AIMC Topic: Histocompatibility Antigens Class II

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Immunopeptidomic Data Integration to Artificial Neural Networks Enhances Protein-Drug Immunogenicity Prediction.

Frontiers in immunology Jun 23, 2020
Recombinant DNA technology has, in the last decades, contributed to a vast expansion of the use of protein drugs as pharmaceutical agents. However, such biological drugs can lead to the formation of anti-drug antibodies (ADAs) that may result in adve...
CD4-Positive T-Lymphocytes Infliximab Antirheumatic Agents Rituximab Protein Binding Proteomics Mass Spectrometry Histocompatibility Antigens Class II Humans Peptides Dendritic Cells Neural Networks, Computer Epitopes, T-Lymphocyte
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High-Throughput Prediction of MHC Class I and II Neoantigens with MHCnuggets.

Cancer immunology research Dec 23, 2019
Computational prediction of binding between neoantigen peptides and major histocompatibility complex (MHC) proteins can be used to predict patient response to cancer immunotherapy. Current neoantigen predictors focus on estimation of MHC binding aff...
Software Algorithms Neoplasms Protein Binding Humans Neural Networks, Computer Artificial Intelligence Mutation, Missense CD8-Positive T-Lymphocytes Histocompatibility Antigens Class II Predictive Value of Tests Histocompatibility Antigens Class I Data Mining Computational Biology Cancer Vaccines Antigens, Neoplasm
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Introducing of an integrated artificial neural network and Chou's pseudo amino acid composition approach for computational epitope-mapping of Crimean-Congo haemorrhagic fever virus antigens.

International immunopharmacology Nov 24, 2019
This study was aimed to introduce a novel algorithm for determining linear B- and T-cell epitopes from Crimean-Congo haemorrhagic fever virus (CCHFV) antigens. To this end, 387 approved B- and T-cell epitopes, as well as 331 non-epitope peptides from...
Epitopes, B-Lymphocyte Epitope Mapping Hemorrhagic Fever Virus, Crimean-Congo Hemorrhagic Fever, Crimean Antigens, Viral Protein Structure, Tertiary Histocompatibility Antigens Class II Molecular Docking Simulation Peptide Library Computational Biology Datasets as Topic Viral Proteins Support Vector Machine Amino Acid Sequence Humans Neural Networks, Computer Epitopes, T-Lymphocyte
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Machine-Learning Prediction of Tumor Antigen Immunogenicity in the Selection of Therapeutic Epitopes.

Cancer immunology research Sep 12, 2019
Current tumor neoantigen calling algorithms primarily rely on epitope/major histocompatibility complex (MHC) binding affinity predictions to rank and select for potential epitope targets. These algorithms do not predict for epitope immunogenicity usi...
Humans Histocompatibility Antigens Class I Histocompatibility Antigens Class II T-Lymphocytes, Cytotoxic Mice Animals Machine Learning Mice, Inbred C57BL Computational Biology Male Antigens, Neoplasm Algorithms Peptide Fragments Neoplasms Cancer Vaccines Mice, Inbred BALB C Female Epitopes, T-Lymphocyte
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ELM-MHC: An Improved MHC Identification Method with Extreme Learning Machine Algorithm.

Journal of proteome research Feb 18, 2019
The major histocompatibility complex (MHC) is a term for all gene groups of a major histocompatibility antigen. It binds to peptide chains derived from pathogens and displays pathogens on the cell surface to facilitate T-cell recognition and perform ...
Software Algorithms Computational Biology Histocompatibility Antigens Class II Internet Histocompatibility Antigens Class I Humans Machine Learning
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Machine learning reveals a non-canonical mode of peptide binding to MHC class II molecules.

Immunology Jun 19, 2017
MHC class II molecules play a fundamental role in the cellular immune system: they load short peptide fragments derived from extracellular proteins and present them on the cell surface. It is currently thought that the peptide binds lying more or les...
Protein Interaction Domains and Motifs Humans Epitopes Histocompatibility Antigens Class II Ligands Neural Networks, Computer Binding Sites Machine Learning Peptides Structure-Activity Relationship Databases, Protein Protein Binding Antibodies, Monoclonal Mutation Computational Biology
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Accurate pan-specific prediction of peptide-MHC class II binding affinity with improved binding core identification.

Immunogenetics Sep 29, 2015
A key event in the generation of a cellular response against malicious organisms through the endocytic pathway is binding of peptidic antigens by major histocompatibility complex class II (MHC class II) molecules. The bound peptide is then presented ...
Histocompatibility Antigens Class II Binding Sites Molecular Sequence Data Sequence Homology, Amino Acid Computational Biology Animals Cluster Analysis Protein Binding Humans Databases, Protein Mice Models, Molecular Neural Networks, Computer Epitopes Peptide Fragments Algorithms Amino Acid Sequence
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HLAIImaster: a deep learning method with adaptive domain knowledge predicts HLA II neoepitope immunogenic responses.

Briefings in bioinformatics May 23, 2024
While significant strides have been made in predicting neoepitopes that trigger autologous CD4+ T cell responses, accurately identifying the antigen presentation by human leukocyte antigen (HLA) class II molecules remains a challenge. This identifica...
Deep Learning Computational Biology Epitopes Histocompatibility Antigens Class II Humans Epitopes, T-Lymphocyte
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