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Tarabini RF, Fioravanti Vieira G, Rigo MM, de Souza APD. Mutations in glioblastoma proteins do not disrupt epitope presentation and recognition, maintaining a specific CD8 T cell immune response potential. Sci Rep 2024; 14:16721. [PMID: 39030304 PMCID: PMC11271619 DOI: 10.1038/s41598-024-67099-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/28/2024] [Accepted: 07/08/2024] [Indexed: 07/21/2024] Open
Abstract
Antigen-specific cytotoxic CD8 T cells are extremely effective in controlling tumor growth and have been the focus of immunotherapy approaches. We leverage in silico tools to investigate whether the occurrence of mutations in proteins previously described as immunogenic and highly expressed by glioblastoma multiforme (GBM), such as Epidermal Growth Factor Receptor (EGFR), Isocitrate Dehydrogenase 1 (IDH1), Phosphatase and Tensin homolog (PTEN) and Tumor Protein 53 (TP53), may be contributing to the differential presentation of immunogenic epitopes. We recovered Class I MHC binding information from wild-type and mutated proteins using the Immune Epitope Database (IEDB). After that, we built peptide-MHC (pMHC-I) models in HLA-arena, followed by hierarchical clustering analysis based on electrostatic surface features from each complex. We identified point mutations that are determinants for the presentation of a set of peptides from TP53 protein. We point to structural features in the pMHC-I complexes of wild-type and mutated peptides, which may play a role in the recognition of CD8 T cells. To further explore these features, we performed 100 ns molecular dynamics simulations for the peptide pairs (wt/mut) selected. In pursuit of novel therapeutic targets for GBM treatment, we selected peptides where our predictive results indicated that mutations would not disrupt epitope presentation, thereby maintaining a specific CD8 T cell immune response. These peptides hold potential for future GBM interventions, including peptide-based or mRNA vaccine development applications.
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Affiliation(s)
- Renata Fioravanti Tarabini
- Laboratory of Clinical and Experimental Immunology, Infant Center, School of Health Science, Pontifical Catholic University of Rio Grande do Sul (PUCRS), Porto Alegre, Brazil
| | - Gustavo Fioravanti Vieira
- Post-Graduation Program in Genetics and Molecular Biology, Universidade Federal do Rio Grande do Sul, Porto Alegre, Brazil
- Post-Graduation Program in Health and Human Development, Universidade La Salle, Canoas, Brazil
| | - Maurício Menegatti Rigo
- Kavraki Lab, Department of Computer Science, Rice University, Houston, TX, USA.
- Center for Discovery and Innovation, Hackensack Meridian Health, Nutley, NJ, USA.
| | - Ana Paula Duarte de Souza
- Laboratory of Clinical and Experimental Immunology, Infant Center, School of Health Science, Pontifical Catholic University of Rio Grande do Sul (PUCRS), Porto Alegre, Brazil.
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2
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Shao W, Yao Y, Yang L, Li X, Ge T, Zheng Y, Zhu Q, Ge S, Gu X, Jia R, Song X, Zhuang A. Novel insights into TCR-T cell therapy in solid neoplasms: optimizing adoptive immunotherapy. Exp Hematol Oncol 2024; 13:37. [PMID: 38570883 PMCID: PMC10988985 DOI: 10.1186/s40164-024-00504-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/08/2023] [Accepted: 03/21/2024] [Indexed: 04/05/2024] Open
Abstract
Adoptive immunotherapy in the T cell landscape exhibits efficacy in cancer treatment. Over the past few decades, genetically modified T cells, particularly chimeric antigen receptor T cells, have enabled remarkable strides in the treatment of hematological malignancies. Besides, extensive exploration of multiple antigens for the treatment of solid tumors has led to clinical interest in the potential of T cells expressing the engineered T cell receptor (TCR). TCR-T cells possess the capacity to recognize intracellular antigen families and maintain the intrinsic properties of TCRs in terms of affinity to target epitopes and signal transduction. Recent research has provided critical insight into their capability and therapeutic targets for multiple refractory solid tumors, but also exposes some challenges for durable efficacy. In this review, we describe the screening and identification of available tumor antigens, and the acquisition and optimization of TCRs for TCR-T cell therapy. Furthermore, we summarize the complete flow from laboratory to clinical applications of TCR-T cells. Last, we emerge future prospects for improving therapeutic efficacy in cancer world with combination therapies or TCR-T derived products. In conclusion, this review depicts our current understanding of TCR-T cell therapy in solid neoplasms, and provides new perspectives for expanding its clinical applications and improving therapeutic efficacy.
