Prediction of IL4 Inducing Peptides

Dhanda, Sandeep Kumar; Gupta, Sudheer; Vir, Pooja; Raghava, Gajendra P. S.

doi:10.1155/2013/263952

Cited by 239 publications

(159 citation statements)

References 33 publications

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“…So, we subjected the promiscuous CD4 + T-cell epitopes (peptides) to check, whether predicted epitopes are IFN-γ and IL-4 inducer or non-inducer in nature. In this study, the IFN-γ inducing CD4 + T-cell epitopes was predicted by the IFNepitope tool (http:// crdd.osdd.net/raghava/ifnepitope/), an in silico server for predicting and designing interferon-gamma inducing epitopes (Dhanda et al, 2013). The SVM (support vector machine) based method and the IFN-γ versus non IFN-γ model was set as parameters for the prediction of the nature of epitopes.…”

Section: Identification Of Ifn-γ and Il-4 Inducer Property Of The Cd4mentioning

confidence: 99%

“…The SVM (support vector machine) based method and the IFN-γ versus non IFN-γ model was set as parameters for the prediction of the nature of epitopes. On the other hand, the IL-4 inducer was predicted by the IL4pred tool (http://crdd.osdd.net/raghava/il4pred/), an in silico platform for designing and discovering of interleukin-4 inducing peptides (Dhanda et al, 2013). For the prediction of this nature, the SVM based model was used, whereas the threshold value was set at 0.1 as a parameter of the SVM model.…”

Section: Identification Of Ifn-γ and Il-4 Inducer Property Of The Cd4mentioning

confidence: 99%

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Overlapping CD8 + and CD4 + T-cell epitopes identification for the progression of epitope-based peptide vaccine from nucleocapsid and glycoprotein of emerging Rift Valley fever virus using immunoinformatics approach

Adhikari

Rahman

2017

Infection, Genetics and Evolution

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Rift Valley fever virus (RVFV) is an emergent arthropod-borne zoonotic infectious viral pathogen which causes fatal diseases in the humans and ruminants. Currently, no effective and licensed vaccine is available for the prevention of RVFV infection in endemic as well as in non-endemic regions. So, an immunoinformatics-driven genome-wide screening approach was performed for the identification of overlapping CD8+ and CD4+ T-cell epitopes and also linear B-cell epitopes from the conserved sequences of the nucleocapsid (N) and glycoprotein (G) of RVFV. We identified overlapping 99.39% conserved 1 CD8+ T-cell epitope (MMHPSFAGM) from N protein and 100% conserved 7 epitopes (AVFALAPVV, LAVFALAPV, FALAPVVFA, VFALAPVVF, IAMTVLPAL, FFDWFSGLM, and FLLIYLGRT) from G protein and also identified IL-4 and IFN-γ induced (99.39% conserved) 1 N protein CD4+ T-cell epitope (HMMHPSFAGMVDPSL) and 100% conserved 5 G protein CD4+ T-cell epitopes (LPALAVFALAPVVFA, PALAVFALAPVVFAE, GIAMTVLPALAVFAL, GSWNFFDWFSGLMSW, and FFLLIYLGRTGLSKM). The overlapping CD8+ and CD4+ T-cell epitopes were bound with most conserved HLA-C*12:03 and HLA-DRB1*01:01, respectively with the high binding affinity (kcal/mol). The combined population coverage analysis revealed that the allele frequencies of these epitopes are high in endemic and non-endemic regions. Besides, we found 100% conserved and non-allergenic 2 decamer B-cell epitopes, GVCEVGVQAL and RVFNCIDWVH of G protein had the sequence similarity with the nonamer CD8+ T-cell epitopes, VCEVGVQAL and RVFNCIDWV, respectively. Consequently, these epitopes may be used for the development of epitope-based peptide vaccine against emerging RVFV. However, in vivo and in vitro experiments are required for their efficient use as a vaccine.

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Section: Identification Of Ifn-γ and Il-4 Inducer Property Of The Cd4mentioning

confidence: 99%

Section: Identification Of Ifn-γ and Il-4 Inducer Property Of The Cd4mentioning

confidence: 99%

Overlapping CD8 + and CD4 + T-cell epitopes identification for the progression of epitope-based peptide vaccine from nucleocapsid and glycoprotein of emerging Rift Valley fever virus using immunoinformatics approach

Adhikari

Rahman

2017

Infection, Genetics and Evolution

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“…So, we used IFNepitope server for the prediction of IFN-γ inducing HTL epitopes using a hybrid method (Motif and SVM) along with IFN-gamma versus Non-IFN-gamma model [34]. In addition to IFN-gamma, IL-4 and Il-10 properties were also evaluated with IL4pred and IL10pred servers, respectively [35,36].…”

