ABCpred: a webserver for the discovery of acetyl- and butyryl-cholinesterase inhibitors

Development of a broad-spectrum epitope-based vaccine against Streptococcus pneumoniae

Streptococcus pneumoniae (SPN) is a significant pathogen causing pneumonia and meningitis, particularly in vulnerable populations like children and the elderly. Available pneumonia vaccines have limitations since they only cover particular serotypes and have high production costs. The emergence of antibiotic-resistant SPN strains further underscores the need for a new, cost-effective, broad-spectrum vaccine. Two potential vaccine candidates, CbpA and PspA, were identified, and their B-cell, CTL, and HTL epitopes were predicted and connected with suitable linkers, adjivant and PADRE sequence. The vaccine construct was found to be antigenic, non-toxic, non-allergenic, and soluble. The three-dimensional structure of the vaccine candidate was built and validated. Docking analysis of the vaccine candidate by ClusPro demonstrated robust and stable binding interactions between the MEV and toll-like receptor 4 in both humans and animals. The iMOD server and Amber v.22 tool has verified the stability of the docking complexes. GenScript server confirmed the high efficiency of cloning for the construct and in-silico cloning into the pET28a (+) vector using SnapGene, demonstrating successful translation of the epitope region. Immunological responses were shown to be enhanced by the C-IMMSIM server. This study introduced a strong peptide vaccine candidate that has the potential to contribute to the development of a rapid and cost-effective solution for combating SPN. However, experimental verification is necessary to evaluate the vaccine's effectiveness.

Bioinformatics designing of an mRNA vaccine for Mokola virus (MOKV) using immunoinformatics as a secure strategy for successful vaccine development

The Mokola Virus belongs to the family Rhabdoviridae and is genotype 3 of the Lyssavirus genera. A small number of cases of animal and human encephalomyelitis, mainly scattered over sub-Saharan Africa, have been linked to the Mokola Virus (MOKV). Currently there is no vaccine to protect against MOKV infection in people or animals. It has been proven that rabies vaccination does not confer immunity against MOKV infection, even though MOKV and the rabies virus are related. Using immunoinformatics approaches, this study designed an mRNA vaccine that can protect against all the five glycoproteins of the Mokola virus. NCBI was used to obtain the viral sequences, which were then screened for antigenicity, allergenicity, toxicity, B-cell epitopes, CD8 + T lymphocytes (CTL), and CD4 + T lymphocytes (HTL). These epitopes were used in the construction of the vaccine. Some extra co-translational residues were added to the mRNA vaccine construct. Its molecular weight is 129.19083 kDa, and its estimated pI is 8.58. It interacts rather steadily and with limited deformability with TLR 3, among other human innate immune receptors. Overall, the results show that the produced candidate vaccine is non-allergen, non-toxic, and can elicit T-cell and B-cell immune responses. These findings can further be subjected to in-vivo and in-vitro techniques for validation.

Allergies to Allergens from Cats and Dogs: A Review and Update on Sources, Pathogenesis, and Strategies

Inhalation allergies caused by cats and dogs can lead to a range of discomforting symptoms, such as rhinitis and asthma, in humans. With the increasing popularity of and care provided to these companion animals, the allergens they produce pose a growing threat to susceptible patients' health. Allergens from cats and dogs have emerged as significant risk factors for triggering asthma and allergic rhinitis worldwide; however, there remains a lack of systematic measures aimed at assisting individuals in recognizing and preventing allergies caused by these animals. This review provides comprehensive insights into the classification of cat and dog allergens, along with their pathogenic mechanisms. This study also discusses implementation strategies for prevention and control measures, including physical methods, gene-editing technology, and immunological approaches, as well as potential strategies for enhancing allergen immunotherapy combined with immunoinformatics. Finally, it presents future prospects for the prevention and treatment of human allergies caused by cats and dogs. This review will improve knowledge regarding allergies to cats and dogs while providing insights into potential targets for the development of next-generation treatments.

