ProInflam: a webserver for the prediction of proinflammatory antigenicity of peptides and proteins

Design of a multi-epitope vaccine (vme-VAC/MST-1) against cholera and vibriosis based on reverse vaccinology and immunoinformatics approaches

Vibriosis and cholera are serious diseases distributed worldwide and caused by six marine bacteria of the Vibrio genus. Thousands of deaths occur each year due to these illnesses, necessitating the development of new preventive measures. Presently, the existing cholera vaccine demonstrates an effectiveness of approximately 60%. Here we describe a new multi-epitope vaccine, 'vme-VAC/MST-1' based on vaccine targets identified by reverse vaccinology and epitopes predicted by immunoinformatics, two currently effective tools for predicting new vaccines for bacterial pathogens. The vaccine was designed to combat vibriosis and cholera by incorporating epitopes predicted for CTL, HTL, and B cells. These epitopes were identified from six vaccine targets revealed through subtractive genomics, combined with reverse vaccinology, and were further filtered using immunoinformatics approaches based on their predicted immunogenicity. To construct the vaccine, 28 epitopes (24 CTL/B and 4 HTL/B) were linked to the sequence of the cholera toxin B subunit adjuvant. In silico analyses indicate that the resulting immunogen is stable, soluble, non-toxic, and non-allergenic. Furthermore, it exhibits no homology to the host and demonstrates a strong capacity to elicit innate, B-cell, and T-cell immune responses. Our analysis suggests that it is likely to elicit immune reactions mediated through the TLR5 pathway, as evidenced by the molecular docking of the vaccine with the receptor, which revealed high affinity and a favorable reaction. Thus, vme-VAC/MST-1 is predicted to be a safe and effective solution against pathogenic Vibrio spp. However, further experimental analyses are required to measure the vaccine's effects In vivo.Communicated by Ramaswamy H. Sarma.

Computational structure-based design of antiviral peptides as potential protein-protein interaction inhibitors of rabies virus phosphoprotein and human LC8

Rabies is a serious zoonotic disease caused by the rabies virus (RABV). Despite the successful development of vaccines and efforts made in drug discovery, rabies is incurable. Therefore, development of novel drugs is of interest to the scientific community. Antiviral peptides can be designed based on the known structures of viral proteins and their biological targets. Cytoplasmic dynein light chain LC8, one of the first identified host partners of RABV phosphoprotein (RABV P), is an essential factor for RABV transcription and replication. As part of the search for new potential drugs against rabies, we used structure-based drug design using the in silico tools. The binding site of LC8 with RABV P was used for peptide design. Four potential peptide inhibitors (Pep1-4) were selected, modeled, and docked with RABV P. The highest binding affinity was observed for the RABV P-Pep2 complex. Molecular dynamics (MD) simulations were performed and the stability of the peptides and complexes was confirmed. Finally, Pep2 can be used as a potential candidate for peptide-based antiviral therapy against RABV. The identified small peptides may prevent RABV infection based on the results of the current investigation. Further in vitro and in vivo studies are needed to confirm these results.

MultiFeatVotPIP: a voting-based ensemble learning framework for predicting proinflammatory peptides

Inflammatory responses may lead to tissue or organ damage, and proinflammatory peptides (PIPs) are signaling peptides that can induce such responses. Many diseases have been redefined as inflammatory diseases. To identify PIPs more efficiently, we expanded the dataset and designed an ensemble learning model with manually encoded features. Specifically, we adopted a more comprehensive feature encoding method and considered the actual impact of certain features to filter them. Identification and prediction of PIPs were performed using an ensemble learning model based on five different classifiers. The results show that the model's sensitivity, specificity, accuracy, and Matthews correlation coefficient are all higher than those of the state-of-the-art models. We named this model MultiFeatVotPIP, and both the model and the data can be accessed publicly at https://github.com/ChaoruiYan019/MultiFeatVotPIP. Additionally, we have developed a user-friendly web interface for users, which can be accessed at http://www.bioai-lab.com/MultiFeatVotPIP.

Machine learning for antimicrobial peptide identification and design

Artificial intelligence (AI) and machine learning (ML) models are being deployed in many domains of society and have recently reached the field of drug discovery. Given the increasing prevalence of antimicrobial resistance, as well as the challenges intrinsic to antibiotic development, there is an urgent need to accelerate the design of new antimicrobial therapies. Antimicrobial peptides (AMPs) are therapeutic agents for treating bacterial infections, but their translation into the clinic has been slow owing to toxicity, poor stability, limited cellular penetration and high cost, among other issues. Recent advances in AI and ML have led to breakthroughs in our abilities to predict biomolecular properties and structures and to generate new molecules. The ML-based modelling of peptides may overcome some of the disadvantages associated with traditional drug discovery and aid the rapid development and translation of AMPs. Here, we provide an introduction to this emerging field and survey ML approaches that can be used to address issues currently hindering AMP development. We also outline important limitations that can be addressed for the broader adoption of AMPs in clinical practice, as well as new opportunities in data-driven peptide design.

Novel Antimicrobial Peptide SAAP Mutant as a Better Adjuvant to Sulbactam-Based Treatments Against Clinical Strains of XDR Acinetobacter baumannii

The production of extended spectrum β-lactamases (ESBLs) in extensively drug-resistant (XDR) strains of Acinetobacter baumannii has created havoc amongst clinicians making the treatment procedure challenging. Carbapenem-resistant strains have displayed total ineffectiveness towards newer combinations of β-lactam-β-lactamase inhibitors (βL-βLI) in tertiary healthcare settings. Therefore, the present study was aimed to design potential β-lactamase antimicrobial peptide (AMP) inhibitors against ESBLs produced by the strains. We have constructed an AMP mutant library with higher antimicrobial efficacy (range: ~ 15 to 27%) than their parent peptides. The mutants were thoroughly screened based on different physicochemical and immunogenic properties revealing three peptides, namely SAAP-148, HFIAP-1, myticalin-C6 and their mutants with safe pharmacokinetics profile. Molecular docking highlighted SAAP-148_M15 displaying maximum inhibitory potential with lowest binding energies against NDM1 (- 1148.7 kcal/mol), followed by OXA23 (- 1032.5 kcal/mol) and OXA58 (- 925.3 kcal/mol). The intermolecular interaction profiles displayed SAAP-148_M15 exhibiting hydrogen bonds and van der Waals hydrophobic interactions with the crucial residues of metallo β-lactamase [IPR001279] and penicillin-binding transpeptidase [IPR001460] domains. Coarse-grained clustering and molecular dynamics simulations (MDS) further validated the stable backbone profile and minimal residue-level fluctuations of the protein-peptide complex that were maintained throughout the simulation timeframe. The present study hypothesised that the combination of sulbactam (βL) with SAAP-148_M15 (βLI) holds immense potential in inhibiting the ESBLs alongside restoration of sulbactam activity. The current in silico findings upon further experimental validations can pave path towards designing of successful therapeutic strategy against XDR strains of A. baumannii.