Section: Identification Of Cytokine-inducing Htl Epitopesmentioning

confidence: 99%

Immunoinformatics-Guided Designing of Peptide Vaccine against Lassa Virus with Dynamic and Immune Simulation Studies

S¹,

Nain²,

F³

et al. 2019

Preprint

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Lassa virus (LASV) is responsible for a type of acute viral haemorrhagic fever referred to as Lassa fever. Lack of adequate treatment and preventive measures against LASV resulted in a high mortality rate in its endemic regions. In this study, a multi-epitope vaccine was designed using immunoinformatics as a prophylactic agent against the virus. Following a rigorous assessment, the vaccine was built using T-cell (NCTL=8 and NHTL=6) and B-cell (NLBL=4) epitopes from each LASV-derived protein with suitable linkers and adjuvant. The physicochemistry, immunogenic potency and safeness of the designed vaccine (~68 kDa) were assessed. In addition, chosen CTL and HTL epitopes of our vaccine showed 97.37% worldwide population coverage. Besides, disulphide engineering also improved the stability of the chimeric vaccine. Molecular docking of our vaccine protein with toll-like receptor (TLR2) showed binding efficiency followed by dynamic simulation for stable interaction. Furthermore, higher levels of cell-mediated immunity and rapid antigen clearance were suggested by immune simulation and repeated-exposure simulation, respectively. Finally, the optimized codons were used in in silico cloning to ensure higher expression within E. coli K12 bacterium. With further assessment both in vitro and in vivo, we believe that our proposed peptide-vaccine would be potential immunogen against Lassa fever.

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“…We used the Motif-EmeRging and with Classes-Identification (MERCI) Program [34][] to identify motifs exclusively occurring in the A-cell epitopes [35]. Though this program allows searching for gapped and ungapped motifs, but we restricted our analysis to the ungapped motifs.…”

Section: Motif Searchmentioning

confidence: 99%

“…It is important to assess the performance of a model on external or independent dataset because the performance of a model in internal validation may be biased due to optimization of the model [27]. The performance of models was measured using standard metrics namely Sensitivity, Specificity, Accuracy and Matthew's Correlation Coefficient (MCC) [19,35].…”

Section: Evaluation Of Models Using Internal and External Validationmentioning

confidence: 99%

Computer-aided prediction of antigen presenting cell modulators for designing peptide-based vaccine adjuvants

Nagpal

Chaudhary

Agrawal

et al. 2017

Preprint

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BackgroundEvidences in literature strongly advocate the potential of immunomodulatory peptides for use as vaccine adjuvants. All the mechanisms of vaccine adjuvants ensuing immunostimulatory effects directly or indirectly stimulate Antigen Presenting Cells (APCs). While numerous methods have been developed in the past for predicting B-cell and T-cell epitopes; no method is available for predicting the peptides that can modulate the APCs. MethodsWe named the peptides that can activate APCs as A-cell epitopes and developed methods for their prediction in this study. A dataset of experimentally validated A-cell epitopes was collected and compiled from various resources. To predict A-cell epitopes, we developed Support Vector Machine-based machine learning models using different sequence-based features. Results A hybrid model developed on a combination of sequence-based features (dipeptide composition and motif occurrence), achieved the highest accuracy of 96.91% with Matthews Correlation Coefficient (MCC) value of 0.94 on the training dataset. We also evaluated the hybrid models on an independent dataset and achieved a comparable accuracy of 94.93% with MCC 0.90. ConclusionThe models developed in this study were implemented in a web-based platform VaxinPAD to predict and design immunomodulatory peptides or A-cell epitopes. This web server available at http://webs.iiitd.edu.in/raghava/vaxinpad/ and http://crdd.osdd.net/raghava/vaxinpad/ will facilitate researchers in designing peptidebased vaccine adjuvants.

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Prediction of IL4 Inducing Peptides

Cited by 239 publications

References 33 publications

Overlapping CD8 + and CD4 + T-cell epitopes identification for the progression of epitope-based peptide vaccine from nucleocapsid and glycoprotein of emerging Rift Valley fever virus using immunoinformatics approach

Overlapping CD8 + and CD4 + T-cell epitopes identification for the progression of epitope-based peptide vaccine from nucleocapsid and glycoprotein of emerging Rift Valley fever virus using immunoinformatics approach

Immunoinformatics-Guided Designing of Peptide Vaccine against Lassa Virus with Dynamic and Immune Simulation Studies

Computer-aided prediction of antigen presenting cell modulators for designing peptide-based vaccine adjuvants

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