Design of Cryptococcus neoformans multi-epitope vaccine based on immunoinformatics method

Cryptococcus neoformans is a widely distributed opportunistic pathogenic fungus. While C. neoformans commonly infects immunocompromised individuals, it can also affect those who are immunocompetent. Transmission of C. neoformans primarily occurs through the respiratory tract, leading to the development of meningitis. The mortality rate of Cryptococcal meningitis is high, and treatment options are limited. Cryptococcus neoformans infections pose a significant public health threat and currently lack targeted and effective response strategies. This study aimed to screen T lymphocyte (cytotoxic T lymphocyte and helper T lymphocyte) and B lymphocyte epitopes derived from four C. neoformans antigens and develop two multi-epitope vaccines by combining them with various adjuvants. Molecular docking results demonstrated that the vaccines bind stably to Toll-like receptor 4 ( and induce innate immunity. The credibility of the molecular docking results was validated through subsequent molecular dynamics simulations. Furthermore, the results of immune simulation analyses underscored the multi-epitope vaccine's capability to effectively induce robust humoral and cellular immune responses within the host organism. These two vaccines have demonstrated theoretical efficacy against C. neoformans infection as indicated by computer analysis. Nevertheless, additional experimental validation is essential to substantiate the protective efficacy of the vaccines.

Expression of Recombinant Stonustoxin Alpha Subunit and Preparation of polyclonal antiserum for Stonustoxin Neutralization Studies

Stonustoxin (SNTX) is a lethal protein found in stonefish venom, responsible for many of the symptoms associated with stonefish envenomation. To counter stonefish venom challenges, antivenom is a well-established and effective solution. In this study, we aimed to produce the recombinant alpha subunit protein of Stonustoxin from Synanceia horrida and prepare antibodies against it The SNTXα gene sequence was optimized for E. coli BL21 (DE3) expression and cloned into the pET17b vector. Following purification, the recombinant protein was subcutaneously injected into rabbits, and antibodies were extracted from rabbit´s serum using a G protein column As a result of codon optimization, the codon adaptation index for the SNTXα cassette increased to 0.94. SDS-PAGE analysis validated the expression of SNTXα, with a band observed at 73.5 kDa with a yield of 60 mg/l. ELISA results demonstrated rabbits antibody titers were detectable up to a 1:256,000 dilution. The isolated antibody from rabbit´s serum exhibited a concentration of 1.5 mg/ml, and its sensitivity allowed the detection of a minimum protein concentration of 9.7 ng. In the neutralization assay the purified antibody against SNTXα protected mice challenged with 2 LD50. In conclusion, our study successfully expressed the alpha subunit of Stonustoxin in a prokaryotic host, enabling the production of antibodies for potential use in developing stonefish antivenom.

Advancing PEDV Vaccination: Comparison between Inactivated and Flagellin N-Terminus-Adjuvanted Subunit Vaccines

Vaccinations can serve as an important preventive measure against the porcine epidemic diarrhea (PED) virus that currently threatens the swine industry. This study focuses on the development of a fusion protein vaccine, FliC99-mCOE, which combines the N-terminus of flagellin (FliC99) with a modified core neutralizing epitope (mCOE) of PEDV. In silico immunoinformatic analysis confirmed the construct's non-toxic, non-allergenic, and highly antigenic nature. Molecular docking and molecular dynamics (MD) simulations demonstrated FliC99-mCOE's strong binding to the TLR-5 immunological receptor. Repeated exposure simulations and immunological simulations suggested enhanced cell-mediated immunity. Both FliC99-mCOE and an inactivated PEDV vaccine were produced and tested in mice. The results from cell proliferation, ELISA, and neutralization assays indicated that FliC99-mCOE effectively stimulated cellular immunity and neutralized PEDV. We conclude that the FliC99-mCOE fusion protein may serve as a promising vaccine candidate against PEDV.

Riding the wave of innovation: immunoinformatics in fish disease control

The spread of infectious illnesses has been a significant factor restricting aquaculture production. To maximise aquatic animal health, vaccination tactics are very successful and cost-efficient for protecting fish and aquaculture animals against many disease pathogens. However, due to the increasing number of immunological cases and their complexity, it is impossible to manage, analyse, visualise, and interpret such data without the assistance of advanced computational techniques. Hence, the use of immunoinformatics tools is crucial, as they not only facilitate the management of massive amounts of data but also greatly contribute to the creation of fresh hypotheses regarding immune responses. In recent years, advances in biotechnology and immunoinformatics have opened up new research avenues for generating novel vaccines and enhancing existing vaccinations against outbreaks of infectious illnesses, thereby reducing aquaculture losses. This review focuses on understanding in silico epitope-based vaccine design, the creation of multi-epitope vaccines, the molecular interaction of immunogenic vaccines, and the application of immunoinformatics in fish disease based on the frequency of their application and reliable results. It is believed that it can bridge the gap between experimental and computational approaches and reduce the need for experimental research, so that only wet laboratory testing integrated with in silico techniques may yield highly promising results and be useful for the development of vaccines for fish.