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Affiliation(s)
- Weihuan Shao
- Department of Ophthalmology, Ninth People's Hospital, Shanghai JiaoTong University School of Medicine, 639 Zhi Zao Ju Road, Shanghai Ninth People's Hospital, Shanghai, 200011, People's Republic of China
- Shanghai Key Laboratory of Orbital Diseases and Ocular Oncology, Shanghai, 200011, People's Republic of China
| | - Yiran Yao
- Department of Ophthalmology, Ninth People's Hospital, Shanghai JiaoTong University School of Medicine, 639 Zhi Zao Ju Road, Shanghai Ninth People's Hospital, Shanghai, 200011, People's Republic of China
- Shanghai Key Laboratory of Orbital Diseases and Ocular Oncology, Shanghai, 200011, People's Republic of China
| | - Ludi Yang
- Department of Ophthalmology, Ninth People's Hospital, Shanghai JiaoTong University School of Medicine, 639 Zhi Zao Ju Road, Shanghai Ninth People's Hospital, Shanghai, 200011, People's Republic of China
- Shanghai Key Laboratory of Orbital Diseases and Ocular Oncology, Shanghai, 200011, People's Republic of China
| | - Xiaoran Li
- Department of Ophthalmology, Ninth People's Hospital, Shanghai JiaoTong University School of Medicine, 639 Zhi Zao Ju Road, Shanghai Ninth People's Hospital, Shanghai, 200011, People's Republic of China
- Shanghai Key Laboratory of Orbital Diseases and Ocular Oncology, Shanghai, 200011, People's Republic of China
| | - Tongxin Ge
- Department of Ophthalmology, Ninth People's Hospital, Shanghai JiaoTong University School of Medicine, 639 Zhi Zao Ju Road, Shanghai Ninth People's Hospital, Shanghai, 200011, People's Republic of China
- Shanghai Key Laboratory of Orbital Diseases and Ocular Oncology, Shanghai, 200011, People's Republic of China
| | - Yue Zheng
- Department of Ophthalmology, Ninth People's Hospital, Shanghai JiaoTong University School of Medicine, 639 Zhi Zao Ju Road, Shanghai Ninth People's Hospital, Shanghai, 200011, People's Republic of China
- Shanghai Key Laboratory of Orbital Diseases and Ocular Oncology, Shanghai, 200011, People's Republic of China
| | - Qiuyi Zhu
- Department of Ophthalmology, Ninth People's Hospital, Shanghai JiaoTong University School of Medicine, 639 Zhi Zao Ju Road, Shanghai Ninth People's Hospital, Shanghai, 200011, People's Republic of China
- Shanghai Key Laboratory of Orbital Diseases and Ocular Oncology, Shanghai, 200011, People's Republic of China
| | - Shengfang Ge
- Department of Ophthalmology, Ninth People's Hospital, Shanghai JiaoTong University School of Medicine, 639 Zhi Zao Ju Road, Shanghai Ninth People's Hospital, Shanghai, 200011, People's Republic of China
- Shanghai Key Laboratory of Orbital Diseases and Ocular Oncology, Shanghai, 200011, People's Republic of China
| | - Xiang Gu
- Department of Ophthalmology, Ninth People's Hospital, Shanghai JiaoTong University School of Medicine, 639 Zhi Zao Ju Road, Shanghai Ninth People's Hospital, Shanghai, 200011, People's Republic of China
- Shanghai Key Laboratory of Orbital Diseases and Ocular Oncology, Shanghai, 200011, People's Republic of China
| | - Renbing Jia
- Department of Ophthalmology, Ninth People's Hospital, Shanghai JiaoTong University School of Medicine, 639 Zhi Zao Ju Road, Shanghai Ninth People's Hospital, Shanghai, 200011, People's Republic of China.
- Shanghai Key Laboratory of Orbital Diseases and Ocular Oncology, Shanghai, 200011, People's Republic of China.
| | - Xin Song
- Department of Ophthalmology, Ninth People's Hospital, Shanghai JiaoTong University School of Medicine, 639 Zhi Zao Ju Road, Shanghai Ninth People's Hospital, Shanghai, 200011, People's Republic of China.
- Shanghai Key Laboratory of Orbital Diseases and Ocular Oncology, Shanghai, 200011, People's Republic of China.
| | - Ai Zhuang
- Department of Ophthalmology, Ninth People's Hospital, Shanghai JiaoTong University School of Medicine, 639 Zhi Zao Ju Road, Shanghai Ninth People's Hospital, Shanghai, 200011, People's Republic of China.
- Shanghai Key Laboratory of Orbital Diseases and Ocular Oncology, Shanghai, 200011, People's Republic of China.
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3
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Conev A, Fasoulis R, Hall-Swan S, Ferreira R, Kavraki LE. HLAEquity: Examining biases in pan-allele peptide-HLA binding predictors. iScience 2024; 27:108613. [PMID: 38188519 PMCID: PMC10770483 DOI: 10.1016/j.isci.2023.108613] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/08/2023] [Revised: 11/13/2023] [Accepted: 11/29/2023] [Indexed: 01/09/2024] Open
Abstract
Peptide-HLA (pHLA) binding prediction is essential in screening peptide candidates for personalized peptide vaccines. Machine learning (ML) pHLA binding prediction tools are trained on vast amounts of data and are effective in screening peptide candidates. Most ML models report the ability to generalize to HLA alleles unseen during training ("pan-allele" models). However, the use of datasets with imbalanced allele content raises concerns about biased model performance. First, we examine the data bias of two ML-based pan-allele pHLA binding predictors. We find that the pHLA datasets overrepresent alleles from geographic populations of high-income countries. Second, we show that the identified data bias is perpetuated within ML models, leading to algorithmic bias and subpar performance for alleles expressed in low-income geographic populations. We draw attention to the potential therapeutic consequences of this bias, and we challenge the use of the term "pan-allele" to describe models trained with currently available public datasets.
