In silico approach for predicting toxicity of peptides and proteins

Display of porcine epidemic diarrhea virus spike protein B-cell linear epitope on Lactobacillus mucosae G01 S-layer surface induce a robust immunogenicity in mice

The Porcine epidemic diarrhea virus (PEDV) presents a substantial risk to the domestic pig industry, resulting in extensive and fatal viral diarrhea among piglets. Recognizing the mucosal stimulation triggered by PEDV and harnessing the regulatory impact of lactobacilli on intestinal function, we have developed a lactobacillus-based vaccine that is carefully designed to elicit a strong mucosal immune response. Through bioinformatics analysis, we examined PEDV S proteins to identify B-cell linear epitopes that meet the criteria of being non-toxic, soluble, antigenic, and capable of neutralizing the virus. In this study, a genetically modified strain of Lactobacillus mucosae G01 (L.mucosae G01) was created by utilizing the S layer protein (SLP) as a scaffold for surface presentation. Chimeric immunodominant epitopes with neutralizing activity were incorporated at various sites on SLP. The successful expression of SLP chimeric immunodominant epitope 1 on the surface of L.mucosae G01 was confirmed through indirect immunofluorescence and transmission electron microscopy, revealing the formation of a transparent membrane. The findings demonstrate that the oral administration of L.mucosae G01, which expresses the SLP chimeric immunodominant gene epitope1, induces the production of secreted IgA in the intestine and feces of mice. Additionally, there is an elevation in IgG levels in the serum. Moreover, the levels of cytokines IL-2, IL-4, IFN-γ, and IL-17 are significantly increased compared to the negative control group. These results suggest that L. mucosae G01 has the ability to deliver exogenous antigens and elicit a specific mucosal immune response against PEDV. This investigation presents new possibilities for immunoprophylaxis against PEDV-induced diarrhea.

Integrating 16S rRNA profiling and in-silico analysis for an epitope-based vaccine strategy against Achromobacter xylosoxidans infection

Achromobacter xylosoxidans is an aerobic, catalase-positive, non-pigment-forming, Gram-negative, and motile bacterium. It potentially causes a wide range of human infections in cystic fibrosis and non-cystic fibrosis patients. However, developing a safe preventive or therapeutic solution against A. xylosoxidans remains challenging. This study aimed to construct an epitope-based vaccine candidate using immunoinformatic techniques. A. xylosoxidans was isolated from an auto workshop in Lahore, and its identification was confirmed through 16S rRNA amplification and bioinformatic analysis. Two protein targets with GenBank accession numbers AKP90890.1 and AKP90355.1 were selected for the vaccine construct. Both proteins exhibited antigenicity, with scores of 0.757 and 0.580, respectively and the epitopes were selected based on the IC50 value using the ANN 4.0 and NN-align 2.3 epitope prediction method for MHC I and MHC II epitopes respectively and predicted epitopes were analyzed for antigenicity, allergenicity and pathogenicity. The vaccine construct demonstrated structural stability, thermostability, solubility, and hydrophilicity. The vaccine produced 250 B-memory cells per mm3 and approximately 16,000 IgM + IgG counts, indicating an effective immune response against A. xylosoxidans. Moreover, the vaccine candidate interacted stably with toll-like receptor 5, a pattern recognition receptor, with a confidence score of 0.98. These results highlight the potency of the designed vaccine candidate, suggesting its potential to withstand rigorous in vitro and in vivo clinical trials. This epitope-based vaccine could serve as the first preventive immunotherapy against A. xylosoxidans infections, addressing this bacterium's health and financial burdens. The findings demonstrate the value of employing immunoinformatic tools in vaccine development, paving the way for more precise and tailored approaches to combating microbial threats.

In-silico formulation of a next-generation polyvalent vaccine against multiple strains of monkeypox virus and other related poxviruses

Mpox (formerly known as monkeypox) virus and some related poxviruses including smallpox virus pose a significant threat to public health, and effective prevention and treatment strategies are needed. This study utilized a reverse vaccinology approach to retrieve conserved epitopes for monkeypox virus and construct a vaccine that could provide cross-protection against related viruses with similar antigenic properties. The selected virulent proteins of monkeypox virus, MPXVgp165, and Virion core protein P4a, were subjected to epitope mapping for vaccine construction. Two vaccines were constructed using selected T cell epitopes and B cell epitopes with PADRE and human beta-defensins adjuvants conjugated in the vaccine sequence. Both constructs were found to be highly antigenic, non-allergenic, nontoxic, and soluble, suggesting their potential to generate an adequate immune response and be safe for humans. Vaccine construct 1 was selected for molecular dynamic simulation studies. The simulation studies revealed that the TLR8-vaccine complex was more stable than the TLR3-vaccine complex. The lower RMSD and RMSF values of the TLR8 bound vaccine compared to the TLR3 bound vaccine suggested better stability and consistency of hydrogen bonds. The Rg values of the vaccine chain bound to TLR8 indicated overall stability, whereas the vaccine chain bound to TLR3 showed deviations throughout the simulation. These results suggest that the constructed vaccine could be a potential preventive measure against monkeypox and related viruses however, further experimental validation is required to confirm these findings.

Advancing molecular modeling and reverse vaccinology in broad-spectrum yellow fever virus vaccine development

Yellow fever outbreaks are prevalent, particularly in endemic regions. Given the lack of an established treatment for this disease, significant attention has been directed toward managing this arbovirus. In response, we developed a multiepitope vaccine designed to elicit an immune response, utilizing advanced immunoinformatic and molecular modeling techniques. To achieve this, we predicted B- and T-cell epitopes using the sequences from all structural (E, prM, and C) and nonstructural proteins of 196 YFV strains. Through comprehensive analysis, we identified 10 cytotoxic T-lymphocyte (CTL) and 5T-helper (Th) epitopes that exhibited overlap with B-lymphocyte epitopes. These epitopes were further evaluated for their affinity to a wide range of human leukocyte antigen system alleles and were rigorously tested for antigenicity, immunogenicity, allergenicity, toxicity, and conservation. These epitopes were linked to an adjuvant ( β -defensin) and to each other using ligands, resulting in a vaccine sequence with appropriate physicochemical properties. The 3D structure of this sequence was created, improved, and quality checked; then it was anchored to the Toll-like receptor. Molecular Dynamics and Quantum Mechanics/Molecular Mechanics simulations were employed to enhance the accuracy of docking calculations, with the QM portion of the simulations carried out utilizing the density functional theory formalism. Moreover, the inoculation model was able to provide an optimal codon sequence that was inserted into the pET-28a( +) vector for in silico cloning and could even stimulate highly relevant humoral and cellular immunological responses. Overall, these results suggest that the designed multi-epitope vaccine can serve as prophylaxis against the yellow fever virus.

Decrypting the multi-genome data for chimeric vaccine designing against the antibiotic resistant Yersinia pestis

Yersinia pestis, the causative agent of plague, is a gram-negative bacterium that can be fatal if not treated properly. Three types of plague are currently known: bubonic, septicemic, and pneumonic plague, among which the fatality rate of septicemic and pneumonic plague is very high. Bubonic plague can be treated, but only if antibiotics are used at the initial stage of the infection. But unfortunately, Y. pestis has also shown resistance to certain antibiotics such as kanamycin, minocycline, tetracycline, streptomycin, sulfonamides, spectinomycin, and chloramphenicol. Despite tremendous progress in vaccine development against Y. pestis, there is no proper FDA-approved vaccine available to protect people from its infections. Therefore, effective broad-spectrum vaccine development against Y. pestis is indispensable. In this study, vaccinomics-assisted immunoinformatics techniques were used to find possible vaccine candidates by utilizing the core proteome prepared from 58 complete genomes of Y. pestis. Human non-homologous, pathogen-essential, virulent, and extracellular and membrane proteins are potential vaccine targets. Two antigenic proteins were prioritized for the prediction of lead epitopes by utilizing reverse vaccinology approaches. Four vaccine designs were formulated using the selected B- and T-cell epitopes coupled with appropriate linkers and adjuvant sequences capable of inducing potent immune responses. The HLA allele population coverage of the T-cell epitopes selected for vaccine construction was also analyzed. The V2 constructs were top-ranked and selected for further analysis on the basis of immunological, physicochemical, and immune-receptor docking interactions and scores. Docking and molecular dynamic simulations confirmed the stability of construct V2 interactions with the host immune receptors. Immune simulation analysis anticipated the strong immune profile of the prioritized construct. In silico restriction cloning ensured the feasible cloning ability of the V2 construct in the expression system of E. coli strain K12. It is anticipated that the designed vaccine construct may be safe, effective, and able to elicit strong immune responses against Y. pestis infections and may, therefore, merit investigation using in vitro and in vivo assays.

