TY  - JOUR
AU  - Bonsack, Maria
AU  - Hoppe, Stephanie
AU  - Winter, Jan
AU  - Tichy, Diana
AU  - Zeller, Christine
AU  - Küpper, Marius
AU  - Schitter, Eva Christine
AU  - Blatnik, Renata
AU  - Riemer, Angelika
TI  - Performance evaluation of MHC class-I binding prediction tools based on an experimentally validated MHC-peptide binding dataset.
JO  - Cancer immunology research
VL  - 7
IS  - 5
SN  - 2326-6074
CY  - Philadelphia, Pa.
PB  - AACR
M1  - DKFZ-2019-00935
SP  - 719-736
PY  - 2019
AB  - The question whether a protein can be processed and resulting peptides presented by major histocompatibility complex (MHC) is of high importance for immunotherapy design. MHC ligands can be predicted by in silico peptide-MHC class-I binding prediction algorithms. However, there are considerable differences in prediction performance, depending on the selected algorithm, MHC class-I type and peptide length. We evaluated the prediction performance of 13 algorithms based on binding affinity data of 8-11-mer peptides derived from the HPV16 E6 and E7 proteins to the most prevalent human leukocyte antigen (HLA) types. Peptides from high to low predicted binding likelihood were synthesized and their HLA binding experimentally verified by in vitro competitive binding assays. Based on the actual binding capacity of the peptides, the performance of prediction algorithms was analyzed by calculating receiver operating characteristics (ROC) and the area under the curve (AROC). No algorithm outperformed others, but different algorithms predicted best for particular HLA types and peptide lengths. Sensitivity, specificity and accuracy of decision thresholds were calculated. Commonly used decision thresholds yielded only 40
LB  - PUB:(DE-HGF)16
C6  - pmid:30902818
DO  - DOI:10.1158/2326-6066.CIR-18-0584
UR  - https://inrepo02.dkfz.de/record/143345
ER  -