Histocompatibility Antigens Class I - AI Medical Compendium

NeoaPred: a deep-learning framework for predicting immunogenic neoantigen based on surface and structural features of peptide-human leukocyte antigen complexes.

Bioinformatics (Oxford, England) Sep 2, 2024

MOTIVATION: Neoantigens, derived from somatic mutations in cancer cells, can elicit anti-tumor immune responses when presented to autologous T cells by human leukocyte antigen. Identifying immunogenic neoantigens is crucial for cancer immunotherapy d...

Peptides Deep Learning Software Neoplasms HLA Antigens Histocompatibility Antigens Class I Computational Biology Antigens, Neoplasm Humans

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RPEMHC: improved prediction of MHC-peptide binding affinity by a deep learning approach based on residue-residue pair encoding.

Bioinformatics (Oxford, England) Jan 2, 2024

MOTIVATION: Binding of peptides to major histocompatibility complex (MHC) molecules plays a crucial role in triggering T cell recognition mechanisms essential for immune response. Accurate prediction of MHC-peptide binding is vital for the developmen...

Protein Binding Histocompatibility Antigens Class I Major Histocompatibility Complex Peptides Deep Learning

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Deep Learning-Assisted Analysis of Immunopeptidomics Data.

Methods in molecular biology (Clifton, N.J.) Jan 1, 2024

Liquid chromatography-coupled mass spectrometry (LC-MS/MS) is the primary method to obtain direct evidence for the presentation of disease- or patient-specific human leukocyte antigen (HLA). However, compared to the analysis of tryptic peptides in pr...

Humans Peptides HLA Antigens Histocompatibility Antigens Class I Deep Learning Chromatography, Liquid Tandem Mass Spectrometry

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Unsupervised and supervised AI on molecular dynamics simulations reveals complex characteristics of HLA-A2-peptide immunogenicity.

Briefings in bioinformatics Nov 22, 2023

Immunologic recognition of peptide antigens bound to class I major histocompatibility complex (MHC) molecules is essential to both novel immunotherapeutic development and human health at large. Current methods for predicting antigen peptide immunogen...

Protein Binding Humans Artificial Intelligence Histocompatibility Antigens Class I Molecular Dynamics Simulation Peptides HLA-A2 Antigen

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Predicting MHC class I binder: existing approaches and a novel recurrent neural network solution.

Briefings in bioinformatics Nov 5, 2021

Major histocompatibility complex (MHC) possesses important research value in the treatment of complex human diseases. A plethora of computational tools has been developed to predict MHC class I binders. Here, we comprehensively reviewed 27 up-to-date...

Binding Sites Web Browser Carrier Proteins Epitopes Histocompatibility Antigens Class I Amino Acid Sequence Protein Binding Computational Biology Software Databases, Factual ROC Curve Reproducibility of Results Deep Learning Neural Networks, Computer Machine Learning

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ACME: pan-specific peptide-MHC class I binding prediction through attention-based deep neural networks.

Bioinformatics (Oxford, England) Dec 1, 2019

MOTIVATION: Prediction of peptide binding to the major histocompatibility complex (MHC) plays a vital role in the development of therapeutic vaccines for the treatment of cancer. Algorithms with improved correlations between predicted and actual bind...

Attention Neural Networks, Computer Computational Biology Binding Sites Algorithms Histocompatibility Antigens Class I Peptides Protein Binding

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HLA class I binding prediction via convolutional neural networks.

Bioinformatics (Oxford, England) Sep 1, 2017

MOTIVATION: Many biological processes are governed by protein-ligand interactions. One such example is the recognition of self and non-self cells by the immune system. This immune response process is regulated by the major histocompatibility complex ...

Histocompatibility Antigens Class I Machine Learning Humans HLA Antigens Peptides Neural Networks, Computer Epitopes Protein Binding Proteomics

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AIMC Topic: Histocompatibility Antigens Class I

NeoaPred: a deep-learning framework for predicting immunogenic neoantigen based on surface and structural features of peptide-human leukocyte antigen complexes.

RPEMHC: improved prediction of MHC-peptide binding affinity by a deep learning approach based on residue-residue pair encoding.

Deep Learning-Assisted Analysis of Immunopeptidomics Data.

Unsupervised and supervised AI on molecular dynamics simulations reveals complex characteristics of HLA-A2-peptide immunogenicity.

Predicting MHC class I binder: existing approaches and a novel recurrent neural network solution.

ACME: pan-specific peptide-MHC class I binding prediction through attention-based deep neural networks.

HLA class I binding prediction via convolutional neural networks.

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AIMC Topic: Histocompatibility Antigens Class I

NeoaPred: a deep-learning framework for predicting immunogenic neoantigen based on surface and structural features of peptide-human leukocyte antigen complexes.

RPEMHC: improved prediction of MHC-peptide binding affinity by a deep learning approach based on residue-residue pair encoding.

Deep Learning-Assisted Analysis of Immunopeptidomics Data.

Unsupervised and supervised AI on molecular dynamics simulations reveals complex characteristics of HLA-A2-peptide immunogenicity.

Predicting MHC class I binder: existing approaches and a novel recurrent neural network solution.

ACME: pan-specific peptide-MHC class I binding prediction through attention-based deep neural networks.

HLA class I binding prediction via convolutional neural networks.

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