Protein-based nanoparticles for therapeutic nucleic acid delivery

To realize the full potential of emerging nucleic acid therapies, there is a need for effective delivery agents to transport cargo to cells of interest. Protein materials exhibit several unique properties, including biodegradability, biocompatibility, ease of functionalization via recombinant and chemical modifications, among other features, which establish a promising basis for therapeutic nucleic acid delivery systems. In this review, we highlight progress made in the use of non-viral protein-based nanoparticles for nucleic acid delivery in vitro and in vivo, while elaborating on key physicochemical properties that have enabled the use of these materials for nanoparticle formulation and drug delivery. To conclude, we comment on the prospects and unresolved challenges associated with the translation of protein-based nucleic acid delivery systems for therapeutic applications.

Bioinformatics and bioactive peptides from foods: Do they work together?

We live in the Big Data Era which affects many aspects of science, including research on bioactive peptides derived from foods, which during the last few decades have been a focus of interest for scientists. These two issues, i.e., the development of computer technologies and progress in the discovery of novel peptides with health-beneficial properties, are closely interrelated. This Chapter presents the example applications of bioinformatics for studying biopeptides, focusing on main aspects of peptide analysis as the starting point, including: (i) the role of peptide databases; (ii) aspects of bioactivity prediction; (iii) simulation of peptide release from proteins. Bioinformatics can also be used for predicting other features of peptides, including ADMET, QSAR, structure, and taste. To answer the question asked "bioinformatics and bioactive peptides from foods: do they work together?", currently it is almost impossible to find examples of peptide research with no bioinformatics involved. However, theoretical predictions are not equivalent to experimental work and always require critical scrutiny. The aspects of compatibility of in silico and in vitro results are also summarized herein.

Design and characterization of a multi-epitope vaccine against Clostridium botulinum A3 Loch Maree intoxication in humans

Clostridium botulinum Loch Maree expresses an extremely potent botulinum neurotoxin subtype, A3 causing botulism and several gastrointestinal disorders in mammals. Several recombinant vaccines have been developed for human botulism and no vaccine is currently available for the treatment of diseases caused by other virulence factors. Hence, we designed, constructed, and characterized a multi-epitope vaccine from new virulence proteins identified from this organism using an immunoinformatics approach. The vaccine construct used in this study was designed from 6B cell linear epitopes, 12 cytotoxic T cell lymphocyte epitopes, and 15 helper T cell lymphocyte epitopes, with a defensin adjuvant and adjusting linker sequences. A molecular modeling approach was used to model, refine, and validate the 3D structure of the vaccine construct. Molecular docking studies were performed to determine the stability of the molecular interactions between the vaccine construct and human toll-like receptor 7. The in silico molecular cloning was used to clone a codon-optimized synthetic vaccine gene in pCYB1 vector and expressed in Escherichia coli. The results of this study identified six new virulence proteins: peptidoglycan hydrolase, SCP-like extracellular protein, N-acetylmuramoyl-l-alanine amidase, putative membrane protein, drug/metabolite exporter, and bacillolysin. The top B-cell, cytotoxic T-cell lymphocyte, and helper T-lymphocyte epitopes were predicted from these virulence proteins with greater accuracy and reliability. HLA-A*02:01 and HLA-A*03:01 were identified as HLA-A-binding alleles for cytotoxic T-cell lymphocyte epitopes. DRB1*0110 and DRB1*0115 are the dominant alleles that bind to helper T-cell lymphocyte epitopes. The synthetic gene construct was highly expressed in a heterologous host and produced considerable amounts of antigenic protein. The multi-epitope vaccine is more conservative in the sequence-structure-function link, immunogenic with less allergenicity, and possibly provokes cellular and humoral immunity. The present study suggests that the designed multi-epitope vaccine is a promising prophylactic candidate for the virulence and intoxication caused by subtype A3 strains.

Application of artificial intelligence approaches to predict the metabolism of xenobiotic molecules by human gut microbiome

A highly complex, diverse, and dense community of more than 1,000 different gut bacterial species constitutes the human gut microbiome that harbours vast metabolic capabilities encoded by more than 300,000 bacterial enzymes to metabolise complex polysaccharides, orally administered drugs/xenobiotics, nutraceuticals, or prebiotics. One of the implications of gut microbiome mediated biotransformation is the metabolism of xenobiotics such as medicinal drugs, which lead to alteration in their pharmacological properties, loss of drug efficacy, bioavailability, may generate toxic byproducts and sometimes also help in conversion of a prodrug into its active metabolite. Given the diversity of gut microbiome and the complex interplay of the metabolic enzymes and their diverse substrates, the traditional experimental methods have limited ability to identify the gut bacterial species involved in such biotransformation, and to study the bacterial species-metabolite interactions in gut. In this scenario, computational approaches such as machine learning-based tools presents unprecedented opportunities and ability to predict the gut bacteria and enzymes that can potentially metabolise a candidate drug. Here, we have reviewed the need to identify the gut microbiome-based metabolism of xenobiotics and have provided comprehensive information on the available methods, tools, and databases to address it along with their scope and limitations.

Lactiplantibacillus plantarum ZJ316-fermented milk ameliorates dextran sulfate sodium-induced chronic colitis by improving the inflammatory response and regulating intestinal microbiota

The pathogenesis of inflammatory bowel disease may be related to local inflammatory damage and disturbances in intestinal microecology. Probiotic therapy is a safe and effective therapeutic approach. Considering that fermented milk is accepted and enjoyed by many people as a daily dietary intervention strategy, its potential to alleviate dextran sulfate sodium (DSS)-induced chronic colitis in mice needs to be explored. In this study, we evaluated the therapeutic effects of Lactiplantibacillus plantarum ZJ316-fermented milk by establishing a mouse model of DSS-induced chronic colitis. The results showed that the disease severity and colonic lesions of inflammatory bowel disease were effectively alleviated by ingestion of fermented milk. At the same time, the expression of proinflammatory cytokines (TNF-α, IL-1β, and IL-6) effectively decreased, and the expression of antiinflammatory cytokines (IL-10) increased. Results based on 16S rRNA gene sequencing indicated that the structure and diversity of intestinal microorganisms changed markedly by intake of L. plantarum ZJ316-fermented milk, and fermented milk reduced the abundance of harmful bacteria (Helicobacter) while promoting the growth of beneficial bacteria (Faecalibacterium, Lactiplantibacillus, and Bifidobacterium). Additionally, the levels of short-chain fatty acids (acetic acid, propionic acid, butyric acid, pentanoic acid, and isobutyric acid) were also increased. In conclusion, the intake of L. plantarum ZJ316-fermented milk can alleviate chronic colitis by suppressing the inflammatory response and regulating intestinal microbiota.

Immunoreactive proteins of Capsicum-based spices as a threat to human health: mass spectrometry analysis and in silico mapping

Dietary patterns are changing severely, especially the consumption of highly processed foods with lots of spices is increasing, carrying an increased risk of immediate hypersensitivity (type I), in sensitised individuals, due to the possible presence of allergens, especially the hidden ones. Paprika is a fruit of the Capsicum genus, which belongs to the Solanaceae family and is commonly consumed fresh or as a spice. Despite recorded cases of anaphylaxis, its allergenicity has yet to be clearly investigated. In this study, we research to identify proteins that could trigger a severe allergic reaction in patients with an equivocal clinical picture. Two types of protein extracts extracted from 3 different paprika spices were immunoblotted with sera from patients with severe allergic symptoms, presumably to paprika. Proteins from the IgE reactive bands obtained were subjected to LC-MS/MS identification and then in silico analysis to assess their possible sensitising capacity and proinflammatory potential using online tools. The spices were shown to contain a number of incompletely investigated highly immunoreactive allergenic proteins, including proteins of foreign origin (contaminants), the presence of which can stimulate inflammatory mechanisms and cross-reactivity with other food allergens, which can threaten life and health and should be investigated in detail.