Anti-HSV activity of nectin-1-derived peptides targeting HSV gD: an in-silico and in-vitro approach

Herpes simplex virus (HSV) infections affect a wide range of the global population. The emergence of resistance to the existing anti-HSV therapy highlights the necessity for an innovative strategy. The interaction of HSV gD with its main host receptor nectin-1 is a potential target for new antiviral drugs. The aim of this study was to develop a peptide derived from nectin-1 targeting HSV gD using the in-silico method and evaluate them for anti-HSV activity. Residues 59-133 of the Nectin-1 V-domain constitute the interaction interface with HSV gD. Bioinformatic tools viz., PEP-FOLD3, ClusPro 2.0, HawkDock and Desmond were used to model the peptide and confirm its binding specificity with HSV gD protein. The peptides with potential interactions were custom synthesized and anti-HSV activity was evaluated in vitro against HSV-1 and HSV-2 by CPE inhibition assay. Five peptide sequences were identified as exhibiting good interaction with HSV-gD proteins. Among them, peptide N1 (residues 76-90) offered maximum protection against HSV-1 (66.57%) and HSV-2 (71.12%) infections. Modification of the identified peptide through peptidomimetic approaches may further enhance the activity and stability of the identified peptide.Communicated by Ramaswamy H. Sarma.

Enhancing tuberculosis vaccine development: a deconvolution neural network approach for multi-epitope prediction

Tuberculosis (TB) a disease caused by Mycobacterium tuberculosis (Mtb) poses a significant threat to human life, and current BCG vaccinations only provide sporadic protection, therefore there is a need for developing efficient vaccines. Numerous immunoinformatic methods have been utilized previously, here for the first time a deep learning framework based on Deconvolutional Neural Networks (DCNN) and Bidirectional Long Short-Term Memory (DCNN-BiLSTM) was used to predict Mtb Multiepitope vaccine (MtbMEV) subunits against six Mtb H37Rv proteins. The trained model was used to design MEV within a few minutes against TB better than other machine learning models with 99.5% accuracy. The MEV has good antigenicity, and physiochemical properties, and is thermostable, soluble, and hydrophilic. The vaccine's BLAST search ruled out the possibility of autoimmune reactions. The secondary structure analysis revealed 87% coil, 10% beta, and 2% alpha helix, while the tertiary structure was highly upgraded after refinement. Molecular docking with TLR3 and TLR4 receptors showed good binding, indicating high immune reactions. Immune response simulation confirmed the generation of innate and adaptive responses. In-silico cloning revealed the vaccine is highly expressed in E. coli. The results can be further experimentally verified using various analyses to establish a candidate vaccine for future clinical trials.

Arginine and tryptophan-rich dendritic antimicrobial peptides that disrupt membranes for bacterial infection in vivo

The potent antibacterial activity and low resistance of antimicrobial peptides (AMPs) render them potential candidates for treating multidrug-resistant bacterial infections. Herein, a minimalist design strategy was proposed employing the "golden partner" combination of arginine (R) and tryptophan (W), along with a dendritic structure to design AMPs. By extension, the α/ε-amino group and the carboxyl group of lysine (K) were utilized to link R and W, forming dendritic peptide templates αRn(εRn)KWm-NH2 and αWn(εWn)KRm-NH2, respectively. The corresponding linear peptide templates R2nKWm-NH2 and W2nKRm-NH2 were used as controls. Their physicochemical properties, activity, toxicity, and stability were compared. Among these new peptides, the dendritic peptide R2(R2)KW4 was screened as a prospective candidate owing to its preferable antibacterial properties, biocompatibility, and stability. Additionally, R2(R2)KW4 not only effectively restrained the progression of antibiotic resistance, but also demonstrated synergistic utility when combined with conventional antibiotics due to its unique membrane-disruptive mechanism. Furthermore, R2(R2)KW4 possessed low toxicity (LD50 = 109.31 mg/kg) in vivo, while efficiently clearing E. coli in pulmonary-infected mice. In conclusion, R2(R2)KW4 has the potential to become an antimicrobial regent or adjuvant, and the minimalist design strategy of dendritic peptides provides innovative and encouraging thoughts in designing AMPs.

Immunoinformatics design of a structural proteins driven multi-epitope candidate vaccine against different SARS-CoV-2 variants based on fynomer

The ideal vaccines for combating diseases that may emerge in the future require more than simply inactivating a few pathogenic strains. This study aims to provide a peptide-based multi-epitope vaccine effective against various severe acute respiratory syndrome coronavirus 2 strains. To design the vaccine, a library of peptides from the spike, nucleocapsid, membrane, and envelope structural proteins of various strains was prepared. Then, the final vaccine structure was optimized using the fully protected epitopes and the fynomer scaffold. Using bioinformatics tools, the antigenicity, allergenicity, toxicity, physicochemical properties, population coverage, and secondary and three-dimensional structures of the vaccine candidate were evaluated. The bioinformatic analyses confirmed the high quality of the vaccine. According to further investigations, this structure is similar to native protein and there is a stable and strong interaction between vaccine and receptors. Based on molecular dynamics simulation, structural compactness and stability in binding were also observed. In addition, the immune simulation showed that the vaccine can stimulate immune responses similar to real conditions. Finally, codon optimization and in silico cloning confirmed efficient expression in Escherichia coli. In conclusion, the fynomer-based vaccine can be considered as a new style in designing and updating vaccines to protect against coronavirus disease.

Multi-epitopevaccines, from design to expression; an in silico approach

The development of vaccines against a wide range of infectious diseases and pathogens often relies on multi-epitope strategies that can effectively stimulate both humoral and cellular immunity. Immunoinformatics tools play a pivotal role in designing such vaccines, enhancing immune response potential, and minimizing the risk of failure. This review presents a comprehensive overview of practical tools for epitope prediction and the associated immune responses. These immunoinformatics tools facilitate the selection of epitopes based on parameters such as antigenicity, absence of toxic and allergenic sequences, secondary and tertiary structures, sequence conservation, and population coverage. The chosen epitopes can be tailored for B-cells or T-cells, both of which require further assessments covered in this study. We offer a range of suitable linkers that effectively separate cytotoxic T lymphocyte and helper T lymphocyte epitopes while preserving their functionality. Additionally, we identify various adjuvants for specific purposes. We delve into the evaluation of MHC-epitope interactions, MHC clusters, and the simulation of final constructs through molecular docking techniques. We provide diverse linkers and adjuvants optimized for epitope functions to bolster immune responses through epitope attachment. By leveraging these comprehensive tools, the development of multi-epitope vaccines holds the promise of robust immunity and a significant reduction in experimental costs.

In silico design of a novel multi-epitope vaccine against HCV infection through immunoinformatics approaches

Infection with the hepatitis C virus (HCV) is one of the causes of liver cancer, which is the world's sixth most prevalent and third most lethal cancer. The current treatments do not prevent reinfection; because they are expensive, their usage is limited to developed nations. Therefore, a prophylactic vaccine is essential to control this virus. Hence, in this study, an immunoinformatics method was applied to design a multi-epitope vaccine against HCV. The best B- and T-cell epitopes from conserved regions of the E2 protein of seven HCV genotypes were joined with the appropriate linkers to design a multi-epitope vaccine. In addition, cholera enterotoxin subunit B (CtxB) was included as an adjuvant in the vaccine construct. This study is the first to present this epitopes-adjuvant combination. The vaccine had acceptable physicochemical characteristics. The vaccine's 3D structure was predicted and validated. The vaccine's binding stability with Toll-like receptor 2 (TLR2) and TLR4 was confirmed using molecular docking and molecular dynamics (MD) simulation. The immune simulation revealed the vaccine's efficacy by increasing the population of B and T cells in response to vaccination. In silico expression in Escherichia coli (E. coli) was also successful.

Design of multi-epitope chimeric vaccine against Monkeypox virus and SARS-CoV-2: A vaccinomics perspective

The current era we experience is full with pandemic infectious agents that no longer threatens the major local source but the whole globe. Almost the most emerging infectious agents are severe acute respiratory syndrome coronavirus-2 (SARS CoV-2), followed by monkeypox virus (MPXV). Since no approved antiviral drugs nor licensed active vaccines are yet available, we aimed to utilize immunoinformatics approach to design chimeric vaccine against the two mentioned viruses. This is the first study to deal with design divalent vaccine against SARS-CoV-2 and MPXV. ORF8, E and M proteins from Omicron SARS-CoV-2 and gp182 from MPXV were used as the protein precursor from which multi-epitopes (inducing B-cell, helper T cells, cytotoxic T cells and interferon-ɣ) chimeric vaccine was contrived. The structure of the vaccine construct was predicted, validated, and docked to toll-like receptor-2 (TLR-2). Moreover, its sequence was also used to examine the immune simulation profile and was then inserted into the pET-28a plasmid for in silico cloning. The vaccine construct was probable antigen (0.543) and safe (non-allergen) with strong binding energy to TLR-2 (-1169.8 kcal/mol) and found to have significant immune simulation profile. In conclusion, the designed chimeric vaccine was potent and safe against SARS-CoV-2 and MPXV, which deserves further consideration.