Characterization of exclusively non-commensal Neisseria gonorrhoeae pangenome to prioritize globally conserved and thermodynamically stable vaccine candidates using immune-molecular dynamic simulations

Neisseria gonorrhoeae (Ngo) has emerged as a global threat leading to one of the most common sexually transmitted diseases in the world. It has also become one of the leading antimicrobial resistant organisms, resulting in fewer treatment options and an increased morbidity. Therefore, in recent years, there has been an increased focus on the development of new treatments and preventive strategies to combat its infection. In this study, we have combined the most conserved epitopes from the completely assembled strains of Ngo to develop a universal and a thermodynamically stable vaccine candidate. For our vaccine design, the epitopes were selected for their high immunogenicity, non-allergenicity and non-cytotoxicity, making them the ideal candidates for vaccine development. For the screening process, several reverse vaccinology tools were employed to rigorously extract non-homologous and immunogenic epitopes from the selected proteins. Consequently, a total number of 3 B-cell epitopes and 6 T-cell epitopes were selected and joined by multiple immune-modulating adjuvants and linkers to generate a promiscuous immune response. Additionally, the stability and flexible nature of the vaccine construct was confirmed using various molecular dynamic simulation tools. Overall, the vaccine candidate showed promising binding affinity to various HLA alleles and TLR receptors; however, further studies are needed to assess its efficacy in-vivo. In this way, we have designed a multi-subunit vaccine candidate to potentially combat and control the spread of N. gonorrhoeae.

Exploring the utility of FTS as a bonafide binding partner for EGFR: A potential drug target for cervical cancer

Establishment of human papilloma virus (HPV) infection and its progression to cervical cancer (CC) requires the participation of epidermal growth factor (EGF) receptor (EGFR) and fused toes homolog (FTS). This review is an attempt to understand the structure-function relationship between FTS and EGFR as a tool for the development of newer CC drugs. Motif analysis was performed using national center for biotechnology information (NCBI), kyoto encyclopedia of genes and genomes (KEGG), simple modular architecture research tool (SMART) and multiple expectation maximizations for motif elicitation (MEME) database. The secondary and tertiary structure prediction of FTS was performed using DISOPRED3 and threading assembly, respectively. A positive correlation was found between the transcript levels of FTS and EGFR. Amino acids responsible for interaction between EGFR and FTS were determined. The nine micro-RNAs (miRNAs) that regulates the expression of FTS were predicted using Network Analyst 3.0 database. hsa-miR-629-5p and hsa-miR-615-3p are identified as significant positive and negative regulators of FTS gene expression. This review opens up new avenues for the development of CC drugs which interfere with the interaction between FTS and EGFR.

Genomic annotation for vaccine target identification and immunoinformatics-guided multi-epitope-based vaccine design against Songling virus through screening its whole genome encoded proteins

Songling virus (SGLV), a newly discovered tick-borne orthonairovirus, was recently identified in human spleen tissue. It exhibits cytopathic effects in human hepatoma cells and is associated with clinical symptoms including headache, fever, depression, fatigue, and dizziness, but no treatments or vaccines exist for this pathogenic virus. In the current study, immunoinformatics techniques were employed to identify potential vaccine targets within SGLV by comprehensively analyzing SGLV proteins. Four proteins were chosen based on specific thresholds to identify B-cell and T-cell epitopes, validated through IFN-γ epitopes. Six overlap MHC-I, MHC-II, and B cell epitopes were chosen to design a comprehensive vaccine candidate, ensuring 100% global coverage. These structures were paired with different adjuvants for broader protection against international strains. Vaccine constructions' 3D models were high-quality and validated by structural analysis. After molecular docking, SGLV-V4 was selected for further research due to its lowest binding energy (-66.26 kcal/mol) and its suitable immunological and physiochemical properties. The vaccine gene is expressed significantly in E. coli bacteria through in silico cloning. Immunological research and MD simulations supported its molecular stability and robust immune response within the host cell. These findings can potentially be used in designing safer and more effective experimental SGLV-V4 vaccines.

