Identification of B cell epitopes of Per a 5 allergen using bioinformatic approach

Sharma, Swati; Vashisht, Srishti; Gaur, Shailendra Nath; Lavasa, Shakuntala; Arora, Naveen

doi:10.1016/j.imbio.2021.152146

Cited by 2 publications

(1 citation statement)

References 33 publications

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“…In contrast to labor-intensive and costly experimental methods, computational identification is cheap and high-throughput (Peng et al, 2022;Shen et al, 2022;Tian et al, 2022). Over the past decades, no less than 10 computational methods for predicting BCEs have been created (El-Manzalawy et al, 2008aAnsari and Raghava, 2010;El-Manzalawy and Honavar, 2010;Jespersen et al, 2017;Ras-Carmona et al, 2021;Sharma et al, 2021;Alghamdi et al, 2022). The sequence is the simplest manifestation of protein but is pivotal for structure and function formation, and thus, the sequence compositions were frequently employed as a factor to identify BCEs (Chen et al, 2007;Singh et al, 2013).…”

Section: Introductionmentioning

confidence: 99%

DeepLBCEPred: A Bi-LSTM and multi-scale CNN-based deep learning method for predicting linear B-cell epitopes

Zheng²,

Huang³

2023

Front. Microbiol.

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The epitope is the site where antigens and antibodies interact and is vital to understanding the immune system. Experimental identification of linear B-cell epitopes (BCEs) is expensive, is labor-consuming, and has a low throughput. Although a few computational methods have been proposed to address this challenge, there is still a long way to go for practical applications. We proposed a deep learning method called DeepLBCEPred for predicting linear BCEs, which consists of bi-directional long short-term memory (Bi-LSTM), feed-forward attention, and multi-scale convolutional neural networks (CNNs). We extensively tested the performance of DeepLBCEPred through cross-validation and independent tests on training and two testing datasets. The empirical results showed that the DeepLBCEPred obtained state-of-the-art performance. We also investigated the contribution of different deep learning elements to recognize linear BCEs. In addition, we have developed a user-friendly web application for linear BCEs prediction, which is freely available for all scientific researchers at: http://www.biolscience.cn/DeepLBCEPred/.

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Section: Introductionmentioning

confidence: 99%

DeepLBCEPred: A Bi-LSTM and multi-scale CNN-based deep learning method for predicting linear B-cell epitopes

Zheng²,

Huang³

2023

Front. Microbiol.

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Per a 5-derived T-cell peptides modulate NF-kB signalling to ameliorate allergic inflammation systemically in murine model of cockroach allergic hyper-reactivity

Sharma

Nagar

Arora

2022

Clinical and Experimental Immunology

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Peptide immunotherapy (PIT) represents a safe and efficacious therapeutic regimen with in-consequential side-effects. Present study aims to identify T cell epitopes of Per a 5 allergen, a delta class GST from Periplaneta americana and investigate effect of peptide treatment in murine model of cockroach allergen mediated hyper-reactivity. The epitopes (TC-P1, TC-P2 & TC-P3) were identified as promiscuous MHC-II binders by MHC-Pred, ProPred & IEDB analysis tool. Murine model of cockroach allergic hyper-reactivity was generated in Balb/c mice. A marked reduction in cellular infiltration in lungs (3-fold compared to Non-IT) was observed in T3-IT group as evidenced by total leucocyte count in BALF and histology. Specific IgE levels were reduced 3-fold in T2-IT and T3-IT compared to Non-IT with increase in IgG2a levels. IL-4 and IL-13 were reduced upto 2.5-fold in treatment groups compared to Non-IT group. Splenocytes revealed significant increase in levels of CD4 +FoxP3 + T cells in TC-P1 and TC-P2 mice demonstrating a systemic shift towards Tregs. Peptide treatment downregulated NF-kB signalling in lung and enhanced the levels of immune-regulatory molecules α1-antitrypsin and elafin. Our results indicate that, TC-P1 and TC-P3 alter Th2 cytokine milieu and antibody isotype ratio to suppress allergic inflammation. PIT modulates local and systemic mechanisms to resolve inflammation and possess potential for treatment in cockroach allergy.

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Identification of B cell epitopes of Per a 5 allergen using bioinformatic approach

Cited by 2 publications

References 33 publications

DeepLBCEPred: A Bi-LSTM and multi-scale CNN-based deep learning method for predicting linear B-cell epitopes

DeepLBCEPred: A Bi-LSTM and multi-scale CNN-based deep learning method for predicting linear B-cell epitopes

Per a 5-derived T-cell peptides modulate NF-kB signalling to ameliorate allergic inflammation systemically in murine model of cockroach allergic hyper-reactivity

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