide was established by uniting three key constituents, comprising the 14 epitopes of CD8 + cytotoxic T lymphocytes (CTLs), 5 helper epitopes, and the adjuvant. For the generation of the effective response of CD4 + cells towards the T-helper cells, granulocyte-macrophage-colony-stimulating factor (GM-CSF) was applied. With the addition of adjuvants and linkers, the construct size was 547 amino acids. The developed MEV structure was predicted to be antigenic, non-toxic, non-allergenic, and firm in nature. I-tasser anticipated the 3D construction of MEV. Moreover, disulfide engineering further enhanced the stability of the final vaccine protein. In-silico cloning and vaccine codon optimization were done to analyze the up-regulation of its expression. The outcomes established the vaccine's immunogenicity and safety profile, besides its aptitude to encourage both humoral and cellular immune responses. The offered vaccine, grounded on our in-silico investigation, may be considered for ovarian cancer immunotherapy. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s10989-021-10294-w.

Pan-vaccinomics approach towards a universal vaccine candidate against WHO priority pathogens to address growing global antibiotic resistance.

Antimicrobial resistance (AMR) in bacterial pathogens is a major global distress. Due to the slow progress of antibiotics development and the fast pace of resistance acquisition, there is an urgent need for effective vaccines against such bacterial pathogens. In-silico approaches including pan-genomics, subtractive proteomics, reverse vaccinology, immunoinformatics, molecular docking, and dynamics simulation studies were applied in the current study to identify a universal potential vaccine candidate against the 18 multi-drug resistance (MDRs) bacterial pathogenic species from a WHO priority list. Ten non-redundant, non-homologous, virulent, and antigenic vaccine candidates were filtered against all targeted species. Nine B-cell-derived T-cell antigen epitopes which show a great affinity to the dominant HLA allele (DRB1*0101) in the human population were screened from selected vaccine candidates using immunoinformatics approaches. Screened epitopes were then used to design a multi-epitope peptide vaccine construct (MEPVC) along with ß-defensin adjuvant to improve the immunogenic properties of the proposed vaccine construct. Molecular docking and MD simulation were carried out to study the binding affinity and molecular interaction of MEPVC with human immune receptors (TLR2, TLR3, TLR4, and TLR6). The final MEPVC construct was reverse translated and in-silico cloned in the pET28a(+) vector to ensure its effectiveness. This in silico construct is expected to be helpful for vaccinologists to assess its immune protection effectiveness in vivo and in vitro to counter rising antibiotic resistance worldwide.

Development of a Candidate Multi-Epitope Subunit Vaccine against Klebsiella aerogenes: Subtractive Proteomics and Immuno-Informatics Approach.

Klebsiella aerogenes is a Gram-negative bacterium which has gained considerable importance in recent years. It is involved in 10% of nosocomial and community-acquired urinary tract infections and 12% of hospital-acquired pneumonia. This organism has an intrinsic ability to produce inducible chromosomal AmpC beta-lactamases, which confer high resistance. The drug resistance in K. aerogenes has been reported in China, Israel, Poland, Italy and the United States, with a high mortality rate (~50%). This study aims to combine immunological approaches with molecular docking approaches for three highly antigenic proteins to design vaccines against K. aerogenes. The synthesis of the B-cell, T-cell (CTL and HTL) and IFN-γ epitopes of the targeted proteins was performed and most conserved epitopes were chosen for future research studies. The vaccine was predicted by connecting the respective epitopes, i.e., B cells, CTL and HTL with KK, AAY and GPGPG linkers and all these were connected with N-terminal adjuvants with EAAAK linker. The humoral response of the constructed vaccine was measured through IFN-γ and B-cell epitopes. Before being used as vaccine candidate, all identified B-cell, HTL and CTL epitopes were tested for antigenicity, allergenicity and toxicity to check the safety profiles of our vaccine. To find out the compatibility of constructed vaccine with receptors, MHC-I, followed by MHC-II and TLR4 receptors, was docked with the vaccine. Lastly, in order to precisely certify the proper expression and integrity of our construct, in silico cloning was carried out. Further studies are needed to confirm the safety features and immunogenicity of the vaccine.

Rational design of chimeric Multiepitope Based Vaccine (MEBV) against human T-cell lymphotropic virus type 1: An integrated vaccine informatics and molecular docking based approach.