A Multi-epitope Subunit Vaccine Identification and Development Against Scrub Typhus (Orientia tsutsugamushi) Using Immunoinformatics Approaches

Background The pathogen Orientia tsutsugamushi, which causes scrub typhus, is rapidly spreading throughout the tropics. As a measure to improve public health, the development of a vaccine for human use is essential. Scrub typhus is listed as one of the underdiagnosed and underreported febrile infections. This vector-borne zoonotic infection appears as eschar on the patient's skin. Methods Immunoinformatics was employed to predict the multi-epitope subunit vaccine that will activate both B and T cells. The final vaccine includes lipoprotein LprA as an adjuvant at the N-terminus along with B-cell, helper T lymphocyte (HTL), and cytotoxic T lymphocyte (CTL)-binding epitopes to boost immunogenicity. Assessing the vaccine's physiochemistry demonstrates that it is both antigenic and non-allergic. The vaccine structure was developed, enhanced, confirmed, and disulfide-engineered to provide the best possible model. Using molecular docking, the interaction of the produced vaccine with toll-like receptor 2 (TLR2) was analyzed, and the vaccine-receptor complex was stabilized by molecular dynamics (MD) simulation. According to in silico cloning, Escherichia coli can efficiently produce the recommended vaccine. Additionally, the efficacy of the in silico-developed vaccine must be evaluated in an in vitro and in vivo experiment. Results The developed vaccine successfully stimulates cellular and humoral immune responses. The vaccine, which has three B-cell epitopes, three HCL epitopes, and nine CTL epitopes, can bind firmly to immunological receptors. Dynamic investigations of the vaccine-receptor complex show a strong interaction and stable conformation. Conclusion In this study, the vaccine candidate demonstrated strong antigenicity, stability, and solubility while also being non-allergenic to host cells. The vaccine candidate's stability with the TLR2 immune receptor is established by binding studies, and in silico cloning verifies efficient and stable expression in the bacterial system.

Design and computational evaluation of a novel multi-epitope hybrid vaccine against monkeypox virus: Potential targets and immunogenicity assessment for pandemic preparedness

Monkeypox is a type of DNA-enveloped virus that belongs to the orthopoxvirus family, closely related to the smallpox virus. It can cause an infectious disease in humans known as monkeypox disease. Although there are multiple drugs and vaccines designed to combat orthopoxvirus infections, with a primary focus on smallpox, the recent spread of the monkeypox virus to over 50 countries have ignited a mounting global concern. This unchecked viral proliferation has raised apprehensions about the potential for a pandemic corresponding to the catastrophic impact of COVID-19. This investigation explored the structural proteins of monkeypox virus as potential candidates for designing a novel hybrid multi-epitope vaccine. The epitopes obtained from the selected proteins were screened to ensure their non-allergenicity, non-toxicity, and antigenicity to trigger T and B-cell responses. The interaction of the vaccine with toll-like receptor-3 (TLR-3) and major histocompatibility complexes (MHCs) was assessed using Cluspro 2.0. To establish the reliability of the docked complexes, a comprehensive evaluation was conducted using Immune and MD Simulations and Normal Mode Analysis. However, to validate the computational results of this study, additional in-vitro and in-vivo research is essential.

Lactobacilli-derived adjuvants combined with immunoinformatics-driven multi-epitope antigens based approach protects against Clostridium perfringens in a mouse model

Clostridium perfringens is ubiquitously distributed and capable of secreting toxins, posing a significant threat to animal health. Infections caused by Clostridium perfringens, such as Necrotic Enteritis (NE), result in substantial economic losses to the livestock industry annually. However, there is no effective commercial vaccine available. Hence, we set out to propose an effective approach for multi-epitope subunit vaccine construction utilizing biomolecules. We utilized immunoinformatics to design a novel multi-epitope antigen against C. perfringens (CPMEA). Furthermore, we innovated novel bacterium-like particles (BLPs) through thermal acid treatment of various Lactobacillus strains and selected BLP23017 among them. Then, we detailed the structure of CPMEA and BLPs and utilized them to prepare a multi-epitope vaccine. Here, we showed that our vaccine provided full protection against C. perfringens infection after a single dose in a mouse model. Additionally, BLP23017 notably augmented the secretion of secretory immunoglobulin A (sIgA) and enhanced antibody production. We conclude that our vaccine possess safety and high efficacy, making it an excellent candidate for preventing C. perfringens infection. Moreover, we demonstrate our approach to vaccine construction and the preparation of BLP23017 with distinct advantages may contribute to the prevention of a wider array of diseases and the novel vaccine development.

Development of membrane protein-based vaccine against lumpy skin disease virus (LSDV) using immunoinformatic tools

INTRODUCTION

Lumpy skin disease, an economically significant bovine illness, is now found in previously unheard-of geographic regions. Vaccination is one of the most important ways to stop its further spread.

AIM

Therefore, in this study, we applied advanced immunoinformatics approaches to design and develop an effective lumpy skin disease virus (LSDV) vaccine.

METHODS

The membrane glycoprotein was selected for prediction of the different B- and T-cell epitopes by using the immune epitope database. The selected B- and T-cell epitopes were combined with the appropriate linkers and adjuvant resulted in a vaccine chimera construct. Bioinformatics tools were used to predict, refine and validate the 2D, 3D structures and for molecular docking with toll-like receptor 4 using different servers. The constructed vaccine candidate was further processed on the basis of antigenicity, allergenicity, solubility, different physiochemical properties and molecular docking scores.

RESULTS

The in silico immune simulation induced significant response for immune cells. In silico cloning and codon optimization were performed to express the vaccine candidate in Escherichia coli. This study highlights a good signal for the design of a peptide-based LSDV vaccine.

CONCLUSION

Thus, the present findings may indicate that the engineered multi-epitope vaccine is structurally stable and can induce a strong immune response, which should help in developing an effective vaccine towards controlling LSDV infection.

Current treatment strategies for targeting virulence factors and biofilm formation in Acinetobacter baumannii

A higher prevalence of Acinetobacter baumannii infections and mortality rate has been reported recently in hospital-acquired infections (HAI). The biofilm-forming capability of A. baumannii makes it an extremely dangerous pathogen, especially in device-associated hospital-acquired infections (DA-HAI), thereby it resists the penetration of antibiotics. Further, the transmission of the SARS-CoV-2 virus was exacerbated in DA-HAI during the epidemic. This review specifically examines the complex interconnections between several components and genes that play a role in the biofilm formation and the development of infections. The current review provides insights into innovative treatments and therapeutic approaches to combat A. baumannii biofilm-related infections, thereby ultimately improving patient outcomes and reducing the burden of HAI.

Computational exploration of novel antimicrobial modalities targeting fucose-binding lectins and ribosomes in Mycobacterium smegmatis using tRNA-encoded peptides

tRNA-Encoded Peptides (tREPs), encoded by small open reading frames (smORFs) within tRNA genes, have recently emerged as a new class of functional peptides exhibiting antiparasitic activity. The discovery of tREPs has led to a re-evaluation of the role of tRNAs in biology and has expanded our understanding of the genetic code. This presents an immense, unexplored potential in the realm of tRNA-peptide interactions, paving the way for groundbreaking discoveries and innovative applications in various biological functions. This study explores the antimicrobial potential of tREPs against protein targets by employing a computational method that uses verified data sources and highly recognized predictive algorithms to provide a sorted list of likely antimicrobial peptides, which were then filtered for toxicity, cell permeability, allergenicity and half-life. These peptides were then docked with screened protein targets and computationally validated using molecular dynamics (MD) simulations for 150 ns and the binding free energy was estimated. The peptides Pep2 (VVLWRKPRVRKTG) and Pep6 (HRLRLRRRKPWW) exhibited good binding affinities of -110.5 +/- 2.5 and -129.0 +/- 3.9, respectively, with RMSD values of 0.4 and 0.25 nm against the fucose-binding lectin (7NEF) and the 30S ribosome of Mycobacterium smegmatis (5O5J) protein targets. The 7NEF-Pep2 and 5O5J-Pep6 complexes indicated higher negative binding free energies of -52.55 kcal/mol and -55.52 kcal/mol respectively, as calculated by Molecular Mechanics Poisson-Boltzmann Surface Area (MMPBSA). Thus, the tREPs derived peptides designed as a part of this study, provide novel approaches for potential anti-bacterial therapeutic modalities.Communicated by Ramaswamy H. Sarma.

A Structural In Silico Analysis of the Immunogenicity of L-Asparaginase from Penicillium cerradense

L-asparaginase is an essential drug used to treat acute lymphoid leukemia (ALL), a cancer of high prevalence in children. Several adverse reactions associated with L-asparaginase have been observed, mainly caused by immunogenicity and allergenicity. Some strategies have been adopted, such as searching for new microorganisms that produce the enzyme and applying protein engineering. Therefore, this work aimed to elucidate the molecular structure and predict the immunogenic profile of L-asparaginase from Penicillium cerradense, recently revealed as a new fungus of the genus Penicillium and producer of the enzyme, as a motivation to search for alternatives to bacterial L-asparaginase. In the evolutionary relationship, L-asparaginase from P. cerradense closely matches Aspergillus species. Using in silico tools, we characterized the enzyme as a protein fragment of 378 amino acids (39 kDa), including a signal peptide containing 17 amino acids, and the isoelectric point at 5.13. The oligomeric state was predicted to be a homotetramer. Also, this L-asparaginase presented a similar immunogenicity response (T- and B-cell epitopes) compared to Escherichia coli and Dickeya chrysanthemi enzymes. These results suggest a potentially useful L-asparaginase, with insights that can drive strategies to improve enzyme production.

Design of a multi-epitope-based vaccine candidate against Bovine Genital Campylobacteriosis using a reverse vaccinology approach

BACKGROUND

Bovine Genital Campylobacteriosis (BGC), a worldwide distributed venereal disease caused by Campylobacter fetus subsp. venerealis (Cfv), has a relevant negative economic impact in cattle herds. The control of BGC is hampered by the inexistence of globally available effective vaccines. The present in silico study aimed to develop a multi-epitope vaccine candidate against Cfv through reverse vaccinology.

