Browsing by Author "Fasoulis, Romanos"

Now showing 1 - 3 of 3
  • Thumbnail Image
    Item
    Charge-based interactions through peptide position 4 drive diversity of antigen presentation by human leukocyte antigen class I molecules
    (Oxford University Press, 2022) Jackson, Kyle R; Antunes, Dinler A; Talukder, Amjad H; Maleki, Ariana R; Amagai, Kano; Salmon, Avery; Katailiha, Arjun S; Chiu, Yulun; Fasoulis, Romanos; Rigo, Maurício Menegatti; Abella, Jayvee R; Melendez, Brenda D; Li, Fenge; Sun, Yimo; Sonnemann, Heather M; Belousov, Vladislav; Frenkel, Felix; Justesen, Sune; Makaju, Aman; Liu, Yang; Horn, David; Lopez-Ferrer, Daniel; Huhmer, Andreas F; Hwu, Patrick; Roszik, Jason; Hawke, David; Kavraki, Lydia E; Lizée, Gregory
    Human leukocyte antigen class I (HLA-I) molecules bind and present peptides at the cell surface to facilitate the induction of appropriate CD8+ T cell-mediated immune responses to pathogen- and self-derived proteins. The HLA-I peptide-binding cleft contains dominant anchor sites in the B and F pockets that interact primarily with amino acids at peptide position 2 and the C-terminus, respectively. Nonpocket peptide–HLA interactions also contribute to peptide binding and stability, but these secondary interactions are thought to be unique to individual HLA allotypes or to specific peptide antigens. Here, we show that two positively charged residues located near the top of peptide-binding cleft facilitate interactions with negatively charged residues at position 4 of presented peptides, which occur at elevated frequencies across most HLA-I allotypes. Loss of these interactions was shown to impair HLA-I/peptide binding and complex stability, as demonstrated by both in vitro and in silico experiments. Furthermore, mutation of these Arginine-65 (R65) and/or Lysine-66 (K66) residues in HLA-A*02:01 and A*24:02 significantly reduced HLA-I cell surface expression while also reducing the diversity of the presented peptide repertoire by up to 5-fold. The impact of the R65 mutation demonstrates that nonpocket HLA-I/peptide interactions can constitute anchor motifs that exert an unexpectedly broad influence on HLA-I-mediated antigen presentation. These findings provide fundamental insights into peptide antigen binding that could broadly inform epitope discovery in the context of viral vaccine development and cancer immunotherapy.
  • Thumbnail Image
    Item
    SARS-Arena: Sequence and Structure-Guided Selection of Conserved Peptides from SARS-related Coronaviruses for Novel Vaccine Development
    (Frontiers Media S.A., 2022) Rigo, Mauricio Menegatti; Fasoulis, Romanos; Conev, Anja; Hall-Swan, Sarah; Antunes, Dinler Amaral; Kavraki, Lydia E.; Kavraki Lab
    The pandemic caused by the SARS-CoV-2 virus, the agent responsible for the COVID-19 disease, has affected millions of people worldwide. There is constant search for new therapies to either prevent or mitigate the disease. Fortunately, we have observed the successful development of multiple vaccines. Most of them are focused on one viral envelope protein, the spike protein. However, such focused approaches may contribute for the rise of new variants, fueled by the constant selection pressure on envelope proteins, and the widespread dispersion of coronaviruses in nature. Therefore, it is important to examine other proteins, preferentially those that are less susceptible to selection pressure, such as the nucleocapsid (N) protein. Even though the N protein is less accessible to humoral response, peptides from its conserved regions can be presented by class I Human Leukocyte Antigen (HLA) molecules, eliciting an immune response mediated by T-cells. Given the increased number of protein sequences deposited in biological databases daily and the N protein conservation among viral strains, computational methods can be leveraged to discover potential new targets for SARS-CoV-2 and SARS-CoV-related viruses. Here we developed SARS-Arena, a user-friendly computational pipeline that can be used by practitioners of different levels of expertise for novel vaccine development. SARS-Arena combines sequence-based methods and structure-based analyses to (i) perform multiple sequence alignment (MSA) of SARS-CoV-related N protein sequences, (ii) recover candidate peptides of different lengths from conserved protein regions, and (iii) model the 3D structure of the conserved peptides in the context of different HLAs. We present two main Jupyter Notebook workflows that can help in the identification of new T-cell targets against SARS-CoV viruses. In fact, in a cross-reactive case study, our workflows identified a conserved N protein peptide (SPRWYFYYL) recognized by CD8+ T-cells in the context of HLA-B7+. SARS-Arena is available at https://github.com/KavrakiLab/SARS-Arena.
  • Thumbnail Image
    ItemEmbargo
    Sequence-based and structure-based methods for studying the adaptive immune response
    (2024-08-23) Fasoulis, Romanos; Kavraki, Lydia E.
    The adaptive immune system comprises various biological mechanisms that, in unison, protect an organism against various threats, such as pathogens, viral infections, and tumor cells. One of such mechanisms involves the binding of intracellular protein fragments called peptides to class-I Major Histocompability Complexes (MHCs). The formed peptide-MHC (pMHC) complex is presented to the surface of the cell, where it interacts with the T-cell receptor, an interaction that can elicit an immune response. Knowing which peptides bind to MHCs, which peptides are presented to the surface of the cell, and which peptides elicit an immune response is crucial for successful clinical applications and therapies. Due to the advent of mass spectrometry resulting in high-throughput generation of amino acid sequence-based pMHC binding data, amino acid sequence-based Machine Learning (ML) approaches have dominated the field, showing immense potential. At the same time however, it is known that the pMHC interaction is characterized by a strong structural component that is shown to be extremely important in fully explaining pMHC binding and peptide immunogenicity. This thesis presents methodologies that attend to both the amino acid sequence component and the structural component of the pMHC interaction. Focusing on the sequence component first, we present TLStab and TLImm, two ML-based tools that predict peptide kinetic stability and peptide immunogenicity respectively. Developed through adopting transfer learning methodologies, TLStab and TLImm outperform state-of-the-art approaches in their respective tasks. Next, focusing on the structural component, we present APE-Gen2.0, a new pMHC structural modeling tool. APE-Gen2.0 outperforms other approaches in the literature in regard to modeling accuracy. It also expands the pMHC structural modeling repertoire to peptides exhibiting post-translational modifications, as well as peptides that assume non-canonical geometries in the MHC binding cleft. Finally, we present RankMHC, a novel, Learning to Rank-based pMHC binding mode identification tool. RankMHC outperforms both classical protein-ligand scoring functions and pMHC-specific scoring functions in predicting the most representative peptide conformation among an ensemble of conformations. Overall, acknowledging the potential of both pMHC sequence and pMHC structure information, our work expands on both areas, through novel and effective computational contributions.