Human T-cell lymphotropic virus type 1 (HTLV-1) is an infectious virus that has been linked to adult T cell leukemia /lymphoma, aggressive CD4-T cell malignancy and many other immune-related medical illnesses. So far, no effective vaccine is known to combat HTLV-1, hence, the current research work was performed to design a potential multi-epitope-based subunit vaccine (MEBV) by adopting the latest methodology of reverse vaccinology. Briefly, three highly antigenic proteins (Glycoprotein, Accessory protein, and Tax protein) with no or minimal (<37%) similarity with human proteome were sorted out and potential B- and T-cell epitopes were forecasted from them. Highly antigenic, immunogenic, non-toxic, non-allergenic and overlapping epitopes were short-listed for vaccine development. The chosen T-cell epitopes displayed a strong binding affinity with their corresponding Human Leukocyte Antigen alleles and demonstrated 95.8% coverage of the world's population. Finally, nine Cytotoxic T Lymphocytes, six Helper T Lymphocytes and five Linear B Lymphocytes epitopes, joint through linkers and adjuvant, were exploited to design the final MEBV construct, comprising of 382 amino acids. The developed MEBV structure showed highly antigenic properties while being non-toxic, soluble, non-allergenic, and stable in nature. Moreover, disulphide engineering further enhanced the stability of the final vaccine protein. Additionally, Molecular docking analysis and Molecular Dynamics (MD) simulations confirmed the strong association between MEBV construct and human pathogenic immune receptor TLR-3. Repeated-exposure simulations and Immune simulations ensured the rapid antigen clearance and higher levels of cell-mediated immunity, respectively. Furthermore, MEBV codon optimization and in-silico cloning was carried out to confirm its augmented expression. Results of our experiments suggested that the proposed MEBV could be a potential immunogenic against HTLV-1; nevertheless, additional wet lab experiments are needed to elucidate our conclusion.

Designing multi-epitope vaccine against Staphylococcus aureus by employing subtractive proteomics, reverse vaccinology and immuno-informatics approaches.

Staphylococcus aureus is a deadly human bacterial pathogen that causes a wide variety of clinical manifestations. Invasive S. aureus infections in hospitals and the community are one of the main causes of mortality and morbidity, as virulent and multi-drug-resistant strains have evolved. There is an unmet and urgent clinical need for immune-based non-antibiotic approaches to treat these infections as the growing antibiotic resistance poses a significant public health danger. Subtractive proteomics assisted reverse vaccinology-based immunoinformatics pipeline was used in this study to target the suitable antigenic proteins for the development of multi-epitope vaccine (MEV). Three essential virulent and antigenic proteins were identified including Glycosyltransferase, Elastin Binding Protein, and Staphylococcal secretory antigen. A variety of immunoinformatics tools have been used to forecast T-cell and B-cell epitopes from target proteins. Seven CTL, five HTL, and eight LBL epitopes, connected through suitable linkers and adjuvant, were employed to design 444 amino acids long MEV construct. The vaccine was paired with the TLR4 agonist 50S ribosomal protein L7/L12 adjuvant to enhance the immune response towards the vaccine. The predicted MEV structure was assessed to be highly antigenic, non-toxic, non-allergenic, flexible, stable, and soluble. Molecular docking simulation of the MEV with the human TLR4 (toll-like receptor 4) and major histocompatibility complex molecules (MHCI and MHCII) was performed to validate the interactions with the receptors. Molecular dynamics (MD) simulation and MMGBSA binding free energy analyses were carried out for the stability evaluation and binding of the MEV docked complexes with TLR4, MHCI and MHCII. To achieve maximal vaccine protein expression with optimal post-translational modifications, MEV was reverse translated, its mRNA structure was analyzed, and finally in silico cloning was performed into E. coli expression host. These rigorous computational analyses supported the effectivity of proposed MEV in protection against infections associated with S. aureus. However, further experimental validations are required to fully evaluate the potential of proposed vaccine candidate.

Integrated Core Proteomics, Subtractive Proteomics, and Immunoinformatics Investigation to Unveil a Potential Multi-Epitope Vaccine against Schistosomiasis.

Schistosomiasis is a parasitic infection that causes considerable morbidity and mortality in the world. Infections of parasitic blood flukes, known as schistosomes, cause the disease. No vaccine is available yet and thus there is a need to design an effective vaccine against schistosomiasis. Schistosoma japonicum, Schistosoma mansoni, and Schistosoma haematobium are the main pathogenic species that infect humans. In this research, core proteomics was combined with a subtractive proteomics pipeline to identify suitable antigenic proteins for the construction of a multi-epitope vaccine (MEV) against human-infecting Schistosoma species. The pipeline revealed two antigenic proteins-calcium binding and mycosubtilin synthase subunit C-as promising vaccine targets. T and B cell epitopes from the targeted proteins were predicted using multiple bioinformatics and immunoinformatics databases. Seven cytotoxic T cell lymphocytes (CTL), three helper T cell lymphocytes (HTL), and four linear B cell lymphocytes (LBL) epitopes were fused with a suitable adjuvant and linkers to design a 217 amino-acid-long MEV. The vaccine was coupled with a TLR-4 agonist (RS-09; Sequence: APPHALS) adjuvant to enhance the immune responses. The designed MEV was stable, highly antigenic, and non-allergenic to human use. Molecular docking, molecular dynamics (MD) simulations, and molecular mechanics/generalized Born surface area (MMGBSA) analysis were performed to study the binding affinity and molecular interactions of the MEV with human immune receptors (TLR2 and TLR4) and MHC molecules (MHC I and MHC II). The MEV expression capability was tested in an Escherichia coli (strain-K12) plasmid vector pET-28a(+). Findings of these computer assays proved the MEV as highly promising in establishing protective immunity against the pathogens; nevertheless, additional validation by in vivo and in vitro experiments is required to discuss its real immune-protective efficacy.