HLA-Arena: a customizable environment for the structural modeling and analysis of peptide-HLA complexes for cancer immunotherapy

Dinler A. Antunes; Jayvee R. Abella; Sarah Hall-Swan; Didier Devaurs; Anja Conev; Mark Moll; Gregory Lizée; Lydia E. Kavraki

doi:10.1200/CCI.19.00123

HLA-Arena: a customizable environment for the structural modeling and analysis of peptide-HLA complexes for cancer immunotherapy

Dinler A. Antunes, Jayvee R. Abella, Sarah Hall-Swan, Didier Devaurs, Anja Conev, Mark Moll, Gregory Lizée, Lydia E. Kavraki^*

^*Corresponding author for this work

Computer And Information Sciences

Research output: Contribution to journal › Article › peer-review

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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.

Original language	English
Pages (from-to)	623-636
Number of pages	14
Journal	JCO Clinical Cancer Informatics
Volume	4
DOIs	https://doi.org/10.1200/CCI.19.00123
Publication status	Published - 15 Jul 2020

Funding

Supported in part by National Institutes of Health Grant No. 1R21CA209941-01 through the Informatics Technology for Cancer Research initiative of the National Cancer Institute; by the Cancer Prevention and Research Institute of Texas through Award No. RP170508; by a fellowship from the Gulf Cost Consortia on the Computational Cancer Biology Training Program (Grant No. RP170593); by a training fellowship from the National Library of Medicine Training Program in Biomedical Informatics (Grant No. T15LM007093); by funds from Rice University; and by a Big-Data Private-Cloud Research Cyberinfrastructure Major Research Instrumentation Program award, funded by the National Science Foundation (NSF) under Grant No. CNS-1338099. This work used the Extreme Science and Engineering Discovery Environment, which is supported by NSF Grant No. ACI-1548562; more specifically, the work involved the Stampede cluster at the Texas Advanced Computing Center, funded through Allocation No. MCB180187.

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 3 Good Health and Well-being

Keywords

HLA protein receptors
cancer immunotherapy
computational methods

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10.1200/CCI.19.00123Licence: CC BY 4.0

Antunes-etal-JCOCCI-2020-HLA-Arena-a-customizable-environment-for-the-structural-modeling-and-analysis-of-peptide-HLA-complexesFinal published version, 1.82 MBLicence: CC BY 4.0

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title = "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.",

keywords = "HLA protein receptors, cancer immunotherapy, computational methods",

author = "Antunes, \{Dinler A.\} and Abella, \{Jayvee R.\} and Sarah Hall-Swan and Didier Devaurs and Anja Conev and Mark Moll and Gregory Liz{\'e}e and Kavraki, \{Lydia E.\}",

year = "2020",

month = jul,

day = "15",

doi = "10.1200/CCI.19.00123",

language = "English",

volume = "4",

pages = "623--636",

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TY - JOUR

T1 - HLA-Arena

T2 - a customizable environment for the structural modeling and analysis of peptide-HLA complexes for cancer immunotherapy

AU - Antunes, Dinler A.

AU - Abella, Jayvee R.

AU - Hall-Swan, Sarah

AU - Devaurs, Didier

AU - Conev, Anja

AU - Moll, Mark

AU - Lizée, Gregory

AU - Kavraki, Lydia E.

PY - 2020/7/15

Y1 - 2020/7/15

N2 - 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.

AB - 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.

KW - HLA protein receptors

KW - cancer immunotherapy

KW - computational methods

UR - https://github.com/KavrakiLab/hla-arena

U2 - 10.1200/CCI.19.00123

DO - 10.1200/CCI.19.00123

M3 - Article

C2 - 32667823

AN - SCOPUS:85088156625

VL - 4

SP - 623

EP - 636

JO - JCO Clinical Cancer Informatics

JF - JCO Clinical Cancer Informatics

ER -

HLA-Arena: a customizable environment for the structural modeling and analysis of peptide-HLA complexes for cancer immunotherapy

Abstract

Funding

UN SDGs

Keywords

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