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Affiliation(s)
- Anja Conev
- Department of Computer Science, Rice University, Houston, TX, USA
| | - Romanos Fasoulis
- Department of Computer Science, Rice University, Houston, TX, USA
| | - Sarah Hall-Swan
- Department of Computer Science, Rice University, Houston, TX, USA
| | - Rodrigo Ferreira
- Department of Computer Science, Rice University, Houston, TX, USA
| | - Lydia E. Kavraki
- Department of Computer Science, Rice University, Houston, TX, USA
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4
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Sobral PS, Luz VCC, Almeida JMGCF, Videira PA, Pereira F. Computational Approaches Drive Developments in Immune-Oncology Therapies for PD-1/PD-L1 Immune Checkpoint Inhibitors. Int J Mol Sci 2023; 24:ijms24065908. [PMID: 36982981 PMCID: PMC10054797 DOI: 10.3390/ijms24065908] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2023] [Revised: 03/16/2023] [Accepted: 03/19/2023] [Indexed: 03/30/2023] Open
Abstract
Computational approaches in immune-oncology therapies focus on using data-driven methods to identify potential immune targets and develop novel drug candidates. In particular, the search for PD-1/PD-L1 immune checkpoint inhibitors (ICIs) has enlivened the field, leveraging the use of cheminformatics and bioinformatics tools to analyze large datasets of molecules, gene expression and protein-protein interactions. Up to now, there is still an unmet clinical need for improved ICIs and reliable predictive biomarkers. In this review, we highlight the computational methodologies applied to discovering and developing PD-1/PD-L1 ICIs for improved cancer immunotherapies with a greater focus in the last five years. The use of computer-aided drug design structure- and ligand-based virtual screening processes, molecular docking, homology modeling and molecular dynamics simulations methodologies essential for successful drug discovery campaigns focusing on antibodies, peptides or small-molecule ICIs are addressed. A list of recent databases and web tools used in the context of cancer and immunotherapy has been compilated and made available, namely regarding a general scope, cancer and immunology. In summary, computational approaches have become valuable tools for discovering and developing ICIs. Despite significant progress, there is still a need for improved ICIs and biomarkers, and recent databases and web tools have been compiled to aid in this pursuit.
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Affiliation(s)
- Patrícia S Sobral
- LAQV and REQUIMTE, Department of Chemistry, NOVA School of Science and Technology, Universidade NOVA de Lisboa, 2829-516 Caparica, Portugal
- UCIBIO, Applied Molecular Biosciences Unit, Department of Life Sciences, NOVA School of Science and Technology, Universidade NOVA de Lisboa, 2829-516 Caparica, Portugal
- Associate Laboratory i4HB-Institute for Health and Bioeconomy, NOVA School of Science and Technology, Universidade NOVA de Lisboa, 2829-516 Caparica, Portugal
| | - Vanessa C C Luz
- UCIBIO, Applied Molecular Biosciences Unit, Department of Life Sciences, NOVA School of Science and Technology, Universidade NOVA de Lisboa, 2829-516 Caparica, Portugal
- Associate Laboratory i4HB-Institute for Health and Bioeconomy, NOVA School of Science and Technology, Universidade NOVA de Lisboa, 2829-516 Caparica, Portugal
| | - João M G C F Almeida
- UCIBIO, Applied Molecular Biosciences Unit, Department of Life Sciences, NOVA School of Science and Technology, Universidade NOVA de Lisboa, 2829-516 Caparica, Portugal
| | - Paula A Videira
- UCIBIO, Applied Molecular Biosciences Unit, Department of Life Sciences, NOVA School of Science and Technology, Universidade NOVA de Lisboa, 2829-516 Caparica, Portugal
- Associate Laboratory i4HB-Institute for Health and Bioeconomy, NOVA School of Science and Technology, Universidade NOVA de Lisboa, 2829-516 Caparica, Portugal
| | - Florbela Pereira
- LAQV and REQUIMTE, Department of Chemistry, NOVA School of Science and Technology, Universidade NOVA de Lisboa, 2829-516 Caparica, Portugal
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5
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Baulu E, Gardet C, Chuvin N, Depil S. TCR-engineered T cell therapy in solid tumors: State of the art and perspectives. SCIENCE ADVANCES 2023; 9:eadf3700. [PMID: 36791198 PMCID: PMC9931212 DOI: 10.1126/sciadv.adf3700] [Citation(s) in RCA: 87] [Impact Index Per Article: 87.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/17/2022] [Accepted: 01/06/2023] [Indexed: 05/25/2023]
Abstract
T cell engineering has changed the landscape of cancer immunotherapy. Chimeric antigen receptor T cells have demonstrated a remarkable efficacy in the treatment of B cell malignancies in hematology. However, their clinical impact on solid tumors has been modest so far. T cells expressing an engineered T cell receptor (TCR-T cells) represent a promising therapeutic alternative. The target repertoire is not limited to membrane proteins, and intrinsic features of TCRs such as high antigen sensitivity and near-to-physiological signaling may improve tumor cell detection and killing while improving T cell persistence. In this review, we present the clinical results obtained with TCR-T cells targeting different tumor antigen families. We detail the different methods that have been developed to identify and optimize a TCR candidate. We also discuss the challenges of TCR-T cell therapies, including toxicity assessment and resistance mechanisms. Last, we share some perspectives and highlight future directions in the field.