Targeting Peptides: The New Generation of Targeted Drug Delivery Systems

Peptides can act as targeting molecules, analogously to oligonucleotide aptamers and antibodies. They are particularly efficient in terms of production and stability in physiological environments; in recent years, they have been increasingly studied as targeting agents for several diseases, from tumors to central nervous system disorders, also thanks to the ability of some of them to cross the blood-brain barrier. In this review, we will describe the techniques employed for their experimental and in silico design, as well as their possible applications. We will also discuss advancements in their formulation and chemical modifications that make them even more stable and effective. Finally, we will discuss how their use could effectively help to overcome various physiological problems and improve existing treatments.

Immunoinformatics-guided design of a multi-valent vaccine against Rotavirus and Norovirus (ChRNV22)

Rotavirus (RV) and Norovirus (NV) are the main viral etiologic agents of acute gastroenteritis (AG), a serious pediatric condition associated with significant death rates and long-term complications. Anti-RV vaccination has been proved efficient in the reduction of severe AG worldwide, however, the available vaccines are all attenuated and have suboptimal efficiencies in developing countries, where AG leads to substantial disease burden. On the other hand, no NV vaccine has been licensed so far. Therefore, we used immunoinformatics tools to develop a multi-epitope vaccine (ChRNV22) to prevent severe AG by RV and NV. Epitopes were predicted against 17 prevalent genotypes of four structural proteins (NV's VP1, RV's VP4, VP6 and VP7), and then assembled in a chimeric protein, with two small adjuvant sequences (tetanus toxin P2 epitope and a conserved sequence of RV's enterotoxin, NSP4). Simulations of the immune response and interactions with immune receptors indicated the immunogenic properties of ChRNV22, including a Th1-biased response. In silico search for putative host-homologous, allergenic and toxic regions also indicated the vaccine safety. In summary, we developed a multi-epitope vaccine against different NV and RV genotypes that seems promising for the prevention of severe AG, which will be further assessed by in vivo tests.

Reverse vaccinology approaches to introduce promising immunogenic and drug targets against antibiotic-resistant Neisseria gonorrhoeae: Thinking outside the box in current prevention and treatment

Gonorrhea is an urgent antimicrobial resistance threat and its therapeutic options are continuously getting restricted. Moreover, no vaccine has been approved against it so far. Hence, the present study aimed to introduce novel immunogenic and drug targets against antibiotic-resistant Neisseria gonorrhoeae strains. In the first step, the core proteins of 79 complete genomes of N. gonorrhoeae were retrieved. Next, the surface-exposed proteins were evaluated from different aspects such as antigenicity, allergenicity, conservancy, and B-cell and T-cell epitopes to introduce promising immunogenic candidates. Then, the interactions with human Toll-like receptors (TLR-1, 2, and 4), and immunoreactivity to elicit humoral and cellular immune responses were simulated. On the other hand, to identify novel broad-spectrum drug targets, the cytoplasmic and essential proteins were detected. Then, the N. gonorrhoeae metabolome-specific proteins were compared to the drug targets of the DrugBank, and novel drug targets were retrieved. Finally, the protein data bank (PDB) file availability and prevalence among the ESKAPE group and common sexually transmitted infection (STI) agents were assessed. Our analyses resulted in the recognition of ten novel and putative immunogenic targets including murein transglycosylase A, PBP1A, Opa, NlpD, Azurin, MtrE, RmpM, LptD, NspA, and TamA. Moreover, four potential and broad-spectrum drug targets were identified including UMP kinase, GlyQ, HU family DNA-binding protein, and IF-1. Some of the shortlisted immunogenic and drug targets have confirmed roles in adhesion, immune evasion, and antibiotic resistance that can induce bactericidal antibodies. Other immunogenic and drug targets might be associated with the virulence of N. gonorrhoeae as well. Thus, further experimental studies and site-directed mutations are recommended to investigate the role of potential vaccine and drug targets in the pathogenesis of N. gonorrhoeae.

Teleost Piscidins-In Silico Perspective of Natural Peptide Antibiotics from Marine Sources

Fish, like all other animals, are exposed to constant contact with microbes, both on their skin and on the surfaces of their respiratory and digestive systems. Fish have a system of non-specific immune responses that provides them with initial protection against infection and allows them to survive under normal conditions despite the presence of these potential invaders. However, fish are less protected against invading diseases than other marine vertebrates because their epidermal surface, composed primarily of living cells, lacks the keratinized skin that serves as an efficient natural barrier in other marine vertebrates. Antimicrobial peptides (AMPs) are one type of innate immune protection present in all life forms. AMPs have been shown to have a broader range of biological effects than conventional antibiotics, including antibacterial, antiviral, antiprotozoal, and antifungal effects. Although other AMPs, such as defensins and hepcidins, are found in all vertebrates and are relatively well conserved, piscidins are found exclusively in Teleost fish and are not found in any other animal. Therefore, there is less information on the expression and bioactivity of piscidins than on other AMPs. Piscidins are highly effective against Gram-positive and Gram-negative bacteria that cause disease in fish and humans and have the potential to be used as pharmacological anti-infectives in biomedicine and aquaculture. To better understand the potential benefits and limitations of using these peptides as therapeutic agents, we are conducting a comprehensive study of the Teleost piscidins included in the "reviewed" category of the UniProt database using bioinformatics tools. They all have amphipathic alpha-helical structures. The amphipathic architecture of piscidin peptides and positively charged residues influence their antibacterial activity. These alpha-helices are intriguing antimicrobial drugs due to their stability in high-salt and metal environments. New treatments for multidrug-resistant bacteria, cancer, and inflammation may be inspired by piscidin peptides.

A web server for predicting and scanning of IL-5 inducing peptides using alignment-free and alignment-based method

Interleukin-5 (IL-5) can act as an enticing therapeutic target due to its pivotal role in several eosinophil-mediated diseases. The aim of this study is to develop a model for predicting IL-5 inducing antigenic regions in a protein with high precision. All models in this study have been trained, tested and validated on experimentally validated 1907 IL-5 inducing and 7759 non-IL-5 inducing peptides obtained from IEDB. Our primary analysis indicates that IL-5 inducing peptides are dominated by certain residues like Ile, Asn, and Tyr. It was also observed that binders of a wide range of HLA alleles can induce IL-5. Initially, alignment-based methods have been developed using similarity and motif search. These alignment-based methods provide high precision but poor coverage. In order to overcome this limitation, we explore alignment-free methods which are mainly machine learning-based models. Firstly, models have been developed using binary profiles and eXtreme Gradient Boosting-based model achieved a maximum AUC of 0.59. Secondly, composition-based models have been developed and our dipeptide-based random forest model achieved a maximum AUC of 0.74. Thirdly, random forest model developed using selected 250 dipeptides and achieved AUC 0.75 and MCC 0.29 on validation dataset; best among alignment-free models. In order to improve the performance, we developed an ensemble or hybrid method that combined alignment-based and alignment-free methods. Our hybrid method achieved AUC 0.94 with MCC 0.60 on a validation/independent dataset. The best hybrid model developed in this study has been incorporated into the user-friendly web server and a standalone package named 'IL5pred' (https://webs.iiitd.edu.in/raghava/il5pred/).