Designing of multi-epitope peptide vaccine against Acinetobacter baumannii through combined immunoinformatics and protein interaction-based approaches

Acinetobacter baumannii is one of the major pathogenic ESKAPE bacterium, which is responsible for about more than 722,000 cases in a year, globally. Despite the alarming increase in multidrug resistance, a safe and effective vaccine for Acinetobacter infections is still not available. Hence in the current study, a multiepitope vaccine construct was developed using linear B cell, cytotoxic T cell, and helper T cell epitopes from the antigenic and well-conserved lipopolysaccharide assembly proteins employing systematic immunoinformatics and structural vaccinology strategies. The multi-peptide vaccine was predicted to be highly antigenic, non-allergenic, non-toxic, and cover maximum population coverage worldwide. Further, the vaccine construct was modeled along with adjuvant and peptide linkers and validated to achieve a high-quality three-dimensional structure which was subsequently utilized for cytokine prediction, disulfide engineering, and docking analyses with Toll-like receptor (TLR4). Ramachandran plot showed 98.3% of the residues were located in the most favorable and permitted regions, thereby corroborating the feasibility of the modeled vaccine construct. Molecular dynamics simulation for a 100 ns timeframe further confirmed the stability of the binding vaccine-receptor complex. Finally, in silico cloning and codon adaptation were also performed with the pET28a (+) plasmid vector to determine the efficiency of expression and translation of the vaccine. Immune simulation studies demonstrated that the vaccine could trigger both B and T cell responses and can elicit strong primary, secondary, and tertiary immune responses. The designed multi-peptide subunit vaccine would certainly expedite the experimental approach for the development of a vaccine against A. baumannii infection.

Identification of vaccine candidates against rhodococcus equi by combining pangenome analysis with a reverse vaccinology approach

Rhodococcus equi (R. equi) is a zoonotic opportunistic pathogen that can cause life-threatening infections. The rapid evolution of multidrug-resistant R. equi and the fact that there is no currently licensed effective vaccine against R. equi warrant the need for vaccine development. Reverse vaccinology (RV), which involves screening a pathogen's entire genome and proteome using various web-based prediction tools, is considered one of the most effective approaches for identifying vaccine candidates. Here, we performed a pangenome analysis to determine the core proteins of R. equi. We then used the RV approach to examine the subcellular localization, host and gut flora homology, antigenicity, transmembrane helices, physicochemical properties, and immunogenicity of the core proteins to select potential vaccine candidates. The vaccine candidates were then subjected to epitope mapping to predict the exposed antigenic epitopes that possess the ability to bind with major histocompatibility complex I/II (MHC I/II) molecules. These vaccine candidates and epitopes will form a library of elements for the development of a polyvalent or universal vaccine against R. equi. Sixteen R. equi complete proteomes were found to contain 6,238 protein families, and the core proteins consisted of 3,969 protein families (∼63.63% of the pangenome), reflecting a low degree of intraspecies genomic variability. From the pool of core proteins, 483 nonhost homologous membrane and extracellular proteins were screened, and 12 vaccine candidates were finally identified according to their antigenicity, physicochemical properties and other factors. These included four cell wall/membrane/envelope biogenesis proteins; four amino acid transport and metabolism proteins; one cell cycle control, cell division and chromosome partitioning protein; one carbohydrate transport and metabolism protein; one secondary metabolite biosynthesis, transport and catabolism protein; and one defense mechanism protein. All 12 vaccine candidates have an experimentally validated 3D structure available in the protein data bank (PDB). Epitope mapping of the candidates showed that 16 MHC I epitopes and 13 MHC II epitopes with the strongest immunogenicity were exposed on the protein surface, indicating that they could be used to develop a polypeptide vaccine. Thus, we utilized an analytical strategy that combines pangenome analysis and RV to generate a peptide antigen library that simplifies the development of multivalent or universal vaccines against R. equi and can be applied to the development of other vaccines.

Peptide aptamer-based time-resolved fluoroimmunoassay for CHIKV diagnosis

BACKGROUND

Chikungunya virus (CHIKV) and Dengue virus (DENV) have similar clinical symptoms, which often induce misdiagnoses. Therefore, an antigen detection diagnostic system that can clearly identify these two viruses is desirable.

METHODS

In this study, we developed a novel peptide with high affinity and specificity to CHIKV, and further constructed peptide aptamer-based TRFIA assay to efficiently detect CHIKV. Peptide aptamer B2 (ITPQSSTTEAEL) and B3 (DTQGSNWI) were obtained through computer-aided design and selected as CHIKV-specific peptide aptamers based on their high binding affinity, strong hydrogen bonding, and RMSD of molecular docking. Then, a sandwich-Time-Resolved Fluoroimmunoassay (TRFIA) was successfully constructed for the detection of the interaction between peptide aptamers and viruses.