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Affiliation(s)
- Estelle Baulu
- Centre de Recherche en Cancérologie de Lyon, Lyon, France
- ErVaccine Technologies, Lyon, France
| | - Célia Gardet
- Centre de Recherche en Cancérologie de Lyon, Lyon, France
| | | | - Stéphane Depil
- Centre de Recherche en Cancérologie de Lyon, Lyon, France
- ErVaccine Technologies, Lyon, France
- Centre Léon Bérard, Lyon, France
- Université Claude Bernard Lyon 1, Lyon, France
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6
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Jani SP, Kumar SP, Mangukia N, Patel SK, Pandya HA, Rawal RM. MHC2AffyPred: A machine-learning approach to estimate affinity of MHC class II peptides based on structural interaction fingerprints. Proteins 2023; 91:277-289. [PMID: 36116110 DOI: 10.1002/prot.26428] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/03/2021] [Revised: 08/03/2022] [Accepted: 08/25/2022] [Indexed: 01/07/2023]
Abstract
Understanding how MHC class II (MHC-II) binding peptides with differing lengths exhibit specific interaction at the core and extended sites within the large MHC-II pocket is a very important aspect of immunological research for designing peptides. Certain efforts were made to generate peptide conformations amenable for MHC-II binding and calculate the binding energy of such complex formation but not directed toward developing a relationship between the peptide conformation in MHC-II structures and the binding affinity (BA) (IC50 ). We present here a machine-learning approach to calculate the BA of the peptides within the MHC-II pocket for HLA-DRA1, HLA-DRB1, HLA-DP, and HLA-DQ allotypes. Instead of generating ensembles of peptide conformations conventionally, the biased mode of conformations was created by considering the peptides in the crystal structures of pMHC-II complexes as the templates, followed by site-directed peptide docking. The structural interaction fingerprints generated from such docked pMHC-II structures along with the Moran autocorrelation descriptors were trained using a random forest regressor specific to each MHC-II peptide lengths (9-19). The entire workflow is automated using Linux shell and Perl scripts to promote the utilization of MHC2AffyPred program to any characterized MHC-II allotypes and is made for free access at https://github.com/SiddhiJani/MHC2AffyPred. The MHC2AffyPred attained better performance (correlation coefficient [CC] of .612-.898) than MHCII3D (.03-.594) and NetMHCIIpan-3.2 (.289-.692) programs in the HLA-DRA1, HLA-DRB1 types. Similarly, the MHC2AffyPred program achieved CC between .91 and .98 for HLA-DP and HLA-DQ peptides (13-mer to 17-mer). Further, a case study on MHC-II binding 15-mer peptides of severe acute respiratory syndrome coronavirus-2 showed very close competency in computing the IC50 values compared to the sequence-based NetMHCIIpan v3.2 and v4.0 programs with a correlation of .998 and .570, respectively.
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Affiliation(s)
- Siddhi P Jani
- Department of Botany, Bioinformatics and Climate Change Impacts Management, School of Sciences, Gujarat University, Ahmedabad, Gujarat, India
| | - Sivakumar Prasanth Kumar
- Department of Botany, Bioinformatics and Climate Change Impacts Management, School of Sciences, Gujarat University, Ahmedabad, Gujarat, India.,Department of Life Science, School of Sciences, Gujarat University, Ahmedabad, Gujarat, India
| | - Naman Mangukia
- Department of Botany, Bioinformatics and Climate Change Impacts Management, School of Sciences, Gujarat University, Ahmedabad, Gujarat, India.,BioInnovations, Mumbai, Maharashtra, India
| | - Saumya K Patel
- Department of Botany, Bioinformatics and Climate Change Impacts Management, School of Sciences, Gujarat University, Ahmedabad, Gujarat, India
| | - Himanshu A Pandya
- Department of Botany, Bioinformatics and Climate Change Impacts Management, School of Sciences, Gujarat University, Ahmedabad, Gujarat, India.,Department of Life Science, School of Sciences, Gujarat University, Ahmedabad, Gujarat, India
| | - Rakesh M Rawal
- Department of Life Science, School of Sciences, Gujarat University, Ahmedabad, Gujarat, India
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7
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Rigo MM, Fasoulis R, Conev A, Hall-Swan S, Antunes DA, Kavraki LE. SARS-Arena: Sequence and Structure-Guided Selection of Conserved Peptides from SARS-related Coronaviruses for Novel Vaccine Development. Front Immunol 2022; 13:931155. [PMID: 35903104 PMCID: PMC9315150 DOI: 10.3389/fimmu.2022.931155] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/28/2022] [Accepted: 06/10/2022] [Indexed: 02/01/2023] Open
Abstract
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.
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Affiliation(s)
| | - Romanos Fasoulis
- Kavraki Lab, Department of Computer Science, Rice University, Houston, TX, United States
| | - Anja Conev
- Kavraki Lab, Department of Computer Science, Rice University, Houston, TX, United States
| | - Sarah Hall-Swan
- Kavraki Lab, Department of Computer Science, Rice University, Houston, TX, United States
| | - Dinler Amaral Antunes
- Antunes Lab, Center for Nuclear Receptors and Cell Signaling, Department of Biology and Biochemistry, University of Houston, Houston, TX, United States,*Correspondence: Lydia E. Kavraki, ; Dinler Amaral Antunes,
| | - Lydia E. Kavraki
- Kavraki Lab, Department of Computer Science, Rice University, Houston, TX, United States,*Correspondence: Lydia E. Kavraki, ; Dinler Amaral Antunes,
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8
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Keller GLJ, Weiss LI, Baker BM. Physicochemical Heuristics for Identifying High Fidelity, Near-Native Structural Models of Peptide/MHC Complexes. Front Immunol 2022; 13:887759. [PMID: 35547730 PMCID: PMC9084917 DOI: 10.3389/fimmu.2022.887759] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/02/2022] [Accepted: 03/29/2022] [Indexed: 11/13/2022] Open
Abstract
There is long-standing interest in accurately modeling the structural features of peptides bound and presented by class I MHC proteins. This interest has grown with the advent of rapid genome sequencing and the prospect of personalized, peptide-based cancer vaccines, as well as the development of molecular and cellular therapeutics based on T cell receptor recognition of peptide-MHC. However, while the speed and accessibility of peptide-MHC modeling has improved substantially over the years, improvements in accuracy have been modest. Accuracy is crucial in peptide-MHC modeling, as T cell receptors are highly sensitive to peptide conformation and capturing fine details is therefore necessary for useful models. Studying nonameric peptides presented by the common class I MHC protein HLA-A*02:01, here we addressed a key question common to modern modeling efforts: from a set of models (or decoys) generated through conformational sampling, which is best? We found that the common strategy of decoy selection by lowest energy can lead to substantial errors in predicted structures. We therefore adopted a data-driven approach and trained functions capable of predicting near native decoys with exceptionally high accuracy. Although our implementation is limited to nonamer/HLA-A*02:01 complexes, our results serve as an important proof of concept from which improvements can be made and, given the significance of HLA-A*02:01 and its preference for nonameric peptides, should have immediate utility in select immunotherapeutic and other efforts for which structural information would be advantageous.
