Structure-based modeling of SARS-CoV-2 peptide/HLA-A02 antigens

Santrupti Nerli; Nikolaos G Sgourakis

groove (termed A-F pockets) define a repertoire of 10 4 -10 6 peptide antigens that can be recognized 48 by each HLA allotype (9, 10). Several machine-learning methods have been developed to predict 49 the likelihood that a target peptide will bind to a given allele (reviewed in (11)). Generally these 50 methods make use of available data sets in the Immune Epitope Database (12) to train artificial 51 neural networks that predict peptide processing, binding and display, and their performance varies 52 depending on peptide length and HLA allele representation in the database. Structure-based 53 approaches have also been proposed to model the bound peptide conformation de novo (reviewed 54 in (13)). These approaches utilize various algorithms to optimize the backbone and side chain 55 degrees of freedom of the peptide/MHC structure according to an all-atom scoring function, 56 derived from physical principles (14-16), that can be further enhanced using modified scoring 57 terms (17) or mean field theory (18). While these methods do not rely on large training data sets, 58 their performance is affected by bottlenecks in sampling of different backbone conformations, and 59 any possible structural adaptations of the HLA peptide-binding groove. 60 Predicting the bound peptide conformation whose N-and C-termini are anchored within a fixed-61 length groove is a tractable modeling problem that can be addressed using standard comparative suggesting that a similar principle can be applied to produce models of candidate epitopes directly 72 from the proteome of a pathogen of interest. Here, we apply RosettaMHC to all HLA-A*02:01 73 epitopes predicted directly from the ~30 kbp SARS-CoV-2 genome, and make our models publicly 74 available through an online database. The computed binding energies of our models can be used 75 as an additional validation layer to select high-affinity epitopes from large peptide sets. As detailed 76 epitope mapping data from high-throughput tetramer staining (23-25) and T cell functional 77 screens (26) become available, the models presented here can provide a toehold for understanding 78 links between pMHC-I antigen structure and immunogenicity, with actionable value for the 79 development of peptide vaccines to combat the disease. 80 author/funder. All rights reserved. No reuse allowed without permission.