ILeukin10Pred: A Computational Approach for Predicting IL-10-Inducing Immunosuppressive Peptides Using Combinations of Amino Acid Global Features

Immunoinformatic strategy for developing multi-epitope subunit vaccine against Helicobacter pylori

Helicobacter pylori is a gram-negative bacterium that persistently infects the human stomach, leading to peptic ulcers, gastritis, and an increased risk of gastric cancer. The extremophilic characteristics of this bacterium make it resistant to current drug treatments, and there are no licensed vaccines available against H. pylori. Computational approaches offer a viable alternative for designing antigenic, stable, and safe vaccines to control infections caused by this pathogen. In this study, we employed an immunoinformatic strategy to design a set of candidate multi-epitope subunit vaccines by combining the most potent B and T cell epitopes from three targeted antigenic proteins (BabA, CagA, and VacA). Out of the 12 hypothetical vaccines generated, two (HP_VaX_V1 and HP_VaX_V2) were found to be strongly immunogenic, non-allergenic, and structurally stable. The proposed vaccine candidates were evaluated based on population coverage, molecular docking, immune simulations, codon adaptation, secondary mRNA structure, and in silico cloning. The vaccine candidates exhibited antigenic scores of 1.19 and 1.01, with 93.5% and 90.4% of the most rama-favored regions, respectively. HP_VaX_V1 and HP_VaX_V2 exhibited the strongest binding affinity towards TLR-7 and TLR-8, as determined by molecular docking simulations (ΔG = -20.3 and -20.9, respectively). Afterward, multi-scale normal mode analysis simulation revealed the structural flexibility and stability of vaccine candidates. Additionally, immune simulations showed elevated levels of cell-mediated immunity, while repeated exposure simulations indicated rapid antigen clearance. Finally, in silico cloning was performed using the expression vector pET28a (+) with optimized restriction sites to develop a viable strategy for large-scale production of the chosen vaccine constructs. These analyses suggest that the proposed vaccines may elicit potent immune responses against H. pylori, but laboratory validation is needed to verify their safety and immunogenicity.

Computer-aided rational design of a mRNA vaccine against Guanarito mammarenavirus

PURPOSE

Guanarito mammarenavirus (GTOV) is a highly pathogenic virus that leads to Venezuelan hemorrhagic fever (VHF). Despite being a severe disease, there are currently no commercially available drugs or vaccines for its prevention.

METHODS

Here we computationally formulated a mRNA vaccine construct (VC) from the genome of GTOV to produce immunity against its infections. Two proteins, namely zinc-finger motif protein (NP_899220.1), and nucleocapsid protein (NP_899211.1) were screened as potential candidates for downstream analysis.

RESULTS

We determined the T and B cell epitopes of the candidate proteins. The resulting epitopes were analyzed, and the best epitopes were utilized in the formation of the peptide vaccine construct. The secondary and tertiary structures of the peptide construct were predicted and validated. Docking was conducted to check the binding energy of the designed peptide vaccine with the human immune receptors, namely TLR2 and TLR4. Our designed vaccine showed stable interactions with the HLA molecules, as verified through normal mode and MD simulation analysis. The immune simulation results indicated a positive immune response against the construct. A potentially stable mRNA vaccine was formulated by adding of sequences such as the Kozak, Goblin 5' UTR, tPA-signal peptide, MITD, 3' UTRs, and a poly(A) tail to the peptide vaccine construct. Lastly, the expression probability of the mRNA vaccine was confirmed in the expression system of E. coli strain K12.

CONCLUSION

The designed vaccine showed the potential to elicit an immune response against the GTOV infection; however, experimental validation is recommended to verify the in-silico findings of this study.