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Affiliation(s)
- Grant L J Keller
- Department of Chemistry & Biochemistry and the Harper Cancer Research Institute, University of Notre Dame, Notre Dame, IN, United States
| | - Laura I Weiss
- Department of Chemistry & Biochemistry and the Harper Cancer Research Institute, University of Notre Dame, Notre Dame, IN, United States
| | - Brian M Baker
- Department of Chemistry & Biochemistry and the Harper Cancer Research Institute, University of Notre Dame, Notre Dame, IN, United States
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9
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Marzella DF, Parizi FM, van Tilborg D, Renaud N, Sybrandi D, Buzatu R, Rademaker DT, ‘t Hoen PAC, Xue LC. PANDORA: A Fast, Anchor-Restrained Modelling Protocol for Peptide: MHC Complexes. Front Immunol 2022; 13:878762. [PMID: 35619705 PMCID: PMC9127323 DOI: 10.3389/fimmu.2022.878762] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/18/2022] [Accepted: 04/07/2022] [Indexed: 11/21/2022] Open
Abstract
Deeper understanding of T-cell-mediated adaptive immune responses is important for the design of cancer immunotherapies and antiviral vaccines against pandemic outbreaks. T-cells are activated when they recognize foreign peptides that are presented on the cell surface by Major Histocompatibility Complexes (MHC), forming peptide:MHC (pMHC) complexes. 3D structures of pMHC complexes provide fundamental insight into T-cell recognition mechanism and aids immunotherapy design. High MHC and peptide diversities necessitate efficient computational modelling to enable whole proteome structural analysis. We developed PANDORA, a generic modelling pipeline for pMHC class I and II (pMHC-I and pMHC-II), and present its performance on pMHC-I here. Given a query, PANDORA searches for structural templates in its extensive database and then applies anchor restraints to the modelling process. This restrained energy minimization ensures one of the fastest pMHC modelling pipelines so far. On a set of 835 pMHC-I complexes over 78 MHC types, PANDORA generated models with a median RMSD of 0.70 Å and achieved a 93% success rate in top 10 models. PANDORA performs competitively with three pMHC-I modelling state-of-the-art approaches and outperforms AlphaFold2 in terms of accuracy while being superior to it in speed. PANDORA is a modularized and user-configurable python package with easy installation. We envision PANDORA to fuel deep learning algorithms with large-scale high-quality 3D models to tackle long-standing immunology challenges.
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Affiliation(s)
- Dario F. Marzella
- Center for Molecular and Biomolecular Informatics, Radboud Institute for Molecular Life Sciences, Radboudumc, Nijmegen, Netherlands
| | - Farzaneh M. Parizi
- Center for Molecular and Biomolecular Informatics, Radboud Institute for Molecular Life Sciences, Radboudumc, Nijmegen, Netherlands
| | - Derek van Tilborg
- Center for Molecular and Biomolecular Informatics, Radboud Institute for Molecular Life Sciences, Radboudumc, Nijmegen, Netherlands
- Department of Biomedical Engineering, Institute for Complex Molecular Systems, Eindhoven University of Technology, Eindhoven, Netherlands
| | - Nicolas Renaud
- Natural Sciences and Engineering section, Netherlands eScience Center, Amsterdam, Netherlands
| | - Daan Sybrandi
- Bijvoet Centre for Biomolecular Research, Faculty of Science - Chemistry, Utrecht University, Utrecht, Netherlands
| | - Rafaella Buzatu
- Center for Molecular and Biomolecular Informatics, Radboud Institute for Molecular Life Sciences, Radboudumc, Nijmegen, Netherlands
| | - Daniel T. Rademaker
- Center for Molecular and Biomolecular Informatics, Radboud Institute for Molecular Life Sciences, Radboudumc, Nijmegen, Netherlands
| | - Peter A. C. ‘t Hoen
- Center for Molecular and Biomolecular Informatics, Radboud Institute for Molecular Life Sciences, Radboudumc, Nijmegen, Netherlands
| | - Li C. Xue
- Center for Molecular and Biomolecular Informatics, Radboud Institute for Molecular Life Sciences, Radboudumc, Nijmegen, Netherlands
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10
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Tarabini RF, Rigo MM, Faustino Fonseca A, Rubin F, Bellé R, Kavraki LE, Ferreto TC, Amaral Antunes D, de Souza APD. Large-Scale Structure-Based Screening of Potential T Cell Cross-Reactivities Involving Peptide-Targets From BCG Vaccine and SARS-CoV-2. Front Immunol 2022; 12:812176. [PMID: 35095907 PMCID: PMC8793865 DOI: 10.3389/fimmu.2021.812176] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/09/2021] [Accepted: 12/21/2021] [Indexed: 12/22/2022] Open
Abstract
Although not being the first viral pandemic to affect humankind, we are now for the first time faced with a pandemic caused by a coronavirus. The Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) has been responsible for the COVID-19 pandemic, which caused more than 4.5 million deaths worldwide. Despite unprecedented efforts, with vaccines being developed in a record time, SARS-CoV-2 continues to spread worldwide with new variants arising in different countries. Such persistent spread is in part enabled by public resistance to vaccination in some countries, and limited access to vaccines in other countries. The limited vaccination coverage, the continued risk for resistant variants, and the existence of natural reservoirs for coronaviruses, highlight the importance of developing additional therapeutic strategies against SARS-CoV-2 and other coronaviruses. At the beginning of the pandemic it was suggested that countries with Bacillus Calmette-Guérin (BCG) vaccination programs could be associated with a reduced number and/or severity of COVID-19 cases. Preliminary studies have provided evidence for this relationship and further investigation is being conducted in ongoing clinical trials. The protection against SARS-CoV-2 induced by BCG vaccination may be mediated by cross-reactive T cell lymphocytes, which recognize peptides displayed by class I Human Leukocyte Antigens (HLA-I) on the surface of infected cells. In order to identify potential targets of T cell cross-reactivity, we implemented an in silico strategy combining sequence-based and structure-based methods to screen over 13,5 million possible cross-reactive peptide pairs from BCG and SARS-CoV-2. Our study produced (i) a list of immunogenic BCG-derived peptides that may prime T cell cross-reactivity against SARS-CoV-2, (ii) a large dataset of modeled peptide-HLA structures for the screened targets, and (iii) new computational methods for structure-based screenings that can be used by others in future studies. Our study expands the list of BCG peptides potentially involved in T cell cross-reactivity with SARS-CoV-2-derived peptides, and identifies multiple high-density "neighborhoods" of cross-reactive peptides which could be driving heterologous immunity induced by BCG vaccination, therefore providing insights for future vaccine development efforts.
