AIMC Topic: Histocompatibility Antigens Class I

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MHCSeqNet: a deep neural network model for universal MHC binding prediction.

BMC bioinformatics
BACKGROUND: Immunotherapy is an emerging approach in cancer treatment that activates the host immune system to destroy cancer cells expressing unique peptide signatures (neoepitopes). Administrations of cancer-specific neoepitopes in the form of synt...

ELM-MHC: An Improved MHC Identification Method with Extreme Learning Machine Algorithm.

Journal of proteome research
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 ...

Predicting peptide presentation by major histocompatibility complex class I: an improved machine learning approach to the immunopeptidome.

BMC bioinformatics
BACKGROUND: To further our understanding of immunopeptidomics, improved tools are needed to identify peptides presented by major histocompatibility complex class I (MHC-I). Many existing tools are limited by their reliance upon chemical affinity data...

Deep convolutional neural networks for pan-specific peptide-MHC class I binding prediction.

BMC bioinformatics
BACKGROUND: Computational scanning of peptide candidates that bind to a specific major histocompatibility complex (MHC) can speed up the peptide-based vaccine development process and therefore various methods are being actively developed. Recently, m...

Improved pan-specific prediction of MHC class I peptide binding using a novel receptor clustering data partitioning strategy.

HLA
Pan-specific prediction of receptor-ligand interaction is conventionally done using machine-learning methods that integrates information about both receptor and ligand primary sequences. To achieve optimal performance using machine learning, dealing ...

Pan-Specific Prediction of Peptide-MHC Class I Complex Stability, a Correlate of T Cell Immunogenicity.

Journal of immunology (Baltimore, Md. : 1950)
Binding of peptides to MHC class I (MHC-I) molecules is the most selective event in the processing and presentation of Ags to CTL, and insights into the mechanisms that govern peptide-MHC-I binding should facilitate our understanding of CTL biology. ...

Gapped sequence alignment using artificial neural networks: application to the MHC class I system.

Bioinformatics (Oxford, England)
MOTIVATION: Many biological processes are guided by receptor interactions with linear ligands of variable length. One such receptor is the MHC class I molecule. The length preferences vary depending on the MHC allele, but are generally limited to pep...

NIEluter: Predicting peptides eluted from HLA class I molecules.

Journal of immunological methods
The immune system has evolved to make a diverse repertoire of peptides processed from self and foreign proteomes, which are displayed in antigen-binding grooves of major histocompatibility complex (MHC) proteins at cell surface for surveillance by T ...

ESMpHLA: Evolutionary Scale Model-Based Deep Learning Prediction of HLA Class I Binding Peptides.

HLA
The recognition of endogenous peptides by HLA class I plays a crucial role in CD8+ T cell immune responses and human adaptive cell immune. Thus, the prediction of HLA class I-peptide binding affinities is always the core issue for the research of imm...

Attention-aware differential learning for predicting peptide-MHC class I binding and T cell receptor recognition.

Briefings in bioinformatics
The identification of neoantigens is crucial for advancing vaccines, diagnostics, and immunotherapies. Despite this importance, a fundamental question remains: how to model the presentation of neoantigens by major histocompatibility complex class I m...