RESULTS

When using B2 as the detection element, highly specific detection of CHIKV E2 was achieved with detection limits of 8.5 ng/ml in PBS solution. Variation coefficient between inter-assay showed the disturbances received from the detection of clinical fluid specimens (including serum and urine), were also within acceptable limits. The detection limits for 10-fold dilution serum and urine were 57.8 ng/mL and 147.3 ng/mL, respectively. The fluorescent signal intensity exhibited a good linear correlation with E2 protein concentration in the range of 0-1000 ng/mL, indicating the potential for quantitative detection of E2 protein.

CONCLUSIONS

These results demonstrate that the construction of peptide aptamers with high affinity and specificity provides an excellent method for rapid diagnostic element screening, and the developed peptide aptamer B2 contributed to better detection of CHIKV viral particles compared to traditional antibodies.

In Silico Characterization of an Important Metacyclogenesis Marker in Leishmania donovani, HASPB1, as a Potential Vaccine Candidate

Visceral leishmaniasis is a life-threatening infectious disease worldwide. Extensive experiments have been done to introduce potential vaccine candidates to combat leishmaniasis. The present study was done to evaluate Leishmania donovani hydrophilic acylated surface protein B1 as a potential vaccine candidate using in silico methods. For this aim, server-based predictions were performed regarding physicochemical characteristics, solubility, antigenicity, allergenicity, signal peptide, transmembrane domain, and posttranslational modifications (PTMs). Also, secondary and tertiary structures were predicted using NetSurfP-3.0 and I-TASSER, respectively. The 3D model was further subjected to refinement and validation, and promising B-cell, cytotoxic T-lymphocyte (CTL; human, dog), and helper T-lymphocyte (HTL; human) epitopes were predicted. The protein had a molecular weight of 42.19 kDa, with high solubility (0.749), stability (instability index: 21.34), and hydrophilicity (GRAVY: -2.322). No signal peptide or transmembrane domain was predicted, and the most abundant PTMs were phosphorylation, O-glycosylation, and acetylation. Many coils and disordered regions existed in the secondary structure analysis, and the tertiary model had a good confidence score (-0.79). Next, the ProSA-web and PROCHECK tools showed adequate improvements in the refined model compared to the crude model. Only 4 shared B-cell epitopes among three web servers (ABCpred, BepiPred 2.0, and SVMTriP) were shown to be antigenic, nonallergenic, and with good water solubility. Also, five potent CTL epitopes in dogs and five in humans were predicted. Notably, two HTL epitopes were found to be potential IFN-γ inducers. In conclusion, our results demonstrated several immunogenic epitopes in this protein, which could be directed towards multiepitope vaccine design.

Identification of Bacterial Strains and Development of anmRNA-Based Vaccine to Combat Antibiotic Resistance in Staphylococcus aureus via In Vitro and In Silico Approaches

The emergence of antibiotic-resistant microorganisms is a significant concern in global health. Antibiotic resistance is attributed to various virulent factors and genetic elements. This study investigated the virulence factors of Staphylococcus aureus to create an mRNA-based vaccine that could help prevent antibiotic resistance. Distinct strains of the bacteria were selected for molecular identification of virulence genes, such as spa, fmhA, lukD, and hla-D, which were performed utilizing PCR techniques. DNA extraction from samples of Staphylococcus aureus was conducted using the Cetyl Trimethyl Ammonium Bromide (CTAB) method, which was confirmed and visualized using a gel doc; 16S rRNA was utilized to identify the bacterial strains, and primers of spa, lukD, fmhA, and hla-D genes were employed to identify the specific genes. Sequencing was carried out at Applied Bioscience International (ABI) in Malaysia. Phylogenetic analysis and alignment of the strains were subsequently constructed. We also performed an in silico analysis of the spa, fmhA, lukD, and hla-D genes to generate an antigen-specific vaccine. The virulence genes were translated into proteins, and a chimera was created using various linkers. The mRNA vaccine candidate was produced utilizing 18 epitopes, linkers, and an adjuvant, known as RpfE, to target the immune system. Testing determined that this design covered 90% of the population conservancy. An in silico immunological vaccine simulation was conducted to verify the hypothesis, including validating and predicting secondary and tertiary structures and molecular dynamics simulations to evaluate the vaccine’s long-term viability. This vaccine design may be further evaluated through in vivo and in vitro testing to assess its efficacy.