RESULTS

The analysis of Cfv strain NCTC 10354 proteome allowed the identification of 9 proteins suitable for vaccine development. From these, an outer membrane protein, OmpA, and a flagellar protein, FliK, were selected for prediction of B-cell and T-cell epitopes. The top-ranked epitopes conservancy was assessed in 31 Cfv strains. The selected epitopes were integrated to form a multi-epitope fragment of 241 amino acids, which included 2 epitopes from OmpA and 13 epitopes from FliK linked by GPGPG linkers and connected to the cholera toxin subunit B by an EAAAK linker. The vaccine candidate was predicted to be antigenic, non-toxic, non-allergenic, and soluble upon overexpression. The protein structure was predicted and optimized, and the sequence was successfully cloned in silico into a plasmid vector. Additionally, immunological simulations demonstrated the vaccine candidate's ability to stimulate an immune response.

CONCLUSIONS

This study developed a novel vaccine candidate suitable for further in vitro and in vivo experimental validation, which may become a useful tool for the control of BGC.

Computational approach for identifying immunogenic epitopes and optimizing peptide vaccine through in-silico cloning against Mycoplasma genitalium

Mycoplasma genitalium is a pathogenic microorganism linked to a variety of severe health conditions including ovarian cancer, prostate cancer, HIV transmission, and sexually transmitted diseases. A more effective approach to address the challenges posed by this pathogen, given its high antibiotic resistance rates, could be the development of a peptide vaccine. In this study, we used experimentally validated 13 membrane proteins and their immunogenicity to identify suitable vaccine candidates. Thus, based on immunogenic properties and high conservation among other Mycoplasma genitalium sub-strains, the P110 surface protein is considered for further investigation. Later on, we identified T-cell epitopes and B-cell epitopes from the P110 protein to construct a multiepitope-based vaccine. As a result, the 'NIAPISFSFTPFTAA' T-cell epitope and 'KVKYESSGSNNISFDS' B-cell epitope have shown 99.53% and 87.50% population coverage along with 100% conservancy among the subspecies, and both epitopes were found to be non-allergenic. Furthermore, focusing on molecular docking analysis showed the lowest binding energy for MHC-I (-137.5 kcal/mol) and MHC-II (-183.3 kcal/mol), leading to a satisfactory binding strength between the T-cell epitopes and the MHC molecules. However, the constructed multiepitope vaccine (MEV) consisting of 54 amino acids demonstrates favorable characteristics for a vaccine candidate, including a theoretical pI of 4.25 with a scaled solubility of 0.812 and high antigenicity probabilities. Additionally, structural analyses reveal that the MEV displays substantial alpha helices and extended strands, vital for its immunogenicity. Molecular docking with the human Toll-like receptors TLR1/2 heterodimer shows strong binding affinity, reinforcing its potential to elicit an immune response. Our immune simulation analysis demonstrates immune memory development and robust immunity, while codon adaptation suggests optimal expression in E. coli using the pET-28a(+) vector. These findings collectively highlight the MEV's potential as a valuable vaccine candidate against M. genitalium.

Peptide based vaccine designing against endemic causing mammarenavirus using reverse vaccinology approach

The rodent-borne Arenavirus in humans has led to the emergence of regional endemic situations and has deeply emerged into pandemic-causing viruses. Arenavirus have a bisegmented ambisense RNA that produces four proteins: glycoprotein, nucleocapsid, RdRp and Z protein. The peptide-based vaccine targets the glycoprotein of the virus encountered by the immune system. Screening of B-Cell and T-Cell epitopes was done based on their immunological properties like antigenicity, allergenicity, toxicity and anti-inflammatory properties were performed. Selected epitopes were then clustered and epitopes were stitched using linker sequences. The immunological and physico-chemical properties of the vaccine construct was checked and modelled structure was validated by a 2-step MD simulation. The thermostability of the vaccine was checked followed by the immune simulation to test the immunogenicity of the vaccine upon introduction into the body over the course of the next 100 days and codon optimization was performed. Finally a 443 amino acid long peptide vaccine was designed which could provide protection against several members of the mammarenavirus family in a variety of population worldwide as denoted by the epitope conservancy and population coverage analysis. This study of designing a peptide vaccine targeting the glycoprotein of mammarenavirues may help develop novel therapeutics in near future.

In silico discovery of diagnostic/vaccine candidate antigenic epitopes and a multi-epitope peptide vaccine (NaeVac) design for the brain-eating amoeba Naegleria fowleri causing human meningitis

Naegleria fowleri, the brain-eating amoeba, is a free-living amoeboflagellate with three different life cycles (trophozoite, flagellated, and cyst) that lives in a variety of habitats around the world including warm freshwater and soil. It causes a disease called naegleriasis leading meningitis and primary amoebic meningoencephalitis (PAM) in humans. N. fowleri is transmitted through contaminated water sources such as insufficiently chlorinated swimming pool water or contaminated tap water, and swimmers are at risk. N. fowleri is found all over the world, and most infections were reported in both developed and developing countries with high mortality rates and serious clinical findings. Until now, there is no FDA approved vaccine and early diagnosis is urgent against this pathogen. In this study, by analyzing the N. fowleri vaccine candidate proteins (Mp2CL5, Nfa1, Nf314, proNP-A and proNP-B), it was aimed to discover diagnostic/vaccine candidate epitopes and to design a multi-epitope peptide vaccine against this pathogen. After the in silico evaluation, three prominent diagnostic/vaccine candidate epitopes (EAKDSK, LLPHIRILVY, and FYAKLLPHIRILVYS) with the highest antigenicities were discovered and a potentially highly immunogenic/antigenic multi-epitope peptide vaccine (NaeVac) was designed against the brain-eating amoeba N. fowleri causing human meningitis.

MultiToxPred 1.0: a novel comprehensive tool for predicting 27 classes of protein toxins using an ensemble machine learning approach

Protein toxins are defense mechanisms and adaptations found in various organisms and microorganisms, and their use in scientific research as therapeutic candidates is gaining relevance due to their effectiveness and specificity against cellular targets. However, discovering these toxins is time-consuming and expensive. In silico tools, particularly those based on machine learning and deep learning, have emerged as valuable resources to address this challenge. Existing tools primarily focus on binary classification, determining whether a protein is a toxin or not, and occasionally identifying specific types of toxins. For the first time, we propose a novel approach capable of classifying protein toxins into 27 distinct categories based on their mode of action within cells. To accomplish this, we assessed multiple machine learning techniques and found that an ensemble model incorporating the Light Gradient Boosting Machine and Quadratic Discriminant Analysis algorithms exhibited the best performance. During the tenfold cross-validation on the training dataset, our model exhibited notable metrics: 0.840 accuracy, 0.827 F1 score, 0.836 precision, 0.840 sensitivity, and 0.989 AUC. In the testing stage, using an independent dataset, the model achieved 0.846 accuracy, 0.838 F1 score, 0.847 precision, 0.849 sensitivity, and 0.991 AUC. These results present a powerful next-generation tool called MultiToxPred 1.0, accessible through a web application. We believe that MultiToxPred 1.0 has the potential to become an indispensable resource for researchers, facilitating the efficient identification of protein toxins. By leveraging this tool, scientists can accelerate their search for these toxins and advance their understanding of their therapeutic potential.

Designing a Novel Multiepitope Vaccine from the Human Papilloma Virus E1 and E2 Proteins for Indonesia with Immunoinformatics and Molecular Dynamics Approaches

One of the deadliest malignant cancer in women globally is cervical cancer. Specifically, cervical cancer is the second most common type of cancer in Indonesia. The main infectious agent of cervical cancer is the human papilloma virus (HPV). Although licensed prophylactic vaccines are available, cervical cancer cases are on the rise. Therapy using multiepitope-based vaccines is a very promising therapy for cervical cancer. This study aimed to develop a multiepitope vaccine based on the E1 and E2 proteins of HPV 16, 18, 45, and 52 using in silico. In this study, we develop a novel multiepitope vaccine candidate using an immunoinformatic approach. We predicted the epitopes of the cytotoxic T lymphocyte (CTL) and helper T lymphocyte (HTL) and evaluated their immunogenic properties. Population coverage analysis of qualified epitopes was conducted to determine the successful use of the vaccine worldwide. The epitopes were constructed into a multiepitope vaccine by using AAY linkers between the CTL epitopes and GPGPG linkers between the HTL epitopes. The tertiary structure of the multiepitope vaccine was modeled with AlphaFold and was evaluated by Prosa-web. The results of vaccine construction were analyzed for B-cell epitope prediction, molecular docking with Toll like receptor-4 (TLR4), and molecular dynamics simulation. The results of epitope prediction obtained 4 CTL epitopes and 7 HTL epitopes that are eligible for construction of multiepitope vaccines. Prediction of the physicochemical properties of multiepitope vaccines obtained good results for recombinant protein production. The interaction showed that the interaction of the multiepitope vaccine-TLR4 complex is stable based on the binding free energy value -106.5 kcal/mol. The results of the immune response simulation show that multiepitope vaccine candidates could activate the adaptive and humoral immune systems and generate long-term B-cell memory. According to these results, the development of a multiepitope vaccine with a reverse vaccinology approach is a breakthrough to develop potential cervical cancer therapeutic vaccines.

