scholarly journals EpiDope: a deep neural network for linear B-cell epitope prediction

2020 ◽  
Author(s):  
Maximilian Collatz ◽  
Florian Mock ◽  
Emanuel Barth ◽  
Martin Hölzer ◽  
Konrad Sachse ◽  
...  
Keyword(s):  
Neural Network ◽  
B Cell ◽  
Deep Neural Network ◽  
Characteristic Curve ◽  
Cell Epitope ◽  
Epitope Prediction ◽  
Supplementary Information ◽  
B Cell Epitopes ◽  
B Cell Epitope ◽  
Linear B

Abstract Motivation By binding to specific structures on antigenic proteins, the so-called epitopes, B-cell antibodies can neutralize pathogens. The identification of B-cell epitopes is of great value for the development of specific serodiagnostic assays and the optimization of medical therapy. However, identifying diagnostically or therapeutically relevant epitopes is a challenging task that usually involves extensive laboratory work. In this study, we show that the time, cost and labor-intensive process of epitope detection in the lab can be significantly reduced using in silico prediction. Results Here, we present EpiDope, a python tool which uses a deep neural network to detect linear B-cell epitope regions on individual protein sequences. With an area under the curve between 0.67 ± 0.07 in the receiver operating characteristic curve, EpiDope exceeds all other currently used linear B-cell epitope prediction tools. Our software is shown to reliably predict linear B-cell epitopes of a given protein sequence, thus contributing to a significant reduction of laboratory experiments and costs required for the conventional approach. Availabilityand implementation EpiDope is available on GitHub (http://github.com/mcollatz/EpiDope). Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals EpiDope: A Deep neural network for linear B-cell epitope prediction

2020 ◽  
Author(s):  
Maximilian Collatz ◽  
Florian Mock ◽  
Martin Hölzer ◽  
Emanuel Barth ◽  
Konrad Sachse ◽  
...  
Keyword(s):  
Neural Network ◽  
B Cell ◽  
Deep Neural Network ◽  
False Positive Rate ◽  
Cell Epitope ◽  
Area Under The Curve ◽  
Epitope Prediction ◽  
B Cell Epitopes ◽  
B Cell Epitope ◽  
Linear B

ABSTRACTBy binding to specific structures on antigenic proteins, the so-called epitopes, B-cell antibodies can neutralize pathogens. The identification of B-cell epitopes is of great value for the development of specific serodiagnostic assays and the optimization of medical therapy. However, identifying diagnostically or therapeutically relevant epitopes is a challenging task that usually involves extensive laboratory work. In this study, we show that the time, cost and labor-intensive process of epitope detection in the lab can be significantly shortened by using in silico prediction. Here we present EpiDope, a python tool which uses a deep neural network to detect B-cell epitope regions on individual protein sequences (github.com/mcollatz/EpiDope). With an area under the curve (AUC) between 0.67 ± 0.07 in the ROC curve, EpiDope exceeds all other currently used B-cell prediction tools. Moreover, for AUC10% (AUC for a false-positive rate < 0.1), EpiDope improves the prediction accuracy in comparison to other state-of-the-art methods. Our software is shown to reliably predict linear B-cell epitopes of a given protein sequence, thus contributing to a significant reduction of laboratory experiments and costs required for the conventional approach.

scholarly journals EpiDope: a deep neural network for linear B-cell epitope prediction

2021 ◽  
Author(s):  
Maximilian Collatz ◽  
Florian Mock ◽  
Emanuel Barth ◽  
Martin Hölzer ◽  
Konrad Sachse ◽  
...  
Keyword(s):  
Neural Network ◽  
B Cell ◽  
Deep Neural Network ◽  
Cell Epitope ◽  
Epitope Prediction ◽  
B Cell Epitope ◽  
Linear B

scholarly journals Immunodominant linear B cell epitopes in the spike and membrane proteins of SARS-CoV-2 identified by immunoinformatics prediction and immunoassay

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Kanokporn Polyiam ◽  
Waranyoo Phoolcharoen ◽  
Namphueng Butkhot ◽  
Chanya Srisaowakarn ◽  
Arunee Thitithanyanont ◽  
...  
Keyword(s):  
B Cell ◽  
Cleavage Site ◽  
Vaccine Development ◽  
Cell Epitope ◽  
Epitope Prediction ◽  
E Proteins ◽  
B Cell Epitopes ◽  
C Terminus ◽  
B Cell Epitope ◽  
Linear B