Bioinformatics and immunoinformatics approach to develop potent multi-peptide vaccine for coxsackievirus B3 capable of eliciting cellular and humoral immune response

Coxsackievirus B3 (CVB3) is a viral pathogen of various human disorders with no effective preventative interventions. Herein, we aimed to design a chimeric vaccine construct for CVB3 using reverse vaccinology and immunoinformatics approaches by screening the whole viral polyprotein sequence. Firstly, screening and mapping of viral polyprotein to predict 21 immunodominant epitopes (B-cell, CD8+ and CD4+ T-cell epitopes), fused with an adjuvant (Resuscitation-promoting factor), appropriate linkers, HIV-TAT peptide, Pan DR epitope, and 6His-tag to assemble a multi-epitope vaccine construct. The chimeric construct is predicted as probable antigen, non-allergen, stable, possess encouraging physicochemical features, and indicates a broader population coverage (98 %). The tertiary structure of the constructed vaccine was predicted and refined, and its interaction with the Toll-like receptor 4 (TLR4) was investigated through molecular docking and dynamics simulation. Computational cloning of the construct was carried out in pET28a (+) plasmid to guarantee the higher expression of the vaccine protein. Lastly, in silico immune simulation foreseen that humoral and cellular immune responses would be elicited in response to the administration of such a potent chimeric construct. Thus, the design constructed could vaccinate against CVB3 infection and various CVB serotypes. However, further in vitro/in vivo research must assess its safety and effectiveness.

Employing an immunoinformatics approach revealed potent multi-epitope based subunit vaccine for lymphocytic choriomeningitis virus

BACKGROUND

Lymphocytic choriomeningitis virus (LCMV) infects many individuals worldwide and causes severe infection in the immunosuppressant recipient, spontaneous abortion, and congenital disabilities in infants.

OBJECTIVES

There is no specific vaccine or therapeutics available to protect against LCMV infection; thus, there is a need to design a potential vaccine to combat the virus by developing immunity in the population. Herein, we attempted to design a potent multi-epitope vaccine for LCMV using immunoinformatics methods.

METHODS

The whole proteome of the virus was screened and mapped to extract immunodominant B-cell and T-cell epitopes which were fused with appropriate linkers (EAAAK, GGGS, AAY, GPGPG, and AAY), PADRE sequence (13aa) and an adjuvant (50 S ribosomal protein L7/L12) to formulate a multi-epitope vaccine ensemble. Codon adaptation and in silico cloning of the constructed vaccine were carried out using bioinformatics tools. The secondary and tertiary structure of the vaccine construct was predicted and refined. The physicochemical profile of the designed vaccine was analyzed, and the multi-epitope vaccine's potential to bind Toll-like receptors (TLR2 and TLR4) was evaluated through molecular docking and molecular dynamics simulations. Computational immune simulation of the designed vaccine antigen was performed using the C-ImmSim server.

RESULTS

The designed multi-epitope-based vaccine (613 aa) comprised 26 immunodominant (six B-cell, nine cytotoxic T lymphocytes, and 11 helper T lymphocytes) epitopes and is predicted antigenic, non-toxic, non-allergen, soluble, and topographically accessible with a suitable physicochemical profile. The designed vaccine is expected to cover a broad worldwide population (96.35 %) and stimulate a robust adaptive immune response against the virus upon administration. In silico cloning of the constructed vaccine in PET28a (+) vector ensured its optimal expression in the Escherichia coli system. Molecular docking, molecular dynamics simulation, and binding free energy estimation collectively support the stability and energetically favourable interaction of the modeled vaccine-TLR2/4 complexes.

CONCLUSION

The designed multi-epitope vaccine in the present study could serve as a potential vaccine candidate to protect against LMCV infection; however, the experimental validation and safety testing of the vaccine is warranted to validate the study's outcomes.