Characterization of four peptides from milk fermented with kombucha cultures and their metal complexes-in search of new biotherapeutics

The most common skin diseases include eczema, psoriasis, acne, and fungal infections. There is often no effective cure for them. Increasing antimicrobial drug resistance prompts us to search for new, safe, and effective therapeutics. Among such interesting candidates are peptides derived from milk fermented with specific lactic acid bacteria or with kombucha cultures, which are a potential treasure trove of bioactive peptides. Four of them are discussed in this article. Their interactions with zinc and copper ions, which are known to improve the well-being of the skin, were characterized by potentiometry, MS, ITC, and spectroscopic methods, and their cytostatic potential was analyzed. The results suggest that they are safe for human cells and can be used alone or in complexes with copper for further testing as potential therapeutics for skin diseases.

Impact of Lactiplantibacillus plantarum and casein fortification on angiotensin converting enzyme inhibitory peptides in yogurt: identification and in silico analysis

In this study, the effects of Lactiplantibacillus plantarum M11 (Lb. plantarum M11) in conjunction with sodium caseinate on the characteristics and angiotensin converting enzyme (ACE) inhibitory activity of yogurt were investigated. ACE inhibitory peptides (ACEIPs) in yogurt were identified by nano-LC-MS/MS and potential ACEIPs were predicted by in silico and molecular docking methods. The results showed that the ACE-inhibitory activity of yogurt was significantly enhanced (p < 0.05), while maintaining the quality characteristics of the yogurt. Thirteen ACEIPs in the improved yogurt (883 + M11-CS group) were identified, which were more abundant than the other yogurt groups (control 883 group, 883 + M11 group and 883-CS group). Two novel peptides with potential ACE inhibitory activity, YPFPGPIH and NILRFF, were screened. The two peptides showed PeptideRanker scores above 0.8, small molecular weight and strong hydrophobicity, and were non-toxic after prediction. Molecular docking results showed that binding energies with ACE were -9.4 kcal mol-1 and -10.7 kcal mol-1, respectively, and could bind to the active site of ACE. These results indicated that yogurt with Lb. plantarum M11 and sodium caseinate has the potential to be utilized as a functional food with antihypertensive properties. The combination of ACEIP-producing strains and casein fortification could be an effective method to promote the release of ACEIPs from yogurt.

Screening, separation and identification of metal-chelating peptides for nutritional, cosmetics and pharmaceutical applications

Metal-chelating peptides, which form metal-peptide coordination complexes with various metal ions, can be used as biofunctional ingredients notably to enhance human health and prevent diseases. This review aims to discuss recent insights into food-derived metal-chelating peptides, the strategies set up for their discovery, their study, and identification. After understanding the overall properties of metal-chelating peptides, their production from food-derived protein sources and their potential applications will be discussed, particularly in nutritional, cosmetics and pharmaceutical fields. In addition, the review provides an overview of the last decades of progress in discovering food-derived metal-chelating peptides, addressing several screening, separation and identification methodologies. Furthermore, it emphasizes the methods used to assess peptide-metal interaction, allowing for better understanding of chemical and thermodynamic parameters associated with the formation of peptide-metal coordination complexes, as well as the specific amino acid residues that play important roles in the metal ion coordination.

In silico screening of potential plant peptides against the non-structural proteins of dengue virus

BACKGROUND OBJECTIVES

Peptides isolated from different sources of plants have the advantages of specificity, lower toxicity, and increased therapeutic effects; hence, it is necessary to search for newer antivirals from plant sources for the treatment of dengue viral infections.

METHODS

In silico screening of selected plant peptides against the non-structural protein 1, NS3 protease domain (NS2B-NS3Pro) with the cofactor and ATPase/helicase domain (NS3 helicase domain/NS3hel) of dengue virus was performed. The physicochemical characteristics of the peptides were calculated using Protparam tools, and the allergenicity and toxicity profiles were assessed using allergenFP and ToxinPred, respectively.

RESULTS

Among the tested compounds, Ginkbilobin demonstrated higher binding energy against three tested nonstructural protein targets. Kalata B8 demonstrated maximum binding energy against NSP-1 and NSP-2, whereas Circulin A acted against the NSP3 protein of dengue virus.

INTERPRETATION CONCLUSION

The three compounds identified by in silico screening can be tested in vitro, which could act as potential leads as they are involved in hampering the replication of the dengue virus by interacting with the three prime non-structural proteins.

A simple tandem bioassay-guided SCX-RP SPE fractionation for efficient active peptide screening from Inca nut cake protein hydrolysate

Typically, bioactive peptides were uncovered from complex hydrolysates using sequential bioassay-guided fractionation. To increase the efficiency of bioactive peptide screening, a simple and convenient tandem bioassay-guided fractionation based on solid-phase extraction (SPE) was conducted to screen the angiotensin-I-converting enzyme (ACE) inhibitory peptides from the hydrolysate of Inca nut cake protein (INCP). The so-called SCX-RP SPE system was constructed by assembling SCX (strong cation exchange) and RP (reversed phase) SPE cartridges. Using this tandem SCX-RP SPE, the INCP digested with combined gastrointestinal protease (INCP GP) was fractionated into 30 fractions. The fraction F11 exhibited the highest ACE inhibitory activity among 30 fractions. The ACE IC50 of fraction F11 was calculated to be 6.6 ± 0.5 µg/mL. The ACEI activity of fraction F11 was stronger than the INCP GP hydrolysate (ACE IC50 of 12.7 ± 0.4 µg/mL). The tandem SCX-RP SPE fractionation reduced the number of ACE inhibitory (ACEI) peptide candidates from 127 peptides in the INCP GP hydrolysate to only ten peptides in fraction F11. Subsequently, WALPTQSW (WW-8) and WLPTKSW (WW-7) from fraction F11 were synthesized, and their ACE IC50 was determined to be 4.7 ± 0.1 and 7.9 ± 0.1 µM, respectively. The dipeptidyl peptidase-4 (DPP4) inhibitory and 2,2-diphenyl-1-picrylhydrazyl (DPPH) scavenging activities of WALPTQSW (WW-8) were also explored to give IC50 values of 131.7 ± 5.2 and 191.8 ± 7.0 µM, respectively. The molecular docking and inhibition mechanism studies indicated that WW-8 inhibited ACE and DPP4 as competitive and non-competitive inhibitors, respectively. The pre-incubation experiment of WW-8 toward ACE and DPP4 demonstrated that WW-8 was a true-inhibitor type. Additionally, the amount of WW-8 was quantified to be 5.8 ± 0.2 and 35 ± 0.4 µg per milligram hydrolysate and fraction F11, respectively. This study demonstrated tandem bioassay-guided SCX-RP SPE fractionation efficiently screened ACEI peptide derived from INCP GP hydrolysate, adding more value to Inca nut cake (a leftover of the oil industry) as a bioactive peptide precursor.

Identification of potential vaccine targets for elicitation of host immune cells against SARS-CoV-2 by reverse vaccinology approach

The COVID-19 pandemic has emerged as a critical global health crisis, demanding urgent and effective strategies for containment. While some knowledge exists about epitope sequences recognized by human immune cells and their activation of CD8+ T cells within the HLA context, comprehensive information remains limited. This study employs reverse vaccinology to explore antigenic HLA-restricted T-cell epitopes capable of eliciting durable immunity. Screening reveals 187 consensus epitopes, with 23 offering broad population coverage worldwide, spanning over 5000 HLA alleles. Sequence alignment analysis highlights the genetic distinctiveness of these peptides from Homo sapiens and their intermediate to high TAP binding efficiency. Notably, these epitopes share 100 % sequence identity across strains from nine countries, indicating potential for a uniform protective immune response among diverse ethnic populations. Docking simulations further confirm their binding capacity with the HLA allele, validating them as promising targets for SARS-CoV-2 immune recognition. The anticipated epitopes are connected with suitable linkers and adjuvant, and then assessed for its translational efficacy within a bacterial expression vector through computational cloning. Through docking, it is observed that the chimeric vaccine construct forms lasting hydrogen bonds with Toll-like receptor (TLR4), while immune simulation illustrates an increased cytotoxic response aimed at CD8+ T cells. This comprehensive computational analysis suggests the chimeric vaccine construct's potential to provoke a robust immune response against SARS-CoV-2. By delineating these antigenic fragments, our study offers valuable insights into effective vaccine and immunotherapy development against COVID-19, contributing significantly to global efforts in combating this infectious threat.