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Affiliation(s)
- Renata Fioravanti Tarabini
- Laboratory of Clinical and Experimental Immunology, Infant Center, School of Health Science, Pontifical Catholic University of Rio Grande do Sul (PUCRS), Porto Alegre, Brazil
| | | | - André Faustino Fonseca
- Antunes Lab, Department of Biology and Biochemistry, University of Houston, Houston, TX, United States
| | - Felipe Rubin
- School of Technology - Pontifical Catholic University of Rio Grande do Sul (PUCRS), Porto Alegre, Brazil
| | - Rafael Bellé
- Laboratório de alto desempenho – Centro de Apoio ao desenvolvimento cientifico e tecnológico da (IDEIA), Pontifical Catholic University of Rio Grande do Sul (PUCRS), Porto Alegre, Brazil
| | - Lydia E Kavraki
- Kavraki Lab, Department of Computer Science, Rice University, Houston, TX, United States
| | - Tiago Coelho Ferreto
- School of Technology - Pontifical Catholic University of Rio Grande do Sul (PUCRS), Porto Alegre, Brazil,Laboratório de alto desempenho – Centro de Apoio ao desenvolvimento cientifico e tecnológico da (IDEIA), Pontifical Catholic University of Rio Grande do Sul (PUCRS), Porto Alegre, Brazil
| | - Dinler Amaral Antunes
- Antunes Lab, Department of Biology and Biochemistry, University of Houston, Houston, TX, United States,*Correspondence: Ana Paula Duarte de Souza, ; Dinler Amaral Antunes,
| | - Ana Paula Duarte de Souza
- Laboratory of Clinical and Experimental Immunology, Infant Center, School of Health Science, Pontifical Catholic University of Rio Grande do Sul (PUCRS), Porto Alegre, Brazil,*Correspondence: Ana Paula Duarte de Souza, ; Dinler Amaral Antunes,
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11
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Perez MAS, Cuendet MA, Röhrig UF, Michielin O, Zoete V. Structural Prediction of Peptide-MHC Binding Modes. Methods Mol Biol 2022; 2405:245-282. [PMID: 35298818 DOI: 10.1007/978-1-0716-1855-4_13] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/14/2023]
Abstract
The immune system is constantly protecting its host from the invasion of pathogens and the development of cancer cells. The specific CD8+ T-cell immune response against virus-infected cells and tumor cells is based on the T-cell receptor recognition of antigenic peptides bound to class I major histocompatibility complexes (MHC) at the surface of antigen presenting cells. Consequently, the peptide binding specificities of the highly polymorphic MHC have important implications for the design of vaccines, for the treatment of autoimmune diseases, and for personalized cancer immunotherapy. Evidence-based machine-learning approaches have been successfully used for the prediction of peptide binders and are currently being developed for the prediction of peptide immunogenicity. However, understanding and modeling the structural details of peptide/MHC binding is crucial for a better understanding of the molecular mechanisms triggering the immunological processes, estimating peptide/MHC affinity using universal physics-based approaches, and driving the design of novel peptide ligands. Unfortunately, due to the large diversity of MHC allotypes and possible peptides, the growing number of 3D structures of peptide/MHC (pMHC) complexes in the Protein Data Bank only covers a small fraction of the possibilities. Consequently, there is a growing need for rapid and efficient approaches to predict 3D structures of pMHC complexes. Here, we review the key characteristics of the 3D structure of pMHC complexes before listing databases and other sources of information on pMHC structures and MHC specificities. Finally, we discuss some of the most prominent pMHC docking software.
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Affiliation(s)
- Marta A S Perez
- Computer-aided Molecular Engineering Group, Department of Oncology UNIL-CHUV, Lausanne University, Lausanne, Switzerland
- Ludwig Institute for Cancer Research, Lausanne, Switzerland
- Molecular Modelling Group, SIB Swiss Institute of Bioinformatics, Lausanne, Switzerland
| | - Michel A Cuendet
- Molecular Modelling Group, SIB Swiss Institute of Bioinformatics, Lausanne, Switzerland
- Oncology Department, Centre Hospitalier Universitaire Vaudois (CHUV), Precision Oncology Center, Lausanne, Switzerland
| | - Ute F Röhrig
- Molecular Modelling Group, SIB Swiss Institute of Bioinformatics, Lausanne, Switzerland
| | - Olivier Michielin
- Molecular Modelling Group, SIB Swiss Institute of Bioinformatics, Lausanne, Switzerland.
