UC Santa Cruz

Major Histocompatibility Complex (MHC) class I molecules present intracellular peptides for surveillance by cytotoxic T cells. To fully understand the mechanism of T cell recognition and prioritize different therapeutic targets, it is of practical relevance to first characterize the surface features of peptide/MHC (pMHC) complexes which requires high-resolution structural models. In this work, I present RosettaMHC, a high-throughput pMHC homology modeling approach, which utilizes structural templates from the Protein Data Bank (PDB) selected using several strategies. The accuracy of a peptide backbone is primarily dependent on the selection of a closest template. Here, we have explored template selection based on (i) the peptide sequence, and (ii) the MHC groove sequence which allow us to obtain models within 1.5 Å and 1 Å respectively from their native X-ray structures. In addition, we have added Artificial Neural Network (ANN) models to filter structures that are potentially inaccurate thereby increasing throughput while helping us select models within 1 Å from their corresponding natives. We applied RosettaMHC to model structures of (i) anaplastic lymphoma kinase neoepitopes found in neuroblastoma cancer patients in complex with 2,904 HLA alleles to identify putative alleles that may display these peptides, and (ii) SARS-CoV-2 peptide/HLA-A*02:01 complexes that can be utilized as candidates to generate pMHC tetramers to probe T cells and possibly as vaccine targets. Through this work, we want to establish that, for the most frequent alleles, we have sufficient knowledge from the existing databases to predict sub-angstrom accuracy models thereby reducing the efforts required to experimentally determine pMHC structures. Alongside RosettaMHC, I (i) helped develop resonance assignment methods that are used to interpret NMR data, (ii) worked with alternative protocols in Rosetta to solve structures of biologically important protein targets of varying sizes and complexities with the help of sparse NMR data, and (iii) built a method to estimate side chain conformational entropy using boolean satisfiability and applied it to understand dynamics of the therapeutic cytokine, Interleukin-2, which helped characterize a minor conformational state that is relevant for drug design. Although the Rosetta energy function together with ANN-based scores help us eliminate inaccurate peptide backbones, poor templates may still be locally optimal, leading to imprecise modeling results in some cases. In the future, we want to employ our satisfiability-based side chain packing method towards a more accurate peptide backbone identification. I believe that this work can provide a rational approach for high-throughput modeling of pMHC targets with desired recognition features which are relevant for cancer immunotherapy and infectious diseases.