Rational design of multi-epitope-based vaccine by exploring all dengue virus serotypes proteome: an immunoinformatic approach

Millions of people's lives are being devastated by dengue virus (DENV), a severe tropical and subtropical illness spread by mosquitoes and other vectors. Dengue fever may be self-limiting like a common cold or can rapidly progress to catastrophic dengue hemorrhagic fever or dengue shock syndrome. With four distinct dengue serotypes (DENV1-4), each with the potential to contain antibody-boosting complicated mechanisms, developing a dengue vaccine has been an ambitious challenge. Here, we used a computational pan-vaccinomics-based vaccine design strategy (reverse vaccinology) for all 4 DENV serotypes acquired from different regions of the world to develop a new and safe vaccine against DENV. Consequently, only five mapped epitopes from all the 4 serotypes were shown to be extremely effective for the construction of multi-epitope vaccine constructs. The suggested vaccine construct V5 from eight vaccine models was thus classified as an antigenic, non-allergenic, and stable vaccine model. Moreover, molecular docking and molecular dynamics simulation was performed for the V5 vaccine candidate against the HLAs and TRL2 and 4 immunological receptors. Later, the vaccine sequence was transcribed into the cDNA to generate an expression vector for the Escherichia coli K12 strain. Our research suggests that this vaccine design (V5) has promising potential as a dengue vaccine. However, further experimental analysis into the vaccine's efficacy might be required for the V5 proper validation to combat all DENV serotypes.

Immunoinformatic based designing of potential immunogenic novel mRNA and peptide-based prophylactic vaccines against H5N1 and H7N9 avian influenza viruses

Influenza viruses are the most common cause of serious respiratory illnesses worldwide and are responsible for a significant number of annual fatalities. Therefore, it is crucial to look for new immunogenic sites that might trigger an effective immune response. In the present study, bioinformatics tools were used to design mRNA and multiepitope-based vaccines against H5N1 and H7N9 subtypes of avian influenza viruses. Several Immunoinformatic tools were employed to extrapolate T and B lymphocyte epitopes of HA and NA proteins of both subtypes. The molecular docking approach was used to dock the selected HTL and CTL epitopes with the corresponding MHC molecules. Eight (8) CTL, four (4) HTL, and Six (6) linear B cell epitopes were chosen for the structural arrangement of mRNA and of peptide-based prophylactic vaccine designs. Different physicochemical characteristics of the selected epitopes fitted with suitable linkers were analyzed. High antigenic, non-toxic, and non-allergenic features of the designed vaccines were noted at a neutral physiological pH. Codon optimization tool was used to check the GC content and CAI value of constructed MEVC-Flu vaccine, which were recorded to be 50.42% and 0.97 respectively. the GC content and CAI value verify the stable expression of vaccine in pET28a + vector. In-silico immunological simulation the MEVC-Flu vaccine construct revealed a high level of immune responses. The molecular dynamics simulation and docking results confirmed the stable interaction of TLR-8 and MEVC-Flu vaccine. Based on these parameters, vaccine constructs can be regarded as an optimistic choice against H5N1 and H7N9 strains of the influenza virus. Further experimental testing of these prophylactic vaccine designs against pathogenic avian influenza strains may clarify their safety and efficacy.Communicated by Ramaswamy H. Sarma.

In Silico Analysis of a Candidate Multi-epitope Peptide Vaccine Against Human Brucellosis

Brucellosis is one of the neglected endemic zoonoses in the world. Vaccination appears to be a promising health strategy to prevent it. This study used advanced computational techniques to develop a potent multi-epitope vaccine for human brucellosis. Seven epitopes from four main brucella species that infect humans were selected. They had significant potential to induce cellular and humoral responses. They showed high antigenic ability without the allergenic characteristic. In order to improve its immunogenicity, suitable adjuvants were also added to the structure of the vaccine. The physicochemical and immunological properties of the vaccine were evaluated. Then its two and three-dimensional structure was predicted. The vaccine was docked with toll-like receptor4 to assess its ability to stimulate innate immune responses. For successful expression of the vaccine protein in Escherichia coli, in silico cloning, codon optimization, and mRNA stability were evaluated. The immune simulation was performed to reveal the immune response profile of the vaccine after injection. The designed vaccine showed the high ability to induce immune response, especially cellular responses to human brucellosis. It showed the appropriate physicochemical properties, a high-quality structure, and a high potential for expression in a prokaryotic system.

Inferring B-cell derived T-cell receptor induced multi-epitope-based vaccine candidate against enterovirus 71: a reverse vaccinology approach

Purpose

Enterovirus 71, a pathogen that causes hand-foot and mouth disease (HFMD) is currently regarded as an increasing neurotropic virus in Asia and can cause severe complications in pediatric patients with blister-like sores or rashes on the hand, feet, and mouth. Notwithstanding the significant burden of the disease, no authorized vaccine is available. Previously identified attenuated and inactivated vaccines are worthless over time owing to changes in the viral genome.

Materials and Methods

A novel vaccine construct using B-cell derived T-cell epitopes from the virulent polyprotein found the induction of possible immune response. In order to boost the immune system, a beta-defensin 1 preproprotein adjuvant with EAAAK linker was added at the N-terminal end of the vaccine sequence.

Results

The immunogenicity of the designed, refined, and verified prospective three-dimensional-structure of the multi-epitope vaccine was found to be quite high, exhibiting non-allergenic and antigenic properties. The vaccine candidates bound to toll-like receptor 3 in a molecular docking analysis, and the efficacy of the potential vaccine to generate a strong immune response was assessed through in silico immunological simulation.

Conclusion

Computational analysis has shown that the proposed multi-epitope vaccine is possibly safe for use in humans and can elicit an immune response.

AutoEpiCollect, a Novel Machine Learning-Based GUI Software for Vaccine Design: Application to Pan-Cancer Vaccine Design Targeting PIK3CA Neoantigens

Previous epitope-based cancer vaccines have focused on analyzing a limited number of mutated epitopes and clinical variables preliminarily to experimental trials. As a result, relatively few positive clinical outcomes have been observed in epitope-based cancer vaccines. Further efforts are required to diversify the selection of mutated epitopes tailored to cancers with different genetic signatures. To address this, we developed the first version of AutoEpiCollect, a user-friendly GUI software, capable of generating safe and immunogenic epitopes from missense mutations in any oncogene of interest. This software incorporates a novel, machine learning-driven epitope ranking method, leveraging a probabilistic logistic regression model that is trained on experimental T-cell assay data. Users can freely download AutoEpiCollectGUI with its user guide for installing and running the software on GitHub. We used AutoEpiCollect to design a pan-cancer vaccine targeting missense mutations found in the proto-oncogene PIK3CA, which encodes the p110ɑ catalytic subunit of the PI3K kinase protein. We selected PIK3CA as our gene target due to its widespread prevalence as an oncokinase across various cancer types and its lack of presence as a gene target in clinical trials. After entering 49 distinct point mutations into AutoEpiCollect, we acquired 361 MHC Class I epitope/HLA pairs and 219 MHC Class II epitope/HLA pairs. From the 49 input point mutations, we identified MHC Class I epitopes targeting 34 of these mutations and MHC Class II epitopes targeting 11 mutations. Furthermore, to assess the potential impact of our pan-cancer vaccine, we employed PCOptim and PCOptim-CD to streamline our epitope list and attain optimized vaccine population coverage. We achieved a world population coverage of 98.09% for MHC Class I data and 81.81% for MHC Class II data. We used three of our predicted immunogenic epitopes to further construct 3D models of peptide-HLA and peptide-HLA-TCR complexes to analyze the epitope binding potential and TCR interactions. Future studies could aim to validate AutoEpiCollect's vaccine design in murine models affected by PIK3CA-mutated or other mutated tumor cells located in various tissue types. AutoEpiCollect streamlines the preclinical vaccine development process, saving time for thorough testing of vaccinations in experimental trials.

AutoPepVax, a Novel Machine-Learning-Based Program for Vaccine Design: Application to a Pan-Cancer Vaccine Targeting EGFR Missense Mutations

The current epitope selection methods for peptide vaccines often rely on epitope binding affinity predictions, prompting the need for the development of more sophisticated in silico methods to determine immunologically relevant epitopes. Here, we developed AutoPepVax to expedite and improve the in silico epitope selection for peptide vaccine design. AutoPepVax is a novel program that automatically identifies non-toxic and non-allergenic epitopes capable of inducing tumor-infiltrating lymphocytes by considering various epitope characteristics. AutoPepVax employs random forest classification and linear regression machine-learning-based models, which are trained with datasets derived from tumor samples. AutoPepVax, along with documentation on how to run the program, is freely available on GitHub. We used AutoPepVax to design a pan-cancer peptide vaccine targeting epidermal growth factor receptor (EGFR) missense mutations commonly found in lung adenocarcinoma (LUAD), colorectal adenocarcinoma (CRAD), glioblastoma multiforme (GBM), and head and neck squamous cell carcinoma (HNSCC). These mutations have been previously targeted in clinical trials for EGFR-specific peptide vaccines in GBM and LUAD, and they show promise but lack demonstrated clinical efficacy. Using AutoPepVax, our analysis of 96 EGFR mutations identified 368 potential MHC-I-restricted epitope-HLA pairs from 49,113 candidates and 430 potential MHC-II-restricted pairs from 168,669 candidates. Notably, 19 mutations presented viable epitopes for MHC I and II restrictions. To evaluate the potential impact of a pan-cancer vaccine composed of these epitopes, we used our program, PCOptim, to curate a minimal list of epitopes with optimal population coverage. The world population coverage of our list ranged from 81.8% to 98.5% for MHC Class II and Class I epitopes, respectively. From our list of epitopes, we constructed 3D epitope-MHC models for six MHC-I-restricted and four MHC-II-restricted epitopes, demonstrating their epitope binding potential and interaction with T-cell receptors. AutoPepVax's comprehensive approach to in silico epitope selection addresses vaccine safety, efficacy, and broad applicability. Future studies aim to validate the AutoPepVax-designed vaccines with murine tumor models that harbor the studied mutations.