Immunoinformatics Vaccine Design for Zika Virus

Zika virus (ZIKV) is an emerging virus from the Flaviviridae family and Flavivirus genus that has caused important outbreaks around the world. ZIKV infection is associated with severe neuropathology in newborns and adults. Until now, there is no licensed vaccine available for ZIKV infection. Therefore, the development of a safe and effective vaccine against ZIKV is an urgent need. Recently, we designed an in silico multi-epitope vaccine for ZIKV based on immunoinformatics tools. To construct this in silico ZIKV vaccine, we used a consensus sequence generated from ZIKV sequences available in databank. Then, we selected CD4+ and CD8+ T cell epitopes from all ZIKV proteins based on the binding prediction to class II and class I human leukocyte antigen (HLA) molecules, promiscuity, and immunogenicity. ZIKV Envelope protein domain III (EDIII) was added to the construct and B cell epitopes were identified. Adjuvants were associated to increase immunogenicity. Distinct linkers were used for connecting the CD4+ and CD8+ T cell epitopes, EDIII, and adjuvants. Several analyses, such as antigenicity, population coverage, allergenicity, autoimmunity, and secondary and tertiary structures of the vaccine, were evaluated using various immunoinformatics tools and online web servers. In this chapter, we present the protocols with the rationale and detailed steps needed for this in silico multi-epitope ZIKV vaccine design.

Not all T cell epitopes are equally desired: a review of in silico tools for the prediction of cytokine-inducing potential of T-cell epitopes

Assessment of protective or harmful T cell response induced by any antigenic epitope is important in designing any immunotherapeutic molecule. The understanding of cytokine induction potential also helps us to monitor antigen-specific cellular immune responses and rational vaccine design. The classical immunoinformatics tools served well for prediction of B cell and T cell epitopes. However, in the last decade, the prediction algorithms for T cell epitope inducing specific cytokines have also been developed and appreciated in the scientific community. This review summarizes the current status of such tools, their applications, background algorithms, their use in experimental setup and functionalities available in the tools/web servers.

In silico designing of a recombinant multi-epitope antigen for leprosy diagnosis

BACKGROUND

Leprosy is caused by Mycobacterium leprae and Mycobacterium lepromatosis. Most of the affected population lives in low-income countries and may take up to 10 years to show any clinical signs, which is how physicians diagnose it. However, due to progressive cell damage, early diagnosis is very important. The best way to confirm leprosy is through bacilloscopic, which only confirms the diagnosis and has low accuracy or PCR, that requires specialized operators and is expensive. Since the bacteria are fastidious and do not grow in any culture media, therefore, diagnosing leprosy in the lab is still a challenge. In this concern, a recombinant multi-epitope protein can be a beneficial strategy in the management of the diagnosis, as diverse immunogenic epitopes are precisely selected to detect specific antibodies. Therefore, the purposes of the present study were to select immunogenic epitopes from different relevant proteins, with immunogenic properties, and then to construct a recombinant multi-epitope protein that accuses the presence of the antibodies in the early stages of the disease, making it more than appropriate to be applied as a diagnostic tool.

RESULTS

We selected 22 common proteins from both species and, using bioinformatics tools, predicted B and T cell epitopes. After multiple filtering and analyzing, we ended up with 29 epitopes {MHC-I (total 18) and MHC-II (total 11)} from 10 proteins, which were then merged into one construct. Its secondary and tertiary structures were also predicted and refined to comprise the amino acid residues in the best conformation possible. The multi-epitope protein construct was stable, non-host homologous, non-allergic, non-toxic, and elicit humoral and cellular responses. It has conformational B cell epitopes and potential to elicit IFN-γ, IL-4, and IL-10 secretion.

CONCLUSIONS

This novel recombinant multi-epitope protein constructed using the common epitopes from M. leprae and M. lepromatosis has a huge immunological potential, is stable, and can be lyophilized to be used in ELISA plates or even in biosensors, which are user-friendly diagnosis tools, facilitating translation into human sample tests.

MPMABP: A CNN and Bi-LSTM-Based Method for Predicting Multi-Activities of Bioactive Peptides

Bioactive peptides are typically small functional peptides with 2-20 amino acid residues and play versatile roles in metabolic and biological processes. Bioactive peptides are multi-functional, so it is vastly challenging to accurately detect all their functions simultaneously. We proposed a convolution neural network (CNN) and bi-directional long short-term memory (Bi-LSTM)-based deep learning method (called MPMABP) for recognizing multi-activities of bioactive peptides. The MPMABP stacked five CNNs at different scales, and used the residual network to preserve the information from loss. The empirical results showed that the MPMABP is superior to the state-of-the-art methods. Analysis on the distribution of amino acids indicated that the lysine preferred to appear in the anti-cancer peptide, the leucine in the anti-diabetic peptide, and the proline in the anti-hypertensive peptide. The method and analysis are beneficial to recognize multi-activities of bioactive peptides.

Immunoinformatics Approach Toward the Introduction of a Novel Multi-Epitope Vaccine Against Clostridium difficile

Clostridium difficile (C.difficile) is an exclusively anaerobic, spore-forming, and Gram-positive pathogen that is the most common cause of nosocomial diarrhea and is becoming increasingly prevalent in the community. Because C. difficile is strictly anaerobic, spores that can survive for months in the external environment contribute to the persistence and diffusion of C. difficile within the healthcare environment and community. Antimicrobial therapy disrupts the natural intestinal flora, allowing spores to develop into propagules that colonize the colon and produce toxins, thus leading to antibiotic-associated diarrhea and pseudomembranous enteritis. However, there is no licensed vaccine to prevent Clostridium difficile infection (CDI). In this study, a multi-epitope vaccine was designed using modern computer methods. Two target proteins, CdeC, affecting spore germination, and fliD, affecting propagule colonization, were chosen to construct the vaccine so that it could simultaneously induce the immune response against two different forms (spore and propagule) of C. difficile. We obtained the protein sequences from the National Center for Biotechnology Information (NCBI) database. After the layers of filtration, 5 cytotoxic T-cell lymphocyte (CTL) epitopes, 5 helper T lymphocyte (HTL) epitopes, and 7 B-cell linear epitopes were finally selected for vaccine construction. Then, to enhance the immunogenicity of the designed vaccine, an adjuvant was added to construct the vaccine. The Prabi and RaptorX servers were used to predict the vaccine's two- and three-dimensional (3D) structures, respectively. Additionally, we refined and validated the structures of the vaccine construct. Molecular docking and molecular dynamics (MD) simulation were performed to check the interaction model of the vaccine-Toll-like receptor (TLR) complexes, vaccine-major histocompatibility complex (MHC) complexes, and vaccine-B-cell receptor (BCR) complex. Furthermore, immune stimulation, population coverage, and in silico molecular cloning were also conducted. The foregoing findings suggest that the final formulated vaccine is promising against the pathogen, but more researchers are needed to verify it.

