scholarly journals NIgPred: Class-Specific Antibody Prediction for Linear B-Cell Epitopes Based on Heterogeneous Features and Machine-Learning Approaches

Viruses ◽  
2021 ◽  
Vol 13 (8) ◽  
pp. 1531
Author(s):  
Chi-Hua Tung ◽  
Yi-Sheng Chang ◽  
Kai-Po Chang ◽  
Yen-Wei Chu
Keyword(s):  
Machine Learning ◽  
B Cell ◽  
Training Model ◽  
Hill Climbing ◽  
Learning Approaches ◽  
Data Set ◽  
B Cell Epitopes ◽  
Hill Climbing Algorithm ◽  
Allergen Detection ◽  
Linear B

Upon invasion by foreign pathogens, specific antibodies can identify specific foreign antigens and disable them. As a result of this ability, antibodies can help with vaccine production and food allergen detection in patients. Many studies have focused on predicting linear B-cell epitopes, but only two prediction tools are currently available to predict the sub-type of an epitope. NIgPred was developed as a prediction tool for IgA, IgE, and IgG. NIgPred integrates various heterologous features with machine-learning approaches. Differently from previous studies, our study considered peptide-characteristic correlation and autocorrelation features. Sixty kinds of classifier were applied to construct the best prediction model. Furthermore, the genetic algorithm and hill-climbing algorithm were used to select the most suitable features for improving the accuracy and reducing the time complexity of the training model. NIgPred was found to be superior to the currently available tools for predicting IgE epitopes and IgG epitopes on independent test sets. Moreover, NIgPred achieved a prediction accuracy of 100% for the IgG epitopes of a coronavirus data set. NIgPred is publicly available at our website.

scholarly journals Linear B-Cell Epitopes in Human Norovirus GII.4 Capsid Protein Elicit Blockade Antibodies

Vaccines ◽  
2021 ◽  
Vol 9 (1) ◽  
pp. 52
Author(s):  
Hassan Moeini ◽  
Suliman Qadir Afridi ◽  
Sainitin Donakonda ◽  
Percy A. Knolle ◽  
Ulrike Protzer ◽  
...  
Keyword(s):  
B Cell ◽  
Capsid Protein ◽  
Immune Escape ◽  
Solvent Accessibility ◽  
Effective Vaccine ◽  
Blood Group Antigens ◽  
Human Norovirus ◽  
B Cell Epitopes ◽  
Blocking Rate ◽  
Linear B

Human norovirus (HuNoV) is the leading cause of nonbacterial gastroenteritis worldwide with the GII.4 genotype accounting for over 80% of infections. The major capsid protein of GII.4 variants is evolving rapidly, resulting in new epidemic variants with altered antigenic potentials that must be considered for the development of an effective vaccine. In this study, we identify and characterize linear blockade B-cell epitopes in HuNoV GII.4. Five unique linear B-cell epitopes, namely P2A, P2B, P2C, P2D, and P2E, were predicted on the surface-exposed regions of the capsid protein. Evolving of the surface-exposed epitopes over time was found to correlate with the emergence of new GII.4 outbreak variants. Molecular dynamic simulation (MD) analysis and molecular docking revealed that amino acid substitutions in the putative epitopes P2B, P2C, and P2D could be associated with immune escape and the appearance of new GII.4 variants by affecting solvent accessibility and flexibility of the antigenic sites and histo-blood group antigens (HBAG) binding. Testing the synthetic peptides in wild-type mice, epitopes P2B (336–355), P2C (367–384), and P2D (390–400) were recognized as GII.4-specific linear blockade epitopes with the blocking rate of 68, 55 and 28%, respectively. Blocking rate was found to increase to 80% using the pooled serum of epitopes P2B and P2C. These data provide a strategy for expanding the broad blockade potential of vaccines for prevention of NoV infection.

scholarly journals Mining of linear B cell epitopes of SARS-CoV-2 ORF8 protein from COVID-19 patients

2021 ◽  
pp. 1-19
Author(s):  
Xiaohui Wang ◽  
Joy-Yan Lam ◽  
Linlei Chen ◽  
Shannon Wing-Ngor Au ◽  
Kelvin K. W. To ◽  
...  
Keyword(s):  
B Cell ◽  
B Cell Epitopes ◽  
Linear B

Machine learning identifies an immunological pattern associated with multiple juvenile idiopathic arthritis subtypes

2019 ◽  
Vol 78 (5) ◽  
pp. 617-628 ◽  
Author(s):  
Erika Van Nieuwenhove ◽  
Vasiliki Lagou ◽  
Lien Van Eyck ◽  
James Dooley ◽  
Ulrich Bodenhofer ◽  
...  
Keyword(s):  
Machine Learning ◽  
Juvenile Idiopathic Arthritis ◽  
Large Scale ◽  
Inflammatory Diseases ◽  
Adaptive Immune System ◽  
Healthy Children ◽  
Learning Approaches ◽  
Data Set ◽  
Immune Signature ◽  
Systemic Jia