Multi-epitope vaccine design for hepatitis E virus based on protein ORF2 and ORF3

Introduction

Hepatitis E virus (HEV), with heightened virulence in immunocompromised individuals and pregnant women, is a pervasive threat in developing countries. A globaly available vaccine against HEV is currently lacking.

Methods

We designed a multi-epitope vaccine based on protein ORF2 and ORF3 of HEV using immunoinformatics.

Results

The vaccine comprised 23 nontoxic, nonallergenic, soluble peptides. The stability of the docked peptide vaccine-TLR3 complex was validated by molecular dynamic simulations. The induction of effective cellular and humoral immune responses by the multi-peptide vaccine was verified by simulated immunization.

Discussion

These findings provide a foundation for future HEV vaccine studies.

Gonorrhea caused due to antimicrobial-resistant bacteria Neisseria gonorrhoeae treated using probiotic peptide

Neisseria gonorrhea is a sexually transmitted disease from gonorrhea that lacks treatment; despite the urgency, the absence of adequate drugs, lack of human correlates of protection, and inadequate animal models of infection have delayed progress toward the prevention of gonococcal infection. Lactobacillus crispatus is a lactic acid bacterium typically found in the human vaginal microbiota. Peptides from L. crispatus have shown a potential therapeutic option for targetting N. gonorrhea. Bioinformatics analysis is important for speeding up drug target acquisition, screening refinement, and evaluating adverse effects and drug resistance prediction. Therefore, this study identified an antimicrobial peptide from the bacteriocin immunity protein (BIP) of L. crispatus using the bioinformatics tool and Collection of Antimicrobial Peptide (CAMPR3). Based on the AMP score and highest ADMET properties, the peptide SM20 was chosen for docking analysis. SM20 was docked against multiple proteins from the genome of the AMR bacterium N. gonorrhea using an online tool; protein-peptide interactions were established and visualized using the PyMol visualizing tool. Molecular docking was carried out using the CABSdock tool, and multiple conformations were obtained against the membrane proteins of N. gonorrhoea. The peptide SM20 exhibited higher docking scores and ADMET properties. Therefore, SM20 could be further encapsulated with cellulose; it can be applied topically to the genital tract to target N. gonorrhea infection. The controlled release of the antimicrobial peptide from the gel can provide sustained delivery of the treatment, increasing its efficacy and reducing the risk of resistance development.

Identification of a Novel Peptide with Alcohol Dehydrogenase Activating Ability from Ethanol-Induced Lactococcus lactis: A Combined In Silico Prediction and In Vivo Validation

Alcohol dehydrogenase (ADH) is a crucial rate-limiting enzyme in alcohol metabolism. Our previous research found that ethanol-induced intracellular extracts of Lactococcus lactis (L. lactis) could enhance alcohol metabolism in mice, but the responsible compounds remain unidentified. The study aimed to screen potential ADH-activating peptides from ethanol-induced L. lactis using virtual screening and molecular docking calculation. Among them, the pentapeptide FAPEG might bind to ADH through hydrophobic interaction and hydrogen bonds, then enhancing ADH activity. Spectroscopy analysis further investigated the peptide-enzyme interaction between FAPEG and ADH, including changes in the amino acid residue microenvironment and secondary structural alterations. Furthermore, FAPEG could protect against alcoholic liver injury (ALI) in mice by reducing blood alcohol concentration, enhancing the activity of antioxidant and alcohol metabolism enzymes, and attenuating alcohol-induced hepatotoxicity, which was related to the activation of the Nrf2/keap1/HO-1 signaling pathway. The study provided preliminary evidence that the generation of ADH-activating peptides in ethanol-induced L. lactis has the potential in preventing ALI in mice using in silico prediction and in vivo validation approaches.

Enhancing explainable SARS-CoV-2 vaccine development leveraging bee colony optimised Bi-LSTM, Bi-GRU models and bioinformatic analysis

The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is a single-stranded RNA virus that caused the outbreak of the coronavirus disease 2019 (COVID-19). The COVID-19 outbreak has led to millions of deaths and economic losses globally. Vaccination is the most practical solution, but finding epitopes (antigenic peptide regions) in the SARS-CoV-2 proteome is challenging, costly, and time-consuming. Here, we proposed a deep learning method based on standalone Recurrent Neural networks to predict epitopes from SARS-CoV-2 proteins easily. We optimised the standalone Bidirectional Long Short-Term Memory (Bi-LSTM) and Bidirectional Gated Recurrent Unit (Bi-GRU) with a bioinspired optimisation algorithm, namely, Bee Colony Optimization (BCO). The study shows that LSTM-based models, particularly BCO-Bi-LSTM, outperform all other models and achieve an accuracy of 0.92 and AUC of 0.944. To overcome the challenge of understanding the model predictions, explainable AI using the Shapely Additive Explanations (SHAP) method was employed to explain how Blackbox models make decisions. Finally, the predicted epitopes led to the development of a multi-epitope vaccine. The multi-epitope vaccine effectiveness evaluation is based on vaccine toxicity, allergic response risk, and antigenic and biochemical characteristics using bioinformatic tools. The developed multi-epitope vaccine is non-toxic and highly antigenic. Codon adaptation, cloning, gel electrophoresis assess genomic sequence, protein composition, expression and purification while docking and IMMSIM servers simulate interactions and immunological response, respectively. These investigations provide a conceptual framework for developing a SARS-CoV-2 vaccine.

De novo antioxidant peptide design via machine learning and DFT studies

Antioxidant peptides (AOPs) are highly valued in food and pharmaceutical industries due to their significant role in human function. This study introduces a novel approach to identifying robust AOPs using a deep generative model based on sequence representation. Through filtration with a deep-learning classification model and subsequent clustering via the Butina cluster algorithm, twelve peptides (GP1-GP12) with potential antioxidant capacity were predicted. Density functional theory (DFT) calculations guided the selection of six peptides for synthesis and biological experiments. Molecular orbital representations revealed that the HOMO for these peptides is primarily localized on the indole segment, underscoring its pivotal role in antioxidant activity. All six synthesized peptides exhibited antioxidant activity in the DPPH assay, while the hydroxyl radical test showed suboptimal results. A hemolysis assay confirmed the non-hemolytic nature of the generated peptides. Additionally, an in silico investigation explored the potential inhibitory interaction between the peptides and the Keap1 protein. Analysis revealed that ligands GP3, GP4, and GP12 induced significant structural changes in proteins, affecting their stability and flexibility. These findings highlight the capability of machine learning approaches in generating novel antioxidant peptides.

Derivation of a novel antimicrobial peptide from the Red Sea Brine Pools modified to enhance its anticancer activity against U2OS cells

Cancer associated drug resistance is a major cause for cancer aggravation, particularly as conventional therapies have presented limited efficiency, low specificity, resulting in long term deleterious side effects. Peptide based drugs have emerged as potential alternative cancer treatment tools due to their selectivity, ease of design and synthesis, safety profile, and low cost of manufacturing. In this study, we utilized the Red Sea metagenomics database, generated during AUC/KAUST Red Sea microbiome project, to derive a viable anticancer peptide (ACP). We generated a set of peptide hits from our library that shared similar composition to ACPs. A peptide with a homeodomain was selected, modified to improve its anticancer properties, verified to maintain high anticancer properties, and processed for further in-silico prediction of structure and function. The peptide's anticancer properties were then assessed in vitro on osteosarcoma U2OS cells, through cytotoxicity assay (MTT assay), scratch-wound healing assay, apoptosis/necrosis detection assay (Annexin/PI assay), RNA expression analysis of Caspase 3, KI67 and Survivin, and protein expression of PARP1. L929 mouse fibroblasts were also assessed for cytotoxicity treatment. In addition, the antimicrobial activity of the peptide was also examined on E coli and S. aureus, as sample representative species of the human bacterial microbiome, by examining viability, disk diffusion, morphological assessment, and hemolytic analysis. We observed a dose dependent cytotoxic response from peptide treatment of U2OS, with a higher tolerance in L929s. Wound closure was debilitated in cells exposed to the peptide, while annexin fluorescent imaging suggested peptide treatment caused apoptosis as a major mode of cell death. Caspase 3 gene expression was not altered, while KI67 and Survivin were both downregulated in peptide treated cells. Additionally, PARP-1 protein analysis showed a decrease in expression with peptide exposure. The peptide exhibited minimal antimicrobial activity on critical human microbiome species E. coli and S. aureus, with a low inhibition rate, maintenance of structural morphology and minimal hemolytic impact. These findings suggest our novel peptide displayed preliminary ACP properties against U2OS cells, through limited specificity, while triggering apoptosis as a primary mode of cell death and while having minimal impact on the microbiological species E. coli and S. aureus.

