Computer-aided designing of immunosuppressive peptides based on IL-10 inducing potential

Nagpal, Gandharva; Usmani, Salman Sadullah; Dhanda, Sandeep Kumar; Kaur, Harpreet; Singh, Sandeep; Sharma, Meenu; Raghava, Gajendra P. S.

doi:10.1038/srep42851

Cited by 217 publications

(131 citation statements)

References 53 publications

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“…IL10pred (Nagpal et al 2017) servers, respectively. The IL4pred and IL10pred operations were carried out based on SVM method with threshold value 0.2 and -0.3, respectively.…”

Section: Prediction and Evaluation Of Helper T-lymphocyte (Htl) Epitomentioning

confidence: 99%

Structural Basis and Designing of Peptide Vaccine using PE-PGRS Family Protein of Mycobacterium ulcerans – An Integrated Vaccinomics Approach

Nain

Karim

Sen³

et al. 2019

Preprint

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Buruli ulcer is an emerging-necrotizing skin infection, responsible for permanent deformity if untreated, caused by the pathogen Mycobacterium ulcerans (M. ulcerans). Despite this debilitating condition, no specific disease-modifying therapeutics or vaccination is available. Therefore, we aimed to design an effective multi-epitope vaccine against M.ulcerans through an integrated vaccinomics approach. Briefly, the highest antigenic PE-PGRS protein was selected from which the promiscuous T-and B-cell epitopes were predicted. After rigorous assessment, 15 promising CTL, HTL and LBL epitopes were selected. The identified T-cell epitopes showed marked interactions towards the HLA binding alleles and provided 99.8% world population coverage. Consequently, a vaccine chimera was designed by connecting these epitopes with suitable linkers and adjuvant (LprG). The vaccine construct was antigenic and immunogenic as well as non-allergenic; hence, subjected to homology modelling. The molecular docking and dynamic simulation revealed strong and stable binding affinity between the vaccine and TLR2 receptor. The binding energy (∆G) and dissociation constant (Kd) were -15.3 kcal/mol and 5.9×10 -12 M, respectively. Further, disulfide engineering was applied to improve vaccine' stability and higher expression in Escherichia coli K12 system was ensured by codon optimization and cloning in silico.The computer-simulated immune responses were characterized by higher levels of IgM and IgG antibodies,helper T-cells with increased IFN-γ production, and macrophage activity crucial for immunity against M. ulcerans.Therefore, our data suggest that, if the designed vaccine is validated experimentally, it will prevent Buruli ulcer by generating robust immune response against M. ulcerans.

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“…IL10pred (Nagpal et al 2017) servers, respectively. The IL4pred and IL10pred operations were carried out based on SVM method with threshold value 0.2 and -0.3, respectively.…”

Section: Prediction and Evaluation Of Helper T-lymphocyte (Htl) Epitomentioning

confidence: 99%

Structural Basis and Designing of Peptide Vaccine using PE-PGRS Family Protein of Mycobacterium ulcerans – An Integrated Vaccinomics Approach

Nain

Karim

Sen³

et al. 2019

Preprint

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“…We employed these five training and test sets for performing five-fold cross-validation to select the best machine learning models as well as for developing BLAST similarity search based module, as explained in the next sections. Five-fold cross-validation is a standard process that has been successfully implemented in several machine learning-based studies in the past (29,(36)(37)(38)(39)(40).…”

Section: Five -Fold Cross Validationmentioning

confidence: 99%

“…Different classifiers such as Random Forest (RF), Logistic Regression (LR), Support Vector Machine (SVM), Extra Trees (ET), K-Nearest Neighbour (KNN) and Multi-Layer Perceptron (MLP) were used to develop prediction models. All these machine learning methods have been successfully applied in many bioinformatics studies (29,36,40,48,49).…”

Section: Machine Learning Techniquesmentioning

confidence: 99%

A Hybrid Model for Predicting Pattern Recognition Receptors using Evolutionary Information

Kaur

Arora

Raghava

2019

Preprint

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This study describes a method developed for predicting pattern recognition receptors (PRRs), which are an integral part of the immune system. The models developed here were trained and evaluated on the largest possible non-redundant PRRs, and non-pattern recognition receptors (Non-PRRs) obtained from PRRDB 2.0. Firstly, a similarity-based approach using BLAST was used to predict PRRs and got limited success due to a large number of no-hits. Secondly, machine learning-based models were developed using sequence composition and achieved a maximum MCC of 0.63. In addition to this, models were developed using evolutionary information in the form of PSSM composition and achieved maximum MCC value of 0.66. Finally, we developed hybrid models that combined a similarity-based approach using BLAST and machine learningbased models. Our best model, which combined BLAST and PSSM based model, achieved a maximum MCC value of 0.82 with an AUROC value of 0.95, utilizing the potential of both similarity-based search and machine learning techniques. In order to facilitate the scientific community, we also developed a web server "PRRpred" based on the best model developed in this study (http://webs.iiitd.edu.in/raghava/prrpred/).

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“…Several cytokines such as IL-2, IL-6, IL-10, IFN-γ, and TNF-γ are associated with Hantavirus immune system 93 . Herein, due to the tools unavailability, we only predicted the IL-10, and IFN-γ inducer HTL epitopes through the IL-10Pred server (https://webs.iiitd.edu.in/raghava/il10pred/) 94 and IFNepitope server (http://crdd.osdd.net/raghava/ifnepitope/) 95 , respectively in which the SVM technique was chosen as a prediction method.…”

Section: Screening Of Cytokine-inducing Htl Epitopesmentioning

confidence: 99%

Computational Approach for Screening the Whole Proteome of Hantavirus and Designing a Multi-Epitope Subunit Vaccine

Abdulla

Nain

et al. 2019

Preprint

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Hantaviruses are a newly zoonotic emerging group of rodent-borne viruses that have a significant impact on global public health by increasing amplitude and magnitude of outbreaks. As no permanent cure yet, it is now growing and challenging interest to develop a vaccine against Hantavirus. This study endeavored to design a robust subunit vaccine using a novel immunoinformatics approach. After meticulous evaluation, top ones from predicted CTL, HTL, and B-cell epitopes were considered as potential vaccine candidates. Among generated four vaccine models with different adjuvant, the model with TLR-4 agonist adjuvant was selected for its high antigenicity, non-allergenicity, and structural quality. The conformational B-cell epitope prediction assured its humoral response inducing ability. Thereafter, the molecular docking and dynamics simulation confirmed a good binding affinity with immune receptor TLR-4 and stability of the vaccine-receptor complex. In immune simulation, significantly high levels of IgM and IgG1 immunoglobulins, TC and TH-cell populations, and various cytokines (i.e. IFN-γ, IL-2 etc.) are coherence with actual immune response and also showed faster antigen clearance for repeated exposures. Finally, disulfide engineering enhanced vaccine stability and in silico cloning confirmed the better expression in E. coli K12. Nonetheless, experimental validation can proof the proposed vaccine's safety and ability to control Hantavirus infection.

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Computer-aided designing of immunosuppressive peptides based on IL-10 inducing potential

Cited by 217 publications

References 53 publications

Structural Basis and Designing of Peptide Vaccine using PE-PGRS Family Protein of Mycobacterium ulcerans – An Integrated Vaccinomics Approach

Structural Basis and Designing of Peptide Vaccine using PE-PGRS Family Protein of Mycobacterium ulcerans – An Integrated Vaccinomics Approach

A Hybrid Model for Predicting Pattern Recognition Receptors using Evolutionary Information

Computational Approach for Screening the Whole Proteome of Hantavirus and Designing a Multi-Epitope Subunit Vaccine

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