Harnessing Fuzzy Rule Based System for Screening Major Histocompatibility Complex Class I Peptide Epitopes from the Whole Proteome: An Implementation on the Proteome of Leishmania donovani

The development of peptide-based vaccines is enhanced by immunoinformatics, which predicts the patterns that B cells and T cells recognize. Although several tools are available for predicting the Major histocompatibility complex (MHC-I) binding peptides, the wide variants of human leucocyte antigen allele make it challenging to choose a peptide that will induce an immune response in a majority of people. In addition, for a peptide to be considered a potential vaccine candidate, factors such as T cell affinity, proteasome cleavage, and similarity to human proteins also play a major role. Identifying peptides that satisfy the earlier cited measures across the entire proteome is, therefore, challenging. Hence, the fuzzy inference system (FIS) is proposed to detect each peptide's potential as a vaccine candidate and assign it either a very high, high, moderate, or low ranking. The FIS includes input features from 6 modules (binding of 27 major alleles, T cell propensity, pro-inflammatory response, proteasome cleavage, transporter associated with antigen processing, and similarity with human peptide) and rules derived from an observation of features on positive samples. On validation of experimentally verified peptides, a balanced accuracy of ∼80% was achieved, with a Mathew's correlation coefficient score of 0.67 and an F-1 score of 0.74. In addition, the method was implemented on complete proteome of Leishmania donovani, which contains ∼4,800,000 peptides. Lastly, a searchable database of the ranked results of the L. donovani proteome was made and is available online (MHC-FIS-LdDB). It is hoped that this method will simplify the identification of potential MHC-I binding candidates from a large proteome.

Comparative analysis of machine learning-based approaches for identifying therapeutic peptides targeting SARS-CoV-2

Coronavirus disease 2019 (COVID-19) has impacted public health as well as societal and economic well-being. In the last two decades, various prediction algorithms and tools have been developed for predicting antiviral peptides (AVPs). The current COVID-19 pandemic has underscored the need to develop more efficient and accurate machine learning (ML)-based prediction algorithms for the rapid identification of therapeutic peptides against severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2). Several peptide-based ML approaches, including anti-coronavirus peptides (ACVPs), IL-6 inducing epitopes and other epitopes targeting SARS-CoV-2, have been implemented in COVID-19 therapeutics. Owing to the growing interest in the COVID-19 field, it is crucial to systematically compare the existing ML algorithms based on their performances. Accordingly, we comprehensively evaluated the state-of-the-art IL-6 and AVP predictors against coronaviruses in terms of core algorithms, feature encoding schemes, performance evaluation metrics and software usability. A comprehensive performance assessment was then conducted to evaluate the robustness and scalability of the existing predictors using well-constructed independent validation datasets. Additionally, we discussed the advantages and disadvantages of the existing methods, providing useful insights into the development of novel computational tools for characterizing and identifying epitopes or ACVPs. The insights gained from this review are anticipated to provide critical guidance to the scientific community in the rapid design and development of accurate and efficient next-generation in silico tools against SARS-CoV-2.

In silico construction of a multiepitope Zika virus vaccine using immunoinformatics tools

Zika virus (ZIKV) is an arbovirus from the Flaviviridae family and Flavivirus genus. Neurological events have been associated with ZIKV-infected individuals, such as Guillain-Barré syndrome, an autoimmune acute neuropathy that causes nerve demyelination and can induce paralysis. With the increase of ZIKV infection incidence in 2015, malformation and microcephaly cases in newborns have grown considerably, which suggested congenital transmission. Therefore, the development of an effective vaccine against ZIKV became an urgent need. Live attenuated vaccines present some theoretical risks for administration in pregnant women. Thus, we developed an in silico multiepitope vaccine against ZIKV. All structural and non-structural proteins were investigated using immunoinformatics tools designed for the prediction of CD4 + and CD8 + T cell epitopes. We selected 13 CD8 + and 12 CD4 + T cell epitopes considering parameters such as binding affinity to HLA class I and II molecules, promiscuity based on the number of different HLA alleles that bind to the epitopes, and immunogenicity. ZIKV Envelope protein domain III (EDIII) was added to the vaccine construct, creating a hybrid protein domain-multiepitope vaccine. Three high scoring continuous and two discontinuous B cell epitopes were found in EDIII. Aiming to increase the candidate vaccine antigenicity even further, we tested secondary and tertiary structures and physicochemical parameters of the vaccine conjugated to four different protein adjuvants: flagellin, 50S ribosomal protein L7/L12, heparin-binding hemagglutinin, or RS09 synthetic peptide. The addition of the flagellin adjuvant increased the vaccine's predicted antigenicity. In silico predictions revealed that the protein is a probable antigen, non-allergenic and predicted to be stable. The vaccine’s average population coverage is estimated to be 87.86%, which indicates it can be administered worldwide. Peripheral Blood Mononuclear Cells (PBMC) of individuals with previous ZIKV infection were tested for cytokine production in response to the pool of CD4 and CD8 ZIKV peptide selected. CD4 + and CD8 + T cells showed significant production of IFN-γ upon stimulation and IL-2 production was also detected by CD8 + T cells, which indicated the potential of our peptides to be recognized by specific T cells and induce immune response. In conclusion, we developed an in silico universal vaccine predicted to induce broad and high-coverage cellular and humoral immune responses against ZIKV, which can be a good candidate for posterior in vivo validation.

Revisiting the Principles of Designing a Vaccine

Immune principles formulated by Jenner, Pasteur, and early immunologists served as fundamental propositions for vaccine discovery against many dreadful pathogens. However, decisive success in the form of an efficacious vaccine still eludes for diseases such as tuberculosis, leishmaniasis, and trypanosomiasis. Several antileishmanial vaccine trials have been undertaken in past decades incorporating live, attenuated, killed, or subunit vaccination, but the goal remains unmet. In light of the above facts, we have to reassess the principles of vaccination by dissecting factors associated with the hosts' immune response. This chapter discusses the pathogen-associated perturbations at various junctures during the generation of the immune response which inhibits antigenic processing, presentation, or remodels memory T cell repertoire. This can lead to ineffective priming or inappropriate activation of memory T cells during challenge infection. Thus, despite a protective primary response, vaccine failure can occur due to altered immune environments in the presence of pathogens.

In silico tools and databases for designing cancer immunotherapy

Immunotherapy is a rapidly growing therapy for cancer which have numerous benefits over conventional treatments like surgery, chemotherapy, and radiation. Overall survival of cancer patients has improved significantly due to the use of immunotherapy. It acts as a novel pillar for treating different malignancies from their primary to the metastatic stage. Recent preferments in high-throughput sequencing and computational immunology leads to the development of targeted immunotherapy for precision oncology. In the last few decades, several computational methods and resources have been developed for designing immunotherapy against cancer. In this review, we have summarized cancer-associated genomic, transcriptomic, and mutation profile repositories. We have also enlisted in silico methods for the prediction of vaccine candidates, HLA binders, cytokines inducing peptides, and potential neoepitopes. Of note, we have incorporated the most important bioinformatics pipelines and resources for the designing of cancer immunotherapy. Moreover, to facilitate the scientific community, we have developed a web portal entitled ImmCancer (https://webs.iiitd.edu.in/raghava/immcancer/), comprises cancer immunotherapy tools and repositories.