Peptide discovery across the spectrum of neuroinflammation; microglia and astrocyte phenotypical targeting, mediation, and mechanistic understanding

Uncontrolled and chronic inflammatory states in the Central Nervous System (CNS) are the hallmark of neurodegenerative pathology and every injury or stroke-related insult. The key mediators of these neuroinflammatory states are glial cells known as microglia, the resident immune cell at the core of the inflammatory event, and astroglia, which encapsulate inflammatory insults in proteoglycan-rich scar tissue. Since the majority of neuroinflammation is exclusively based on the responses of said glia, their phenotypes have been identified to be on an inflammatory spectrum encompassing developmental, homeostatic, and reparative behaviors as opposed to their ability to affect devastating cell death cascades and scar tissue formation. Recently, research groups have focused on peptide discovery to identify these phenotypes, find novel mechanisms, and mediate or re-engineer their actions. Peptides retain the diverse function of proteins but significantly reduce the activity dependence on delicate 3D structures. Several peptides targeting unique phenotypes of microglia and astroglia have been identified, along with several capable of mediating deleterious behaviors or promoting beneficial outcomes in the context of neuroinflammation. A comprehensive review of the peptides unique to microglia and astroglia will be provided along with their primary discovery methodologies, including top-down approaches using known biomolecules and naïve strategies using peptide and phage libraries.

StackIL10: A stacking ensemble model for the improved prediction of IL-10 inducing peptides

Interleukin-10, a highly effective cytokine recognized for its anti-inflammatory properties, plays a critical role in the immune system. In addition to its well-documented capacity to mitigate inflammation, IL-10 can unexpectedly demonstrate pro-inflammatory characteristics under specific circumstances. The presence of both aspects emphasizes the vital need to identify the IL-10-induced peptide. To mitigate the drawbacks of manual identification, which include its high cost, this study introduces StackIL10, an ensemble learning model based on stacking, to identify IL-10-inducing peptides in a precise and efficient manner. Ten Amino-acid-composition-based Feature Extraction approaches are considered. The StackIL10, stacking ensemble, the model with five optimized Machine Learning Algorithm (specifically LGBM, RF, SVM, Decision Tree, KNN) as the base learners and a Logistic Regression as the meta learner was constructed, and the identification rate reached 91.7%, MCC of 0.833 with 0.9078 Specificity. Experiments were conducted to examine the impact of various enhancement techniques on the correctness of IL-10 Prediction. These experiments included comparisons between single models and various combinations of stacking-based ensemble models. It was demonstrated that the model proposed in this study was more effective than singular models and produced satisfactory results, thereby improving the identification of peptides that induce IL-10.

A hybrid method for discovering interferon-gamma inducing peptides in human and mouse

Interferon-gamma (IFN-γ) is a versatile pleiotropic cytokine essential for both innate and adaptive immune responses. It exhibits both pro-inflammatory and anti-inflammatory properties, making it a promising therapeutic candidate for treating various infectious diseases and cancers. We present IFNepitope2, a host-specific technique to annotate IFN-γ inducing peptides, it is an updated version of IFNepitope introduced by Dhanda et al. In this study, dataset used for developing prediction method contain experimentally validated 25,492 and 7983 IFN-γ inducing peptides in human and mouse host, respectively. In initial phase, machine learning techniques have been exploited to develop classification model using wide range of peptide features. Further, to improve machine learning based models or alignment free models, we explore potential of similarity-based technique BLAST. Finally, a hybrid model has been developed that combine best machine learning based model with BLAST. In most of the case, models based on extra tree perform better than other machine learning techniques. In case of peptide features, compositional feature particularly dipeptide composition performs better than one-hot encoding or binary profile. Our best machine learning based models achieved AUROC 0.89 and 0.83 for human and mouse host, respectively. The hybrid model achieved the AUROC 0.90 and 0.85 for human and mouse host, respectively. All models have been evaluated on an independent/validation dataset not used for training or testing these models. Newly developed method performs better than existing method on independent dataset. The major objective of this study is to predict, design and scan IFN-γ inducing peptides, thus server/software have been developed ( https://webs.iiitd.edu.in/raghava/ifnepitope2/ ). This method is also available as standalone at https://github.com/raghavagps/ifnepitope2 and python package index at https://pypi.org/project/ifnepitope2/ .

