HLA-Arena: A Customizable Environment for the Structural Modeling and Analysis of Peptide-HLA Complexes for Cancer Immunotherapy

Abstract

PURPOSE HLA protein receptors play a key role in cellular immunity. They bind intracellular peptides and display them for recognition by T-cell lymphocytes. Because T-cell activation is partially driven by structural features of these peptide-HLA complexes, their structural modeling and analysis are becoming central components of cancer immunotherapy projects. Unfortunately, this kind of analysis is limited by the small number of experimentally determined structures of peptide-HLA complexes. Overcoming this limitation requires developing novel computational methods to model and analyze peptide-HLA structures. METHODS Here we describe a new platform for the structural modeling and analysis of peptide-HLA complexes, called HLA-Arena, which we have implemented using Jupyter Notebook and Docker. It is a customizable environment that facilitates the use of computational tools, such as APE-Gen and DINC, which we have previously applied to peptide-HLA complexes. By integrating other commonly used tools, such as MODELLER and MHCflurry, this environment includes support for diverse tasks in structural modeling, analysis, and visualization. RESULTS To illustrate the capabilities of HLA-Arena, we describe 3 example workflows applied to peptide-HLA complexes. Leveraging the strengths of our tools, DINC and APE-Gen, the first 2 workflows show how to perform geometry prediction for peptide-HLA complexes and structure-based binding prediction, respectively. The third workflow presents an example of large-scale virtual screening of peptides for multiple HLA alleles. CONCLUSION These workflows illustrate the potential benefits of HLA-Arena for the structural modeling and analysis of peptide-HLA complexes. Because HLA-Arena can easily be integrated within larger computational pipelines, we expect its potential impact to vastly increase. For instance, it could be used to conduct structural analyses for personalized cancer immunotherapy, neoantigen discovery, or vaccine development.

Immunogenic T cell epitopes of SARS-CoV-2 are recognized by circulating memory and naïve CD8 T cells of unexposed individuals

“…We further estimated the binding affinity of each peptide to HLA-A*02:01 by docking studies. Molecular docking between the epitope library and HLA-A*02:01 was conducted by DockTope [27] and HLA-Arena [28] . The binding energy (ΔG) of HLA-A*02:01/epitope complexes was calculated using the PRODIGY server [29] .…”

Markov state modeling reveals alternative unbinding pathways for peptide–MHC complexes

“…Such analysis can also be done for any other peptide-MHC of interest, providing an initial 3D structure of the complex. In the absence of a crystal structure, an appropriate 3D model could be used, and our group has also contributed tools for this particular task (13,14). The computational cost to build the MSMs was manageable and was done using local GPU computing clusters (about 10,000 GPU-hours compared to 115,000 GPU-hours in ref.…”

“…Moving beyond a simple measurement or prediction of binding, uncovering the molecular mechanisms for strong binding usually starts with an analysis of a structure of the bound complex. Structures can be from one of the few hundred crystal structures available at the Protein Data Bank (PDB) or modeled with a docking-based approach (9)(10)(11)(12)(13)(14). However, an analysis of a single conformation may be misleading due to the flexibility of the structure (15), and the dynamics of peptide-MHC binding must be probed.…”

Large-Scale Structure-Based Screening of Potential T Cell Cross-Reactivities Involving Peptide-Targets From BCG Vaccine and SARS-CoV-2

“…This step defined the set of pHLA complexes that should be modeled in the next phase. The implementation of this phase uses the HLA-Arena tool ( 43 ). After setting up our environment, we implemented different processing pipelines, each one on a separate python script, using as reference the “virtual screening” notebook from HLA-Arena (i.e., 3_virtual_screening.ipynb).…”