Anthem: a user customised tool for fast and accurate prediction of binding between peptides and HLA class I molecules

Abstract

Neopeptide-based immunotherapy has been recognised as a promising approach for the treatment of cancers. For neopeptides to be recognised by CD8+ T cells and induce an immune response, their binding to human leukocyte antigen class I (HLA-I) molecules is a necessary first step. Most epitope prediction tools thus rely on the prediction of such binding. With the use of mass spectrometry, the scale of naturally presented HLA ligands that could be used to develop such predictors has been expanded. However, there are rarely efforts that focus on the integration of these experimental data with computational algorithms to efficiently develop up-to-date predictors. Here, we present Anthem for accurate HLA-I binding prediction. In particular, we have developed a user-friendly framework to support the development of customisable HLA-I binding prediction models to meet challenges associated with the rapidly increasing availability of large amounts of immunopeptidomic data. Our extensive evaluation, using both independent and experimental datasets shows that Anthem achieves an overall similar or higher area under curve value compared with other contemporary tools. It is anticipated that Anthem will provide a unique opportunity for the non-expert user to analyse and interpret their own in-house or publicly deposited datasets.

TransMut: a program to predict HLA-I peptide binding and optimize mutated peptides for vaccine design by the Transformer-derived self-attention model

“…To compare with previous methods conveniently, we followed the training and evaluation strategy of Anthem 23 , which is the state-of-the-art pHLA binding EPIMHC 52 , MHCBN 53 , and SYFPEITHI 54 ; (ii) Allotype-specific HLA ligands identified by mass spectrometry in previously published studies [55][56][57][58][59][60][61][62][63][64][65][66][67][68][69][70] , and (iii)…”

“…Peptide binders from training datasets of other pHLA binding prediction tools 44,51,[71][72][73][74][75][76][77][78][79][80][81][82] . The external test set is experimentally verified by Anthem 23 .…”

“…In this study, the pHLA binding data (that is, positive data) are obtained from Anthem 23 , which can be downloaded from https://github.com/17shutao/Anthem/tree/master/Dataset. The negative data is generated in a similar way as in previous studies 8,9,49 .…”

“…To follow the previous studies 8,23 for pHLA binding prediction, we conducted the 5-fold cross-validation (CV) and independent test. Since the source of the independent test set and the training set are the same, the data distributions between the training set and independent test set are very similar (see Supplementary Figures 9 and 10).…”

“…The above four submodules together constitute the whole model. We compared TransPHLA with 11 previous PHLA binding prediction methods, including the state-of-the-art method 23 , IEDB recommended method 9 , and 9 benchmark methods 9,[24][25][26][27][28][29][30][31] .…”