GradDock: rapid simulation and tailored ranking functions for peptide-MHC Class I docking

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.

show abstract

“…We plan to continuously expand the capabilities of HLA-Arena by providing support for additional tools. 29 - 31 …”

Section: Methodsmentioning

confidence: 99%

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

Antunes

Abella

Hall-Swan

et al. 2020

JCO Clinical Cancer Informatics

View full text Add to dashboard Cite

show abstract

“…Structure-based approaches have also been proposed to model the bound peptide conformation de novo [reviewed in (13)]. These approaches utilize various algorithms to optimize the backbone and side chain degrees of freedom of the peptide/MHC structure according to a scoring function, derived from physical principles (14)(15)(16), that can be further enhanced using modified scoring terms (17) or mean field theory (18). While these methods do not rely on large training data sets, their performance is affected by bottlenecks in sampling of different backbone conformations, and any possible structural adaptations of the HLA peptide-binding groove.…”

Section: Introductionmentioning

confidence: 99%

Structure-Based Modeling of SARS-CoV-2 Peptide/HLA-A02 Antigens

Nerli

Sgourakis

2020

Front. Med. Technol.

View full text Add to dashboard Cite

SARS-CoV-2-specific CD4 and CD8 T cells have been shown to be present in individuals with acute, mild, and asymptomatic Coronavirus disease (COVID-19). Toward the development of diagnostic and therapeutic tools to fight COVID-19, it is important to predict and characterize T cell epitopes expressed by SARS-CoV-2. Here, we use RosettaMHC, a comparative modeling approach which leverages existing structures of peptide/MHC complexes available in the Protein Data Bank, to derive accurate 3D models for putative SARS-CoV-2 CD8 epitopes. We outline an application of our method to model 8–10 residue epitopic peptides predicted to bind to the common allele HLA-A * 02:01, and we make our models publicly available through an online database ( https://rosettamhc.chemistry.ucsc.edu ). We further compare electrostatic surfaces with models of homologous peptide/HLA-A * 02:01 complexes from human common cold coronavirus strains to identify epitopes which may be recognized by a shared pool of cross-reactive TCRs. As more detailed studies on antigen-specific T cell recognition become available, RosettaMHC models can be used to understand the link between peptide/HLA complex structure and surface chemistry with immunogenicity, in the context of SARS-CoV-2 infection.

show abstract

“…[20][21][22][23][24][25][26][27][28][29]. These methods are also known as "ab initio" methods because they do not require the experimental HLA binding data.…”

Section: Introductionmentioning

confidence: 99%

Peptide-Binding Groove Contraction Linked to the Lack of T Cell Response: Using Complex Structure and Energy To Identify Neoantigens

et al. 2018

View full text Add to dashboard Cite

Using personalized peptide vaccines (PPVs) to target tumor-specific nonself-antigens (neoantigens) is a promising approach to cancer treatment. However, the development of PPVs is hindered by the challenge of identifying tumor-specific neoantigens, in part because current in silico methods for identifying such neoantigens have limited effectiveness. In this article, we report the results of molecular dynamics simulations of 12 oligopeptides bound with an HLA, revealing a previously unrecognized association between the inability of an oligopeptide to elicit a T cell response and the contraction of the peptide-binding groove upon binding of the oligopeptide to the HLA. Our conformational analysis showed that this association was due to incompatibility at the interface between the contracted groove and its αβ–T cell Ag receptor. This structural demonstration that having the capability to bind HLA does not guarantee immunogenicity prompted us to develop an atom-based method (SEFF12MC) to predict immunogenicity through using the structure and energy of a peptide·HLA complex to assess the propensity of the complex for further complexation with its TCR. In predicting the immunogenicities of the 12 oligopeptides, SEFF12MC achieved a 100% success rate, compared with success rates of 25–50% for 11 publicly available residue-based methods including NetMHC-4.0. Although further validation and refinements of SEFF12MC are required, our results suggest a need to develop in silico methods that assess peptide characteristics beyond their capability to form stable binary complexes with HLAs to help remove hurdles in using the patient tumor DNA information to develop PPVs for personalized cancer immunotherapy.

show abstract

GradDock: rapid simulation and tailored ranking functions for peptide-MHC Class I docking

Abstract: Supplementary data are available at Bioinformatics online.

Cited by 25 publications

References 60 publications

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

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

Structure-Based Modeling of SARS-CoV-2 Peptide/HLA-A02 Antigens

Peptide-Binding Groove Contraction Linked to the Lack of T Cell Response: Using Complex Structure and Energy To Identify Neoantigens

Contact Info

Product

Resources

About