William H. Hildebrand

NetMHCIIphosPan: a machine learning tool for predictingHLA class II antigen presentation of phosphorylated peptides.

NetMHCIIphosPan predicts HLA class II antigen presentation of phosphorylated peptides using immunopeptidomics data, outperforming existing tools.

Mutations outside the MR1 antigen binding groove differentially inhibit presentation of exogenous antigens

This study shows that mutations in MR1 outside the antigen binding groove differentially affect exogenous antigen presentation by altering B2M association.

Molecular characterization of the archaic HLA-B*73:01 allele reveals presentation of a unique peptidome and skewed engagement by KIR2DL2

The paper characterizes HLA-B*73:01, revealing its unique peptide binding motif, restricted peptidome, and distinct interaction with KIR2DL2, suggesting evolutionary advantages in pathogen resistance.

Disruption of riboflavin biosynthesis in mycobacteria establishes 5-amino-6-D-ribitylaminouracil (5-A-RU) as key precursor of MAIT cell agonists

Deleting early riboflavin genes (ribA2 in both species, ribG in M. smegmatis) sharply reduced MAIT cell activation, while ribH deletion raised recognition of M. tuberculosis only.

Machine learning reveals limited contribution of trans-only encoded variants to the HLA-DQ immunopeptidome by accurate and comprehensive HLA-DQ antigen presentation prediction

The paper improves HLA-DQ antigen presentation prediction using machine learning and shows trans-only variants have limited impact on the immunopeptidome.

Immunogenic potential of neopeptides depends on parent protein subcellular location

The paper shows that neopeptide immunogenicity is influenced by the subcellular location of their parent proteins, improving predictions of vaccine and immunotherapy responses.

A Kmer-based paired-end read (KPR) de novo assembler and genotyper to genotype major histocompatibility complex class I (MHC-I) alleles for the dog

The paper develops a KPR tool for MHC-I genotyping in dogs using RNA-seq data, achieving 86% success rate and identifying 122 new alleles.

Improved prediction of MHC II antigen presentation throughintegration and motif deconvolution of mass spectrometryMHC eluted ligand data

The paper improves MHC II epitope prediction by integrating mass spectrometry data with a new machine learning framework, achieving better performance than existing methods.