EpiCurator: an immunoinformatic workflow to predict and prioritize SARS-CoV-2 epitopes

The ongoing coronavirus 2019 (COVID-19) pandemic, triggered by the emerging SARS-CoV-2 virus, represents a global public health challenge. Therefore, the development of effective vaccines is an urgent need to prevent and control virus spread. One of the vaccine production strategies uses the in silico epitope prediction from the virus genome by immunoinformatic approaches, which assist in selecting candidate epitopes for in vitro and clinical trials research. This study introduces the EpiCurator workflow to predict and prioritize epitopes from SARS-CoV-2 genomes by combining a series of computational filtering tools. To validate the workflow effectiveness, SARS-CoV-2 genomes retrieved from the GISAID database were analyzed. We identified 11 epitopes in the receptor-binding domain (RBD) of Spike glycoprotein, an important antigenic determinant, not previously described in the literature or published on the Immune Epitope Database (IEDB). Interestingly, these epitopes have a combination of important properties: recognized in sequences of the current variants of concern, present high antigenicity, conservancy, and broad population coverage. The RBD epitopes were the source for a multi-epitope design to in silico validation of their immunogenic potential. The multi-epitope overall quality was computationally validated, endorsing its efficiency to trigger an effective immune response since it has stability, high antigenicity and strong interactions with Toll-Like Receptors (TLR). Taken together, the findings in the current study demonstrated the efficacy of the workflow for epitopes discovery, providing target candidates for immunogen development.

Systems Biology to Understand and Regulate Human Retroviral Proinflammatory Response

The majority of human genome are non-coding genes. Recent research have revealed that about half of these genome sequences make up of transposable elements (TEs). A branch of these belong to the endogenous retroviruses (ERVs), which are germline viral infection that occurred over millions of years ago. They are generally harmless as evolutionary mutations have made them unable to produce viral agents and are mostly epigenetically silenced. Nevertheless, ERVs are able to express by still unknown mechanisms and recent evidences have shown links between ERVs and major proinflammatory diseases and cancers. The major challenge is to elucidate a detailed mechanistic understanding between them, so that novel therapeutic approaches can be explored. Here, we provide a brief overview of TEs, human ERVs and their links to microbiome, innate immune response, proinflammatory diseases and cancer. Finally, we recommend the employment of systems biology approaches for future HERV research.

AIEpred: An Ensemble Predictive Model of Classifier Chain to Identify Anti-Inflammatory Peptides

Anti-inflammatory peptides (AIEs) have recently emerged as promising therapeutic agent for treatment of various inflammatory diseases, such as rheumatoid arthritis and Alzheimer's disease. Therefore, detecting the correlation between amino acid sequence and its anti-inflammatory property is of great importance for the discovery of new AIEs. To address this issue, we propose a novel prediction tool for accurate identification of peptides as anti-inflammatory epitopes or non anti-inflammatory epitopes. Most of all, we encode the original peptide sequence for better mining and exploring the information and patterns, based on the three feature representations as amino acid contact, position specific scoring matrix, physicochemical property. At the same time, we exploit several feature extraction models and utilize one feature selection model, in order to construct many base classifiers from various feature representations. More specifically, we develop an effective classification model, with which we can extract and learn a set of informative features from the ensemble classifier chain model with different group of base classifiers. Furthermore, in order to test the predictive power of our model, we conduct the comparative experiments on the leave-one-out cross-validation and the independent test. It shows that our novel predictor performs great accurate for identification of AIEs as well as existing outstanding prediction tools. Source codes are available at https://github.com/guofei-tju/Ensemble-classifier-chain-model.

In silico designing of vaccine candidate against Clostridium difficile

Clostridium difficile is a spore-forming gram-positive bacterium, recognized as the primary cause of antibiotic-associated nosocomial diarrhoea. Clostridium difficile infection (CDI) has emerged as a major health-associated infection with increased incidence and hospitalization over the years with high mortality rates. Contamination and infection occur after ingestion of vegetative spores, which germinate in the gastro-intestinal tract. The surface layer protein and flagellar proteins are responsible for the bacterial colonization while the spore coat protein, is associated with spore colonization. Both these factors are the main concern of the recurrence of CDI in hospitalized patients. In this study, the CotE, SlpA and FliC proteins are chosen to form a multivalent, multi-epitopic, chimeric vaccine candidate using the immunoinformatics approach. The overall reliability of the candidate vaccine was validated in silico and the molecular dynamics simulation verified the stability of the vaccine designed. Docking studies showed stable vaccine interactions with Toll‐Like Receptors of innate immune cells and MHC receptors. In silico codon optimization of the vaccine and its insertion in the cloning vector indicates a competent expression of the modelled vaccine in E. coli expression system. An in silico immune simulation system evaluated the effectiveness of the candidate vaccine to trigger a protective immune response.

Unveiling Putative Functions of Mucus Proteins and Their Tryptic Peptides in Seven Gastropod Species Using Comparative Proteomics and Machine Learning-Based Bioinformatics Predictions

Gastropods are among the most diverse animals. Gastropod mucus contains several glycoproteins and peptides that vary by species and habitat. Some bioactive peptides from gastropod mucus were identified only in a few species. Therefore, using biochemical, mass spectrometric, and bioinformatics approaches, this study aimed to comprehensively identify putative bioactive peptides from the mucus proteomes of seven commonly found or commercially valuable gastropods. The mucus was collected in triplicate samples, and the proteins were separated by 1D-SDS-PAGE before tryptic digestion and peptide identification by nano LC-MS/MS. The mucus peptides were subsequently compared with R scripts. A total of 2818 different peptides constituting 1634 proteins from the mucus samples were identified, and 1218 of these peptides (43%) were core peptides found in the mucus of all examined species. Clustering and correspondence analyses of 1600 variable peptides showed unique mucous peptide patterns for each species. The high-throughput k-nearest neighbor and random forest-based prediction programs were developed with more than 95% averaged accuracy and could identify 11 functional categories of putative bioactive peptides and 268 peptides (9.5%) with at least five to seven bioactive properties. Antihypertensive, drug-delivering, and antiparasitic peptides were predominant. These peptides provide an understanding of gastropod mucus, and the putative bioactive peptides are expected to be experimentally validated for further medical, pharmaceutical, and cosmetic applications.

In silico Approaches for the Design and Optimization of Interfering Peptides Against Protein-Protein Interactions

Large contact surfaces of protein-protein interactions (PPIs) remain to be an ongoing issue in the discovery and design of small molecule modulators. Peptides are intrinsically capable of exploring larger surfaces, stable, and bioavailable, and therefore bear a high therapeutic value in the treatment of various diseases, including cancer, infectious diseases, and neurodegenerative diseases. Given these promising properties, a long way has been covered in the field of targeting PPIs via peptide design strategies. In silico tools have recently become an inevitable approach for the design and optimization of these interfering peptides. Various algorithms have been developed to scrutinize the PPI interfaces. Moreover, different databases and software tools have been created to predict the peptide structures and their interactions with target protein complexes. High-throughput screening of large peptide libraries against PPIs; "hotspot" identification; structure-based and off-structure approaches of peptide design; 3D peptide modeling; peptide optimization strategies like cyclization; and peptide binding energy evaluation are among the capabilities of in silico tools. In the present study, the most recent advances in the field of in silico approaches for the design of interfering peptides against PPIs will be reviewed. The future perspective of the field and its advantages and limitations will also be pinpointed.