Immunoinformatics and computational approaches driven designing a novel vaccine candidate against Powassan virus

Powassan virus (POWV) is an arthropod-borne virus (arbovirus) capable of causing severe illness in humans for severe neurological complications, and its incidence has been on the rise in recent years due to climate change, posing a growing public health concern. Currently, no vaccines to prevent or medicines to treat POWV disease, emphasizing the urgent need for effective countermeasures. In this study, we utilize bioinformatics approaches to target proteins of POWV, including the capsid, envelope, and membrane proteins, to predict diverse B-cell and T-cell epitopes. These epitopes underwent screening for critical properties such as antigenicity, allergenicity, toxicity, and cytokine induction potential. Eight selected epitopes were then conjugated with adjuvants using various linkers, resulting in designing of a potentially stable and immunogenic vaccine candidate against POWV. Moreover, molecular docking, molecular dynamics simulations, and immune simulations revealed a stable interaction pattern with the immune receptor, suggesting the vaccine's potential to induce robust immune responses. In conclusion, our study provided a set of derived epitopes from POWV's proteins, demonstrating the potential for a novel vaccine candidate against POWV. Further in vitro and in vivo studies are warranted to advance our efforts and move closer to the goal of combatting POWV and related arbovirus infections.

Protein profiling and immunoinformatic analysis of the secretome of a metal-resistant environmental isolate Pseudomonas aeruginosa S-8

The bacterial secretome represents a comprehensive catalog of proteins released extracellularly that have multiple important roles in virulence and intercellular communication. This study aimed to characterize the secretome of an environmental isolate Pseudomonas aeruginosa S-8 by analyzing trypsin-digested culture supernatant proteins using nano-LC-MS/MS tool. Using a combined approach of bioinformatics and mass spectrometry, 1088 proteins in the secretome were analyzed by PREDLIPO, SecretomeP 2.0, SignalP 4.1, and PSORTb tool for their subcellular localization and further categorization of secretome proteins according to signal peptides. Using the gene ontology tool, secretome proteins were categorized into different functional categories. KEGG pathway analysis identified the secreted proteins into different metabolic functional pathways. Moreover, our LC-MS/MS data revealed the secretion of various CAZymes into the extracellular milieu, which suggests its strong biotechnological applications to breakdown complex carbohydrate polymers. The identified immunodominant epitopes from the secretome of P. aeruginosa showed the characteristic of being non-allergenic, highly antigenic, nontoxic, and having a low risk of triggering autoimmune responses, which highlights their potential as successful vaccine targets. Overall, the identification of secreted proteins of P. aeruginosa could be important for both diagnostic purposes and the development of an effective candidate vaccine.

Peptide recovery from chicken feather keratin and their anti-biofilm properties against methicillin-resistant Staphylococcus aureus (MRSA)

Bacteria have the potential to adhere to abiotic surfaces, which has an undesirable effect in the food industry because they can survive for sustained periods through biofilm formation. In this study, an antibacterial peptide (ABP), with a molecular mass of 3861 Da, was purified from hydrolyzed chicken feathers using a locally isolated keratinolytic bacterium, namely Rhodococcus erythropolis, and its antibacterial and antibiofilm potential were investigated against planktonic and biofilm cells of Methicillin-Resistant Staphylococcus Aureus (MRSA). The results demonstrated that purified ABP showed the growth inhibition of MRSA cells with the minimum inhibitory concentration (MIC) of 45 µg/ml and disrupted MRSA biofilm formation at a concentration of 200 ug/ml, which results were confirmed by scanning electron micrograph (SEM). Moreover, the secondary structures of the peptide were assessed as part of the FTIR analysis to evaluate its mode of action. ExPASy tools were used to predict the ABP sequence, EPCVQUQDSRVVIQPSPVVVVTLPGPILSSFPQNTA, from a chicken feather keratin sequence database following in silico digestion by trypsin. Also, ABP had 54.29% hydrophobic amino acids, potentially contributing to its antimicrobial activity. The findings of toxicity prediction of the peptide by the ToxinPred tool revealed that ABP had non-toxic effects. Thus, these results support the potential of this peptide to be used as an antimicrobial agent for the treatment or prevention of MRSA biofilm formation in feed, food, or pharmaceutical applications.

A method for predicting linear and conformational B-cell epitopes in an antigen from its primary sequence

B-cell is an essential component of the immune system that plays a vital role in providing the immune response against any pathogenic infection by producing antibodies. Existing methods either predict linear or conformational B-cell epitopes in an antigen. In this study, a single method was developed for predicting both types (linear/conformational) of B-cell epitopes. The dataset used in this study contains 3875 B-cell epitopes and 3996 non-B-cell epitopes, where B-cell epitopes consist of both linear and conformational B-cell epitopes. Our primary analysis indicates that certain residues (like Asp, Glu, Lys, and Asn) are more prominent in B-cell epitopes. We developed machine-learning based methods using different types of sequence composition and achieved the highest AUROC of 0.80 using dipeptide composition. In addition, models were developed on selected features, but no further improvement was observed. Our similarity-based method implemented using BLAST shows a high probability of correct prediction with poor sensitivity. Finally, we developed a hybrid model that combines alignment-free (dipeptide based random forest model) and alignment-based (BLAST-based similarity) models. Our hybrid model attained a maximum AUROC of 0.83 with an MCC of 0.49 on the independent dataset. Our hybrid model performs better than existing methods on an independent dataset used in this study. All models were trained and tested on 80 % of the data using a cross-validation technique, and the final model was evaluated on 20 % of the data, called an independent or validation dataset. A webserver and standalone package named "CLBTope" has been developed for predicting, designing, and scanning B-cell epitopes in an antigen sequence available at (https://webs.iiitd.edu.in/raghava/clbtope/).

Computer-made peptide RQ18 acts as a dual antifungal and antibiofilm peptide though membrane-associated mechanisms of action

The spread of fungi resistant to conventional drugs has become a threatening problem. In this context, antimicrobial peptides (AMPs) have been considered as one of the main alternatives for controlling fungal infections. Here, we report the antifungal and antibiofilm activity and some clues about peptide RQ18's mechanism of action against Candida and Cryptococcus. This peptide inhibited yeast growth from 2.5 μM and killed all Candida tropicalis cells within 2 h incubation. Moreover, it showed a synergistic effect with antifungal agent the amphotericin b. RQ18 reduced biofilm formation and promoted C. tropicalis mature biofilms eradication. RQ18's mechanism of action involves fungal cell membrane damage, which was confirmed by the results of RQ18 in the presence of free ergosterol in the medium and fluorescence microscopy by Sytox green. No toxic effects were observed in murine macrophage cell lines and Galleria mellonella larvae, suggesting fungal target selectivity. Therefore, peptide RQ18 represents a promising strategy as a dual antifungal and antibiofilm agent that contributes to infection control without damaging mammalian cells.

Exploring the feasibility of bacteriocins EntK1 and EntEJ97s in treatment of systemic vancomycin resistant enterococci infections in mice

AIMS

Enterocins K1 and EJ97 have specific antimicrobial activity against Enterococcus faecium and Enterococcus faecalis, respectively. The aim of this study was to investigate the utility of these enterocins for in vivo treatment of systemic enterococcal infections.

METHODS AND RESULTS

The antimicrobial effect in blood was analysed and compared against the effect in saline. Colony forming unit counts revealed that the enterocins killed all the bacteria within 1 hour. Additionally, the bactericidal effect against E. faecalis was more rapid in blood, indicating a possible synergy between EntEJ97 and blood. Importantly, no enterocin resistant mutants emerged in these experiments. Injecting the enterocins intraperitoneally in an in vivo mouse model and using fluorescence and minimum inhibitory concentration determination to estimate concentrations of the peptides in plasma, indicate that the enterocins exist in circulation in therapeutic concentrations. Alanine aminotransferase detection, and haemolysis analysis indicates that there is no detectable liver damage or haemolytic effect after injection.

CONCLUSIONS

The study revealed that EntK1 and EntEJ97 are able to kill all bacteria ex vivo in the presence of blood. In vivo experiments determine that the enterocins exist in circulation in therapeutic concentrations without causing liver damage or haemolysis. Future experiments should test these peptides for treatment of infection in a relevant in vivo model.

In vitro and in silico analysis unravelled clinically desirable attributes of Bacillus altitudinis L-asparaginase

AIMS

To identify a marine L-asparaginase with clinically desirable attributes and characterize the shortlisted candidate through in silico tools.

METHODS AND RESULTS

Marine bacterial strains (number = 105) isolated from marine crabs were evaluated through a stepwise strategy incorporating the crucial attributes for therapeutic safety. The results demonstrated the potential of eight bacterial species for extracellular L-asparaginase production. However, only one isolate (Bacillus altitudinis CMFRI/Bal-2) showed clinically desirable attributes, viz. extracellular production, type-II nature, lack of concurrent L-glutaminase and urease activities, and presence of ansZ (functional gene for clinical type). The enzyme production was 22.55 ± 0.5 µM/mg protein/min within 24 h without optimization. The enzyme also showed good activity and stability in pH 7-8 and temperature 37°C, predicting the functioning inside the human body. The Michealis-Menten constant (Km) was 14.75 µM. Detailed in silico analysis based on functional gene authenticating the results of in vitro characterization and predicted the nonallergenic characteristic of the candidate. Docking results proved the higher affinity of the shortlisted candidate to L-asparagine than L-glutamine and urea.

CONCLUSION

Comprehensively, the study highlighted B. altitudinis type II asparaginase as a competent candidate for further research on clinically safe asparaginases.