AbstractSARS-CoV-2 continues to infect an ever-expanding number of people, resulting in an increase in the number of deaths globally. With the emergence of new variants, there is a corresponding decrease in the currently available vaccine efficacy, highlighting the need for greater insights into the viral epitope profile for both vaccine design and assessment. In this study, three immunodominant linear B cell epitopes in the SARS-CoV-2 spike receptor-binding domain (RBD) were identified by immunoinformatics prediction, and confirmed by ELISA with sera from Macaca fascicularis vaccinated with a SARS-CoV-2 RBD subunit vaccine. Further immunoinformatics analyses of these three epitopes gave rise to a method of linear B cell epitope prediction and selection. B cell epitopes in the spike (S), membrane (M), and envelope (E) proteins were subsequently predicted and confirmed using convalescent sera from COVID-19 infected patients. Immunodominant epitopes were identified in three regions of the S2 domain, one region at the S1/S2 cleavage site and one region at the C-terminus of the M protein. Epitope mapping revealed that most of the amino acid changes found in variants of concern are located within B cell epitopes in the NTD, RBD, and S1/S2 cleavage site. This work provides insights into B cell epitopes of SARS-CoV-2 as well as immunoinformatics methods for B cell epitope prediction, which will improve and enhance SARS-CoV-2 vaccine development against emergent variants.

The Computational Prediction Methods for Linear B-cell Epitopes

2019 ◽  
Vol 14 (3) ◽  
pp. 226-233 ◽  
Author(s):  
Cangzhi Jia ◽  
Hongyan Gong ◽  
Yan Zhu ◽  
Yixia Shi
Keyword(s):  
B Cell ◽  
Structural Information ◽  
Cell Epitope ◽  
Epitope Prediction ◽  
Computational Prediction ◽  
Extraction Methods ◽  
B Cell Epitopes ◽  
B Cell Epitope ◽  
Reliable Methods ◽  
Linear B

Background: B-cell epitope prediction is an essential tool for a variety of immunological studies. For identifying such epitopes, several computational predictors have been proposed in the past 10 years. Objective: In this review, we summarized the representative computational approaches developed for the identification of linear B-cell epitopes. </P><P> Methods: We mainly discuss the datasets, feature extraction methods and classification methods used in the previous work. Results: The performance of the existing methods was not very satisfying, and so more effective approaches should be proposed by considering the structural information of proteins. Conclusion: We consider existing challenges and future perspectives for developing reliable methods for predicting linear B-cell epitopes.

scholarly journals Data curation to improve the pattern recognition performance of B-cell epitope prediction by support vector machine

2021 ◽  
Vol 0 (0) ◽  
Author(s):  
Li Cen Lim ◽  
Yee Ying Lim ◽  
Yee Siew Choong
Keyword(s):  
Pattern Recognition ◽  
Support Vector Machine ◽  
B Cell ◽  
Recognition Performance ◽  
Cell Epitope ◽  
Epitope Prediction ◽  
Support Vector ◽  
B Cell Epitopes ◽  
Prediction Module ◽  
B Cell Epitope

Abstract B-cell epitope will be recognized and attached to the surface of receptors in B-lymphocytes to trigger immune response, thus are the vital elements in the field of epitope-based vaccine design, antibody production and therapeutic development. However, the experimental approaches in mapping epitopes are time consuming and costly. Computational prediction could offer an unbiased preliminary selection to reduce the number of epitopes for experimental validation. The deposited B-cell epitopes in the databases are those with experimentally determined positive/negative peptides and some are ambiguous resulted from different experimental methods. Prior to the development of B-cell epitope prediction module, the available dataset need to be handled with care. In this work, we first pre-processed the B-cell epitope dataset prior to B-cell epitopes prediction based on pattern recognition using support vector machine (SVM). By using only the absolute epitopes and non-epitopes, the datasets were classified into five categories of pathogen and worked on the 6-mers peptide sequences. The pre-processing of the datasets have improved the B-cell epitope prediction performance up to 99.1 % accuracy and showed significant improvement in cross validation results. It could be useful when incorporated with physicochemical propensity ranking in the future for the development of B-cell epitope prediction module.