ObjectivesJuvenile idiopathic arthritis (JIA) is the most common class of childhood rheumatic diseases, with distinct disease subsets that may have diverging pathophysiological origins. Both adaptive and innate immune processes have been proposed as primary drivers, which may account for the observed clinical heterogeneity, but few high-depth studies have been performed.MethodsHere we profiled the adaptive immune system of 85 patients with JIA and 43 age-matched controls with indepth flow cytometry and machine learning approaches.ResultsImmune profiling identified immunological changes in patients with JIA. This immune signature was shared across a broad spectrum of childhood inflammatory diseases. The immune signature was identified in clinically distinct subsets of JIA, but was accentuated in patients with systemic JIA and those patients with active disease. Despite the extensive overlap in the immunological spectrum exhibited by healthy children and patients with JIA, machine learning analysis of the data set proved capable of discriminating patients with JIA from healthy controls with ~90% accuracy.ConclusionsThese results pave the way for large-scale immune phenotyping longitudinal studies of JIA. The ability to discriminate between patients with JIA and healthy individuals provides proof of principle for the use of machine learning to identify immune signatures that are predictive to treatment response group.

scholarly journals Immunisation With Immunodominant Linear B Cell Epitopes Vaccine of Manganese Transport Protein C Confers Protection against Staphylococcus aureus Infection

PLoS ONE ◽  
2016 ◽  
Vol 11 (2) ◽  
pp. e0149638 ◽  
Author(s):  
Hui-Jie Yang ◽  
Jin-Yong Zhang ◽  
Chao Wei ◽  
Liu-Yang Yang ◽  
Qian-Fei Zuo ◽  
...  
Keyword(s):  
Staphylococcus Aureus ◽  
B Cell ◽  
Protein C ◽  
Transport Protein ◽  
B Cell Epitopes ◽  
Aureus Infection ◽  
Manganese Transport ◽  
Linear B

scholarly journals Anaplasma marginale Major Surface Protein 2 CD4+-T-Cell Epitopes Are Evenly Distributed in Conserved and Hypervariable Regions (HVR), Whereas Linear B-Cell Epitopes Are Predominantly Located in the HVR

2004 ◽  
Vol 72 (12) ◽  
pp. 7360-7366 ◽  
Author(s):  
Jeffrey R. Abbott ◽  
Guy H. Palmer ◽  
Chris J. Howard ◽  
Jayne C. Hope ◽  
Wendy C. Brown
Keyword(s):  
T Cell ◽  
B Cell ◽  
Surface Protein ◽  
Anaplasma Marginale ◽  
T Cell Epitopes ◽  
B Cell Epitopes ◽  
Major Surface Protein ◽  
Hypervariable Regions ◽  
Major Surface ◽  
Linear B

ABSTRACT Organisms in the genus Anaplasma express an immunodominant major surface protein 2 (MSP2), composed of a central hypervariable region (HVR) flanked by highly conserved regions. Throughout Anaplasma marginale infection, recombination results in the sequential appearance of novel MSP2 variants and subsequent control of rickettsemia by the immune response, leading to persistent infection. To determine whether immune evasion and selection for variant organisms is associated with a predominant response against HVR epitopes, T-cell and linear B-cell epitopes were localized by measuring peripheral blood gamma interferon-secreting cells, proliferation, and antibody binding to 27 overlapping peptides spanning MSP2 in 16 cattle. Similar numbers of MSP2-specific CD4+ T-cell epitopes eliciting responses of similar magnitude were found in conserved and hypervariable regions. T-cell epitope clusters recognized by the majority of animals were identified in the HVR (amino acids [aa] 171 to 229) and conserved regions (aa 101 to 170 and 272 to 361). In contrast, linear B-cell epitopes were concentrated in the HVR, residing within hydrophilic sequences. The pattern of recognition of epitope clusters by T cells and of HVR epitopes by B cells is consistent with the influence of protein structure on epitope recognition.

Prediction of linear B-cell epitopes using amino acid pair antigenicity scale

Amino Acids ◽  
2007 ◽  
Vol 33 (3) ◽  
pp. 423-428 ◽  
Author(s):  
J. Chen ◽  
H. Liu ◽  
J. Yang ◽  
K.-C. Chou
Keyword(s):  
Amino Acid ◽  
B Cell ◽  
Amino Acid Pair ◽  
B Cell Epitopes ◽  
Linear B

Screening and Identification of Linear B Cell Epitopes Within the Nonstructural Proteins of Enterovirus 71

2019 ◽  
Vol 32 (2) ◽  
pp. 84-88 ◽  
Author(s):  
Jianhua Zhang ◽  
Huiqi Huang ◽  
Lian Xu ◽  
Chaonan Lou ◽  
Mi Pan
Keyword(s):  
B Cell ◽  
Enterovirus 71 ◽  
Nonstructural Proteins ◽  
B Cell Epitopes ◽  
Screening And Identification ◽  
Linear B
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