AntiBP3: A Method for Predicting Antibacterial Peptides against Gram-Positive/Negative/Variable Bacteria

Most of the existing methods developed for predicting antibacterial peptides (ABPs) are mostly designed to target either gram-positive or gram-negative bacteria. In this study, we describe a method that allows us to predict ABPs against gram-positive, gram-negative, and gram-variable bacteria. Firstly, we developed an alignment-based approach using BLAST to identify ABPs and achieved poor sensitivity. Secondly, we employed a motif-based approach to predict ABPs and obtained high precision with low sensitivity. To address the issue of poor sensitivity, we developed alignment-free methods for predicting ABPs using machine/deep learning techniques. In the case of alignment-free methods, we utilized a wide range of peptide features that include different types of composition, binary profiles of terminal residues, and fastText word embedding. In this study, a five-fold cross-validation technique has been used to build machine/deep learning models on training datasets. These models were evaluated on an independent dataset with no common peptide between training and independent datasets. Our machine learning-based model developed using the amino acid binary profile of terminal residues achieved maximum AUC 0.93, 0.98, and 0.94 for gram-positive, gram-negative, and gram-variable bacteria, respectively, on an independent dataset. Our method performs better than existing methods when compared with existing approaches on an independent dataset. A user-friendly web server, standalone package and pip package have been developed to facilitate peptide-based therapeutics.

Deciphering the Immunogenicity of Monkeypox Proteins for Designing the Potential mRNA Vaccine

The Monkeypox virus (MPXV), an orthopox virus, is responsible for monkeypox in humans, a zoonotic disease similar to smallpox. This infection first appeared in the 1970s in humans and then in 2003, after which it kept on spreading all around the world. To date, various antivirals have been used to cure this disease, but now, MPXV has developed resistance against these, thus increasing the need for an alternative cure for this deadly disease. In this study, we devised a reverse vaccinology approach against MPXV using a messenger RNA (mRNA) vaccine by pinning down the antigenic proteins of this virus. By using bioinformatic tools, we predicted prospective immunogenic B and T lymphocyte epitopes. Based on cytokine inducibility score, nonallergenicity, nontoxicity, antigenicity, and conservancy, the final epitopes were selected. Our analysis revealed the stable structure of the mRNA vaccine and its efficient expression in host cells. Furthermore, strong interactions were demonstrated with toll-like receptors 2 (TLR2) and 4 (TLR4) according to the molecular dynamic simulation studies. The in silico immune simulation analyses revealed an overall increase in the immune responses following repeated exposure to the designed vaccine. Based on our findings, the vaccine candidate designed in this study has the potential to be tested as a promising novel mRNA therapeutic vaccine against MPXV infection.

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/).

C10Pred: A First Machine Learning Based Tool to Predict C10 Family Cysteine Peptidases Using Sequence-Derived Features

Streptococcus pyogenes, or group A Streptococcus (GAS), a gram-positive bacterium, is implicated in a wide range of clinical manifestations and life-threatening diseases. One of the key virulence factors of GAS is streptopain, a C10 family cysteine peptidase. Since its discovery, various homologs of streptopain have been reported from other bacterial species. With the increased affordability of sequencing, a significant increase in the number of potential C10 family-like sequences in the public databases is anticipated, posing a challenge in classifying such sequences. Sequence-similarity-based tools are the methods of choice to identify such streptopain-like sequences. However, these methods depend on some level of sequence similarity between the existing C10 family and the target sequences. Therefore, in this work, we propose a novel predictor, C10Pred, for the prediction of C10 peptidases using sequence-derived optimal features. C10Pred is a support vector machine (SVM) based model which is efficient in predicting C10 enzymes with an overall accuracy of 92.7% and Matthews’ correlation coefficient (MCC) value of 0.855 when tested on an independent dataset. We anticipate that C10Pred will serve as a handy tool to classify novel streptopain-like proteins belonging to the C10 family and offer essential information.