The immunopeptidome from a genomic perspective: establishing the noncanonical landscape of MHC class I–associated peptides
Tumor antigens can emerge through multiple mechanisms, including translation of
noncoding genomic regions. This noncanonical category of tumor antigens has recently …
noncoding genomic regions. This noncanonical category of tumor antigens has recently …
[HTML][HTML] CapsNet-MHC predicts peptide-MHC class I binding based on capsule neural networks
M Kalemati, S Darvishi, S Koohi - Communications Biology, 2023 - nature.com
Abstract The Major Histocompatibility Complex (MHC) binds to the derived peptides from
pathogens to present them to killer T cells on the cell surface. Developing computational …
pathogens to present them to killer T cells on the cell surface. Developing computational …
HLA class I binding prediction via convolutional neural networks
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 …
such example is the recognition of self and non-self cells by the immune system. This …
[HTML][HTML] Predicting Class II MHC-Peptide binding: a kernel based approach using similarity scores
J Salomon, DR Flower - BMC bioinformatics, 2006 - Springer
Background Modelling the interaction between potentially antigenic peptides and Major
Histocompatibility Complex (MHC) molecules is a key step in identifying potential T-cell …
Histocompatibility Complex (MHC) molecules is a key step in identifying potential T-cell …
[HTML][HTML] A mechanistic model for predicting cell surface presentation of competing peptides by MHC class I molecules
DSM Boulanger, RC Eccleston, A Phillips… - Frontiers in …, 2018 - frontiersin.org
Major histocompatibility complex-I (MHC-I) molecules play a central role in the immune
response to viruses and cancers. They present peptides on the surface of affected cells, for …
response to viruses and cancers. They present peptides on the surface of affected cells, for …
[HTML][HTML] USMPep: universal sequence models for major histocompatibility complex binding affinity prediction
Background Immunotherapy is a promising route towards personalized cancer treatment. A
key algorithmic challenge in this process is to decide if a given peptide (neoepitope) binds …
key algorithmic challenge in this process is to decide if a given peptide (neoepitope) binds …
[HTML][HTML] MHCflurry 2.0: improved pan-allele prediction of MHC class I-presented peptides by incorporating antigen processing
Computational prediction of the peptides presented on major histocompatibility complex
(MHC) class I proteins is an important tool for studying T cell immunity. The data available to …
(MHC) class I proteins is an important tool for studying T cell immunity. The data available to …
MHCquant: automated and reproducible data analysis for immunopeptidomics
L Bichmann, A Nelde, M Ghosh… - Journal of proteome …, 2019 - ACS Publications
Personalized multipeptide vaccines are currently being discussed intensively for tumor
immunotherapy. In order to identify epitopes—short, immunogenic peptides—suitable for …
immunotherapy. In order to identify epitopes—short, immunogenic peptides—suitable for …
[HTML][HTML] MHCflurry: open-source class I MHC binding affinity prediction
TJ O'Donnell, A Rubinsteyn, M Bonsack, AB Riemer… - Cell systems, 2018 - cell.com
Predicting the binding affinity of major histocompatibility complex I (MHC I) proteins and their
peptide ligands is important for vaccine design. We introduce an open-source package for …
peptide ligands is important for vaccine design. We introduce an open-source package for …
[HTML][HTML] Estimating tissue-specific peptide abundance from public RNA-Seq data
A Frentzen, JA Greenbaum, H Kim, B Peters… - Frontiers in …, 2023 - frontiersin.org
Several novel MHC class I epitope prediction tools additionally incorporate the abundance
levels of the peptides' source antigens and have shown improved performance for predicting …
levels of the peptides' source antigens and have shown improved performance for predicting …