- Oncology Department, Centre Hospitalier Universitaire Vaudois (CHUV), Precision Oncology Center, Lausanne, Switzerland.
| | - Vincent Zoete
- Computer-aided Molecular Engineering Group, Department of Oncology UNIL-CHUV, Lausanne University, Lausanne, Switzerland.
- Ludwig Institute for Cancer Research, Lausanne, Switzerland.
- Molecular Modelling Group, SIB Swiss Institute of Bioinformatics, Lausanne, Switzerland.
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12
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Baulu E, Dougé A, Chuvin N, Bay JO, Depil S. [T cell-based immunotherapies in solid tumors]. Bull Cancer 2021; 108:S96-S108. [PMID: 34920813 DOI: 10.1016/j.bulcan.2021.06.004] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/17/2021] [Revised: 06/07/2021] [Accepted: 06/11/2021] [Indexed: 12/08/2022]
Abstract
In solid tumors, adoptive T cell therapies based on ex vivo amplification of antitumor T cell are represented by three main complementary approaches : (i) tumor infiltrating lymphocytes (TILs) which are amplified in vitro before reinjection to the patient, (ii) chimeric antigen receptor (CAR) engineered T cells and (iii) T cell receptor (TCR) engineered T cells. Despite encouraging results, some obstacles remain, such as optimal target selection and tumor microenvironment. In this Review, we discuss pros and cons of these different therapeutic strategies that may open new perspectives in the treatment of solid tumors.
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Affiliation(s)
- Estelle Baulu
- Centre de recherche en cancérologie de Lyon, 28, rue Laennec, 69008 Lyon, France; ErVaccine Technologies, 28, rue Laennec, 69008 Lyon, France
| | - Aurore Dougé
- CHU Estaing, service d'hématologie, 1, rue Lucie et Raymond Aubrac, 63100 Clermont-Ferrand, France
| | - Nicolas Chuvin
- ErVaccine Technologies, 28, rue Laennec, 69008 Lyon, France
| | - Jacques-Olivier Bay
- CHU Estaing, service d'hématologie, 1, rue Lucie et Raymond Aubrac, 63100 Clermont-Ferrand, France; Faculté de médecine, 28, place Henri Dunant, 63000 Clermont-Ferrand, France
| | - Stéphane Depil
- Centre de recherche en cancérologie de Lyon, 28, rue Laennec, 69008 Lyon, France; ErVaccine Technologies, 28, rue Laennec, 69008 Lyon, France; Centre Léon Bérard, 28, Prom. Léa et Napoléon Bullukian, 69008 Lyon, France; Université Claude-Bernard Lyon 1, 43, boulevard du 11 novembre 1918, 69100 Villeurbanne, France.
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13
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Hall-Swan S, Devaurs D, Rigo MM, Antunes DA, Kavraki LE, Zanatta G. DINC-COVID: A webserver for ensemble docking with flexible SARS-CoV-2 proteins. Comput Biol Med 2021; 139:104943. [PMID: 34717233 PMCID: PMC8518241 DOI: 10.1016/j.compbiomed.2021.104943] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/26/2021] [Revised: 09/27/2021] [Accepted: 10/11/2021] [Indexed: 12/16/2022]
Abstract
An unprecedented research effort has been undertaken in response to the ongoing COVID-19 pandemic. This has included the determination of hundreds of crystallographic structures of SARS-CoV-2 proteins, and numerous virtual screening projects searching large compound libraries for potential drug inhibitors. Unfortunately, these initiatives have had very limited success in producing effective inhibitors against SARS-CoV-2 proteins. A reason might be an often overlooked factor in these computational efforts: receptor flexibility. To address this issue we have implemented a computational tool for ensemble docking with SARS-CoV-2 proteins. We have extracted representative ensembles of protein conformations from the Protein Data Bank and from in silico molecular dynamics simulations. Twelve pre-computed ensembles of SARS-CoV-2 protein conformations have now been made available for ensemble docking via a user-friendly webserver called DINC-COVID (dinc-covid.kavrakilab.org). We have validated DINC-COVID using data on tested inhibitors of two SARS-CoV-2 proteins, obtaining good correlations between docking-derived binding energies and experimentally-determined binding affinities. Some of the best results have been obtained on a dataset of large ligands resolved via room temperature crystallography, and therefore capturing alternative receptor conformations. In addition, we have shown that the ensembles available in DINC-COVID capture different ranges of receptor flexibility, and that this diversity is useful in finding alternative binding modes of ligands. Overall, our work highlights the importance of accounting for receptor flexibility in docking studies, and provides a platform for the identification of new inhibitors against SARS-CoV-2 proteins.