Insight into potent TLR2 inhibitors for the treatment of disease caused by Mycoplasma pneumoniae based on machine learning approaches

Mycoplasma pneumoniae (MP) is one of the most common pathogens that causes acute respiratory tract infections. Children experiencing MP infection often suffer severe complications, lung injury, and even death. Previous studies have demonstrated that Toll-like receptor 2 (TLR2) is a potential therapeutic target for treating the MP-induced inflammatory response. However, the screening of natural compounds has received more attention for the treatment of bacterial infections to reduce the likelihood of bacterial resistance. Herein, we screened compounds by combining molecular docking and machine learning approaches to find potential lead compounds for treating MP infection. First, all compounds were docked with the TLR2 receptor protein to screen for potential candidates. To predict drug bioactivity, a machine learning model (random forest) was trained for TLR2 inhibitors to obtain the predictive model. The model achieved significant squared correlation coefficient (R²) values for the training set (0.85) and validation set (0.84) of compounds. The developed machine learning model was then used to predict the pIC50 values of the top 50 candidates from the Traditional Chinese compounds and Discovery Diversity sets of compounds. As a result, these compounds are capable of inhibiting the inflammatory response induced by MP. However, prior to bringing these compounds to market, it is necessary to verify these results with additional biological testing, including preclinical and clinical studies. Moreover, the present study provides a theoretical basis for the use of natural compounds as potential candidates to treat pneumonia caused by MP.

Insight into TLR4 receptor inhibitory activity via QSAR for the treatment of Mycoplasma pneumonia disease

Mycoplasma pneumoniae (MP) is one of the most common pathogenic organisms causing upper and lower respiratory tract infections, lung injury, and even death in young children. Toll-like receptors (TLRs) play an important role in innate immunity by allowing the host to recognize pathogens invading the body. Previous studies demonstrated that TLR4 is a potential therapeutic target for the treatment of MP pneumonia. Therefore, the present study aimed to screen biologically active ingredients that target the TLR4 receptor pathway. We first used molecular docking to screen out the active compounds inhibiting the TLR4 pathway, and then used regression and classification machine learning algorithms to establish a quantitative structure-activity relationship (QSAR) model to predict the biological activity of the screened compounds. A total of 78 molecules were used in QSAR modelling, which were retrieved from the ChEMBL database. The QSAR models had acceptable correlation coefficients of R ² on the training and testing dataset in the range of 0.96 to 0.91 and 0.93 to 0.76, respectively. The multiclass classification models showed accuracy on training and testing data within ranges of 1.0 to 0.70, 0.96 to 0.63, and log loss ranges from 0.27 to 8.63, respectively. In addition, molecular descriptors and fingerprints have been studied as structural elements involved in increased and decreased inhibitory activities. These results provide a quantitative analysis of QSAR and classification models applicable for high-throughput screening, as well as insights into the mechanisms of inhibition of TLR4 antagonists.

Computational Vaccine Design for Poxviridae Family Viruses

The computational approach to designing vaccines has several useful characteristics over traditional vaccine development, such as being highly specific, less time-consuming and less expensive. Thus, this chapter describes an immunoinformatics workflow to design a vaccine against a member of the Poxviridae family known as Monkeypox virus. The immunoinformatics approach uses several online servers to select highly antigenic and non-allergenic CTL, HTL, and B cell epitopes. Then, it links the predicted epitopes through linkers and submit them for 3D structure modeling. Afterward, the modeled vaccine is docked with TLRs to check the induction of the immune system. Finally, immune simulations are performed to check the level of several immune factors like IgG, IgM, cytokines and interleukins, among others, upon the injection of the constructed vaccine. This approach can be used to successfully design novel and effective vaccine candidates against emerging species from the Poxviridae family.

Setomimycin as a potential molecule for COVID‑19 target: in silico approach and in vitro validation

Abstract

COVID-19 pandemic caused by the SARS-CoV-2 virus has led to a worldwide crisis. In view of emerging variants time to time, there is a pressing need of effective COVID-19 therapeutics. Setomimycin, a rare tetrahydroanthracene antibiotic, remained unexplored for its therapeutic uses. Herein, we report our investigations on the potential of setomimycin as COVID-19 therapeutic. Pure setomimycin was isolated from Streptomyces sp. strain RA-WS2 from NW Himalayan region followed by establishing in silico as well as in vitro anti-SARS-CoV-2 property of the compound against SARS-CoV-2 main protease (M^pro). It was found that the compound targets M^pro enzyme with an IC₅₀ value of 12.02 ± 0.046 μM. The molecular docking study revealed that the compound targets Glu166 residue of M^pro enzyme, hence preventing dimerization of SARS-CoV-2 M^pro monomer. Additionally, the compound also exhibited anti-inflammatory and anti-oxidant property, suggesting that setomimycin may be a viable option for application against COVID-19 infections.

Graphical abstract