Convolutional neural networks with image representation of amino acid sequences for protein function prediction

Proteins are one of the most important molecules that govern the cellular processes in most of the living organisms. Various functions of the proteins are of paramount importance to understand the basics of life. Several supervised learning approaches are applied in this field to predict the functionality of proteins. In this paper, we propose a convolutional neural network based approach ProtConv to predict the functionality of proteins by converting the amino-acid sequences to a two dimensional image. We have used a protein embedding technique using transfer learning to generate the feature vector. Feature vector is then converted into a square sized single channel image to be fed into a convolutional network. The neural network architecture used here is a combination of convolutional filters and average pooling layers followed by dense fully connected layers to predict a binary function. We have performed experiments on standard benchmark datasets taken from two very important protein function prediction task: proinflammatory cytokines and anticancer peptides. Our experiments show that the proposed method, ProtConv achieves state-of-the-art performances on both of the datasets. All necessary details about implementation with source code and datasets are made available at: https://github.com/swakkhar/ProtConv.

Computer-aided prediction and design of IL-6 inducing peptides: IL-6 plays a crucial role in COVID-19

Interleukin 6 (IL-6) is a pro-inflammatory cytokine that stimulates acute phase responses, hematopoiesis and specific immune reactions. Recently, it was found that the IL-6 plays a vital role in the progression of COVID-19, which is responsible for the high mortality rate. In order to facilitate the scientific community to fight against COVID-19, we have developed a method for predicting IL-6 inducing peptides/epitopes. The models were trained and tested on experimentally validated 365 IL-6 inducing and 2991 non-inducing peptides extracted from the immune epitope database. Initially, 9149 features of each peptide were computed using Pfeature, which were reduced to 186 features using the SVC-L1 technique. These features were ranked based on their classification ability, and the top 10 features were used for developing prediction models. A wide range of machine learning techniques has been deployed to develop models. Random Forest-based model achieves a maximum AUROC of 0.84 and 0.83 on training and independent validation dataset, respectively. We have also identified IL-6 inducing peptides in different proteins of SARS-CoV-2, using our best models to design vaccine against COVID-19. A web server named as IL-6Pred and a standalone package has been developed for predicting, designing and screening of IL-6 inducing peptides (https://webs.iiitd.edu.in/raghava/il6pred/).

The complete genome and comparative analysis of the phage phiC120 infecting multidrug-resistant Escherichia coli and Salmonella strains

Phages infecting Salmonella and Escherichia coli are promising agents for therapeutics and biological control of these foodborne pathogens, in particular those strains with resistance to several antibiotics. In an effort to assess the potential of the phage phiC120, a virulent phage isolated from horse feces in Mexico, we characterized its morphology, host range and complete genome. Herein, we showed that phiC120 possesses strong lytic activity against several multidrug-resistant E. coli O157: H7 and Salmonella strains, and its morphology indicated that is a member of Myoviridae family. The phiC120 genome is double-stranded DNA and consists of 186,570 bp in length with a 37.6% G + C content. A total of 281 putative open reading frames (ORFs) and two tRNAs were found, where 150 ORFs encoded hypothetical proteins with unknown function. Comparative analysis showed that phiC120 shared high similarity at nucleotide and protein levels with coliphages RB69 and phiE142. Detailed phiC120 analysis revealed that ORF 94 encodes a putative depolymerase, meanwhile genes encoding factors associated with lysogeny, toxins, and antibiotic resistance were absent; however, ORF 95 encodes a putative protein with potential allergenic and pro-inflammatory properties, making needed further studies to guarantee the safety of phiC120 for human use. The characterization of phiC120 expands our knowledge about the biology of coliphages and provides novel insights supporting its potential for the development of phage-based applications to control unwanted bacteria.

Immunoinformatic construction of an adenovirus-based modular vaccine platform and its application in the design of a SARS-CoV-2 vaccine

The current SARS-CoV-2 pandemic has imposed new challenges and demands for health systems, especially in the development of new vaccine strategies. Vaccines for many pathogens were developed based on the display of foreign epitopes in the variable regions of the human adenovirus (HAdV) major capsid proteins (hexon, penton and fiber). The humoral immune response against the HAdV major capsid proteins was demonstrated to play a role in the development of an immune response against the epitopes in display. Through the immunoinformatic profiling of the major capsid proteins of HAdVs from different species, we developed a modular concept that can be used in the development of vaccines based on HAdV vectors. Our data suggests that different immunomodulatory potentials can be observed in the conserved regions, present in the hexon and penton proteins, from different species. Using this modular approach, we developed a HAdV-5 based vaccine strategy for SARS-CoV-2, constructed through the display of SARS-CoV-2 epitopes indicated by our prediction analysis as immunologically relevant. The sequences of the HAdV vector major capsid proteins were also edited to enhance the IFN-gamma induction and antigen presenting cells activation. This is the first study proposing a modular HAdV platform developed to aid the design of new vaccines by inducing an immune response more suited for the epitopes in display.

Taenia solium proteins: a beautiful kaleidoscope of pro and anti-inflammatory antigens

Background: Neurocysticercosis (NCC) is an acquired infection of central nervous system associated with epileptic seizures. The parasite 'Taenia solium' causes this disease and has a complex life cycle and molts into various stages that influence the host-parasite interaction. The disease has a long asymptomatic phase with viable cyst and degeneration of cyst and leaking cyst fluid has been associated with symptomatic phase. The parasite proteome holds the answers and clues to this complex clinical presentation and hence unraveling of proteome of parasite antigens is needed for better understanding of host-parasite interactions. Objective: To understand the proteome make-up of T. solium cyst vesicular fluid (VF) and excretory secretory proteins (ESPs). Methodology: The VF and ESPs for the study were prepared from cyst harvested from naturally infected swine. The samples were prepared for nano LC-MS by in-tube digestion of proteins. The spectra obtained were annotated and enrichment analysis was performed and in silico analysis was done. Results: T. solium VF and ESPs have 206 and 247 proteins of varied make-up including pro-inflammatory and anti-inflammatory nature. Conclusions: Due to varied make-up of VF and ESPs it can generate complex humoral and cellular immune response.