scholarly journals An Introduction to B-Cell Epitope Mapping and In Silico Epitope Prediction

2016 ◽  
Vol 2016 ◽  
pp. 1-11 ◽  
Author(s):  
Lenka Potocnakova ◽  
Mangesh Bhide ◽  
Lucia Borszekova Pulzova
Keyword(s):  
B Cell ◽  
In Silico ◽  
Epitope Mapping ◽  
Cell Epitope ◽  
Epitope Prediction ◽  
Diagnostic Tools ◽  
Accurate Identification ◽  
B Cell Epitopes ◽  
Epitope Identification ◽  
B Cell Epitope

Identification of B-cell epitopes is a fundamental step for development of epitope-based vaccines, therapeutic antibodies, and diagnostic tools. Epitope-based antibodies are currently the most promising class of biopharmaceuticals. In the last decade, in-depth in silico analysis and categorization of the experimentally identified epitopes stimulated development of algorithms for epitope prediction. Recently, various in silico tools are employed in attempts to predict B-cell epitopes based on sequence and/or structural data. The main objective of epitope identification is to replace an antigen in the immunization, antibody production, and serodiagnosis. The accurate identification of B-cell epitopes still presents major challenges for immunologists. Advances in B-cell epitope mapping and computational prediction have yielded molecular insights into the process of biorecognition and formation of antigen-antibody complex, which may help to localize B-cell epitopes more precisely. In this paper, we have comprehensively reviewed state-of-the-art experimental methods for B-cell epitope identification, existing databases for epitopes, and novel in silico resources and prediction tools available online. We have also elaborated new trends in the antibody-based epitope prediction. The aim of this review is to assist researchers in identification of B-cell epitopes.

scholarly journals Improved Method for Linear B-Cell Epitope Prediction Using Antigen’s Primary Sequence

PLoS ONE ◽  
2013 ◽  
Vol 8 (5) ◽  
pp. e62216 ◽  
Author(s):  
Harinder Singh ◽  
Hifzur Rahman Ansari ◽  
Gajendra P. S. Raghava
Keyword(s):  
B Cell ◽  
Cell Epitope ◽  
Epitope Prediction ◽  
Primary Sequence ◽  
Improved Method ◽  
B Cell Epitope ◽  
Linear B

scholarly journals Computational Methods in Linear B-cell Epitope Prediction

2013 ◽  
Vol 63 (12) ◽  
pp. 28-32 ◽  
Author(s):  
Kavitha KV ◽  
Saritha R ◽  
Vinod Chandra S S
Keyword(s):  
B Cell ◽  
Computational Methods ◽  
Cell Epitope ◽  
Epitope Prediction ◽  
B Cell Epitope ◽  
Linear B

scholarly journals Bioinformatics Analysis of Linear B-cell Viscumin Toxin Epitope With Potential Use in Molecularly Imprinted Polymer Biosensors

2020 ◽  
Vol 10 (1) ◽  
pp. 26172
Author(s):  
Sasan Nasirahmadi ◽  
Jamil Zargan
Keyword(s):  
B Cell ◽  
Cell Epitope ◽  
Epitope Prediction ◽  
Linear Epitope ◽  
Fast Detection ◽  
B Cell Epitope ◽  
A Chain ◽  
Physical And Chemical ◽  
Potential Use ◽  
Linear B

Background: There are many diseases around the world that threaten human health and its related hygienic issues. Cancer is among the conditions mentioned above that cause many problems for health sectors worldwide.Methods: The present research analyzed the linear B-cell epitope of viscumin from European mistletoe using bioinformatics tools. We also provided references for the fast detection of biological agents. Several important tools, such as Protparam, NCBI, PDB, T-coffee, BCpred, Bptope, Ellipro, and Cn3D were used to predict the viscumin linear epitope and its physical and chemical properties.Results: The 9-mer epitope found as QQTTGEEYF embedded in the A-chain of protein by the least sequence homology with other homologous rivals. Its molecular weight, theoretical isoelectric point, and the total number of negatively charged residues were 1102.1, 3.79, and 2, respectively.Conclusion: Using different databases and establishing the accuracy level of ˃50% for linear B-cell epitope prediction, the selected epitope passed the related criteria and was introduced as a new linear epitope as a potential biological element in biosensors for cancer (viscumin) fast therapeutic detection.

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