Considerations on the Rational Design of Covalently Conjugated Cell-Penetrating Peptides (CPPs) for Intracellular Delivery of Proteins: A Guide to CPP Selection Using Glucarpidase as the Model Cargo Molecule

Access of proteins to their intracellular targets is limited by a hydrophobic barrier called the cellular membrane. Conjugation with cell-penetrating peptides (CPPs) has been shown to improve protein transduction into the cells. This conjugation can be either covalent or non-covalent, each with its unique pros and cons. The CPP-protein covalent conjugation may result in undesirable structural and functional alterations in the target protein. Therefore, we propose a systematic approach to evaluate different CPPs for covalent conjugations. This guide is presented using the carboxypeptidase G2 (CPG2) enzyme as the target protein. Seventy CPPs -out of 1155- with the highest probability of uptake efficiency were selected. These peptides were then conjugated to the N- or C-terminus of CPG2. Translational efficacy of the conjugates, robustness and thermodynamic properties of the chimera, aggregation possibility, folding rate, backbone flexibility, and aspects of in vivo administration such as protease susceptibility were predicted. The effect of the position of conjugation was evaluated using unpaired t-test (p < 0.05). It was concluded that N-terminal conjugation resulted in higher quality constructs. Seventeen CPP-CPG2/CPG2-CPP constructs were identified as the most promising. Based on this study, the bioinformatics workflow that is presented may be universally applied to any CPP-protein conjugate design.

PIP-EL: A New Ensemble Learning Method for Improved Proinflammatory Peptide Predictions

Proinflammatory cytokines have the capacity to increase inflammatory reaction and play a central role in first line of defence against invading pathogens. Proinflammatory inducing peptides (PIPs) have been used as an antineoplastic agent, an antibacterial agent and a vaccine in immunization therapies. Due to the advancement in sequence technologies that resulted an avalanche of protein sequence data. Therefore, it is necessary to develop an automated computational method to enable fast and accurate identification of novel PIPs within the vast number of candidate proteins and peptides. To address this, we proposed a new predictor, PIP-EL, for predicting PIPs using the strategy of ensemble learning (EL). Our benchmarking dataset is imbalanced. Thus, we applied a random under-sampling technique to generate 10 balanced models for each composition. Technically, PIP-EL is the fusion of 50 independent random forest (RF) models, where each of the five different compositions, including amino acid, dipeptide, composition-transition-distribution, physicochemical properties, and amino acid index contains 10 RF models. PIP-EL achieves the Matthews' correlation coefficient (MCC) of 0.435 in a 5-fold cross-validation test, which is ~2-5% higher than that of the individual classifiers and hybrid feature-based classifier. Furthermore, we evaluate the performance of PIP-EL on the independent dataset, showing that our method outperforms the existing method and two different machine learning methods developed in this study, with an MCC of 0.454. These results indicate that PIP-EL will be a useful tool for predicting PIPs and for researchers working in the field of peptide therapeutics and immunotherapy. The user-friendly web server, PIP-EL, is freely accessible.

Bacteriophage cocktail for biocontrol of Escherichia coli O157:H7: Stability and potential allergenicity study

Escherichia coli O157:H7 has become a global public health and a food safety problem. Despite the implementation of control strategies that guarantee the safety in various products, outbreaks persist and new alternatives are necessary to reduce this pathogen along the food chain. Recently, our group isolated and characterised lytic bacteriophages against E. coli O157:H7 with potential to be used as biocontrol agents in food. To this end, phages need certain requirements to allow their manufacture and application. The aim of this study was to determine the physical stability and allergenic potential of free and microencapsulated (ME) bacteriophage cocktails against E. coli O157:H7. In vitro and in vivo studies were performed to determine phage survival under different pH, gastrointestinal conditions, temperature and UV light intensities. Results showed that the stability of ME phages was significantly (P<0.05) higher than free phages after ultraviolet irradiation, pH conditions between 3 to 7, and exposure to temperatures between at -80°C and 70°C. Both formulations were highly sensitive to very low pH in simulated gastric fluid, but stable in bile salts. In vivo studies in mice confirmed these phages passed through the gastrointestinal tract and were excreted in faeces. In silico, full-length alignment analysis showed that all phage proteins were negative for allergenic potential, but different predicting criteria classified seven phage proteins with a very low probability to be an allergen. In conclusion, these data demonstrated that microencapsulation provided a greater stability to phage formulation under stress conditions and assure a more suitable commercial formulation for the biological control of E. coli O157:H7.

Genome-Wide Analysis to Identify HLA Factors Potentially Associated With Severe Dengue

The pathogenesis of dengue hemorrhagic fever (DHF), following dengue virus (DENV) infection, is a complex and poorly understood phenomenon. In view of the clinical need of identifying patients with higher likelihood of developing this severe outcome, we undertook a comparative genome-wide association analysis of epitope variants from sequences available in the ViPR database that have been reported to be differentially related to dengue fever and DHF. Having enumerated the incriminated epitope variants, we determined the corresponding HLA alleles in the context of which DENV infection could potentially precipitate DHF. Our analysis considered the development of DHF in three different perspectives: (a) as a consequence of primary DENV infection, (b) following secondary DENV infection with a heterologous serotype, (c) as a result of DENV infection following infection with related flaviviruses like Zika virus, Japanese Encephalitis virus, West Nile virus, etc. Subject to experimental validation, these viral and host markers would be valuable in triaging DENV-infected patients for closer supervision owing to the relatively higher risk of poor prognostic outcome and also for the judicious allocation of scarce institutional resources during large outbreaks.

Prediction of anti-inflammatory proteins/peptides: an insilico approach

Background

The current therapy for inflammatory and autoimmune disorders involves the use of nonspecific anti-inflammatory drugs and other immunosuppressant, which are often accompanied with potential side effects. As an alternative therapy, anti-inflammatory peptides are recently being exploited as anti-inflammatory agents for treatment of various inflammatory diseases such as Alzheimer’s disease and rheumatoid arthritis. Thus, understanding the correlation between amino acid sequence and its potential anti-inflammatory property is of great importance for the discovery of novel and efficient anti-inflammatory peptide-based therapeutics.

Methods

In this study, we have developed a prediction tool for the classification of peptides as anti-inflammatory epitopes or non anti-inflammatory epitopes. The training was performed using experimentally validated epitopes obtained from Immune epitope database and analysis resource database. Different sequence-based features and their hybrids with motif information were employed for development of support vector machine-based machine learning models. Similarly, machine learning models were also constructed using random forest.

Results

The composition and terminal residue conservation analysis of peptides revealed the dominance of leucine, serine, tyrosine and arginine residues in anti-inflammatory epitopes as compared to non anti-inflammatory epitopes. Similarly, the anti-inflammatory epitopes specific motifs were found to be rich in hydrophobic and polar residues. The hybrid of tripeptide composition-based support vector machine model and motif yielded the best performance on 10-fold cross validation with an accuracy of 78.1% and MCC of 0.58. The same displayed an accuracy of 72% and MCC of 0.45 on validation dataset, rejecting any possibility of over-fitting. The tripeptide composition-based random forest model displayed an accuracy of 0.8 and MCC of 0.59 on 10-fold cross validation, however, the accuracy (0.68) and MCC (0.31) was lower as compared to support vector machine model on validation dataset. Thus, the support vector machine model is implemented as the default model and an additional option of using the random forest model is provided.

Conclusion

The prediction models along with tools for epitope mapping and similarity search have been provided as a web server which is freely accessible at http://metagenomics.iiserb.ac.in/antiinflam/.

Electronic supplementary material

The online version of this article (doi:10.1186/s12967-016-1103-6) contains supplementary material, which is available to authorized users.