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Affiliation(s)
- Sarah Hall-Swan
- Department of Computer Science, Rice University, Houston, 77005, Texas, United States
| | - Didier Devaurs
- MRC Institute of Genetics and Cancer, University of Edinburgh, Edinburgh, EH4 2XU, United Kingdom
| | - Mauricio M. Rigo
- Department of Computer Science, Rice University, Houston, 77005, Texas, United States
| | - Dinler A. Antunes
- Department of Computer Science, Rice University, Houston, 77005, Texas, United States,Department of Biology and Biochemistry, University of Houston, Houston, 77005, Texas, United States,Corresponding author. Department of Computer Science, Rice University, Houston, 77005, Texas, United States
| | - Lydia E. Kavraki
- Department of Computer Science, Rice University, Houston, 77005, Texas, United States,Corresponding author
| | - Geancarlo Zanatta
- Department of Physics, Federal University of Ceará, Fortaleza, CE, Brazil,Corresponding author
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14
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Bell DR, Domeniconi G, Yang CC, Zhou R, Zhang L, Cong G. Dynamics-Based Peptide-MHC Binding Optimization by a Convolutional Variational Autoencoder: A Use-Case Model for CASTELO. J Chem Theory Comput 2021; 17:7962-7971. [PMID: 34793168 DOI: 10.1021/acs.jctc.1c00870] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
Abstract
An unsolved challenge in the development of antigen-specific immunotherapies is determining the optimal antigens to target. Comprehension of antigen-major histocompatibility complex (MHC) binding is paramount toward achieving this goal. Here, we apply CASTELO, a combined machine learning-molecular dynamics (ML-MD) approach, to identify per-residue antigen binding contributions and then design novel antigens of increased MHC-II binding affinity for a type 1 diabetes-implicated system. We build upon a small-molecule lead optimization algorithm by training a convolutional variational autoencoder (CVAE) on MD trajectories of 48 different systems across four antigens and four HLA serotypes. We develop several new machine learning metrics including a structure-based anchor residue classification model as well as cluster comparison scores. ML-MD predictions agree well with experimental binding results and free energy perturbation-predicted binding affinities. Moreover, ML-MD metrics are independent of traditional MD stability metrics such as contact area and root-mean-square fluctuations (RMSF), which do not reflect binding affinity data. Our work supports the role of structure-based deep learning techniques in antigen-specific immunotherapy design.
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Affiliation(s)
- David R Bell
- IBM Thomas J. Watson Research Center, Yorktown Heights, New York 10598, United States.,Advanced Biomedical Computational Science, Frederick National Laboratory for Cancer Research, Frederick, Maryland 21701, United States
| | - Giacomo Domeniconi
- IBM Thomas J. Watson Research Center, Yorktown Heights, New York 10598, United States
| | - Chih-Chieh Yang
- IBM Thomas J. Watson Research Center, Yorktown Heights, New York 10598, United States
| | - Ruhong Zhou
- IBM Thomas J. Watson Research Center, Yorktown Heights, New York 10598, United States.,Zhejiang University, 688 Yuhangtang Road, Hangzhou 310027, China
| | - Leili Zhang
- IBM Thomas J. Watson Research Center, Yorktown Heights, New York 10598, United States
| | - Guojing Cong
- IBM Thomas J. Watson Research Center, Yorktown Heights, New York 10598, United States.,Oak Ridge National Laboratory, 1 Bethel Valley Road, Oak Ridge, Tennessee 37830, United States
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15
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Quiros-Fernandez I, Poorebrahim M, Fakhr E, Cid-Arregui A. Immunogenic T cell epitopes of SARS-CoV-2 are recognized by circulating memory and naïve CD8 T cells of unexposed individuals. EBioMedicine 2021; 72:103610. [PMID: 34627082 PMCID: PMC8493415 DOI: 10.1016/j.ebiom.2021.103610] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/07/2021] [Revised: 09/17/2021] [Accepted: 09/17/2021] [Indexed: 11/25/2022] Open
Abstract
Background Recent studies have provided evidence of T cell reactivity to Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) in significant numbers of non-infected individuals, which has been attributed to cross-reactive CD4 memory T cells from previous exposure to seasonal coronaviruses. Less evidence of cross-reactive memory CD8 T cells has been documented to date. Methods We used the NetCTLPan neural network of the Epitope Database and Analysis Resource to select a series of 27 HLA-A*02:01 epitopes derived from the proteome of SARS-CoV-2. Their binding capacity was assessed by a HLA-A*02:01 stabilization assay and by quantifying their binding to HLA-A*02:01 monomers for the generation of tetramers. Their ability to stimulate and induce expansion of SARS-CoV-2 reactive CD8 T cells was measured by flow cytometry. The TCR repertoire of COVID convalescent and healthy unexposed donors was analysed using the MIRA database. Findings The HLA-A*02:01 epitopes tested were able to stabilise HLA molecules and induce activation of CD8 T cells of healthy unexposed donors. Our results, based on specific tetramer binding, provide evidence supporting the presence of frequent cross-reactive CD8 T cells to SARS-CoV-2 antigens in non-exposed individuals. Interestingly, the reactive cells were distributed into naïve, memory and effector subsets. Interpretation Our data are consistent with a significant proportion of the reactive CD8 T clones belonging to the public shared repertoire, readily available in absence of previous contact with closely related coronaviruses. Furthermore, we demonstrate the immunogenic capacity of long peptides carrying T cell epitopes, which can serve to isolate virus-specific T cell receptors among the ample repertoire of healthy unexposed subjects and could have application in COVID-19 immunotherapy. Limitations of our study are that it concentrated on one MHC I allele (HLA-A*02:01) and the low numbers of samples and epitopes tested.
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Affiliation(s)
- Isaac Quiros-Fernandez
- Targeted Tumor Vaccines Group, Clinical Cooperation Unit Applied Tumor Immunity, German Cancer Research Center (DKFZ), Im Neuenheimer Feld 280, 69120 Heidelberg, Germany
| | - Mansour Poorebrahim
- Targeted Tumor Vaccines Group, Clinical Cooperation Unit Applied Tumor Immunity, German Cancer Research Center (DKFZ), Im Neuenheimer Feld 280, 69120 Heidelberg, Germany
| | - Elham Fakhr
- Targeted Tumor Vaccines Group, Clinical Cooperation Unit Applied Tumor Immunity, German Cancer Research Center (DKFZ), Im Neuenheimer Feld 280, 69120 Heidelberg, Germany
| | - Angel Cid-Arregui
- Targeted Tumor Vaccines Group, Clinical Cooperation Unit Applied Tumor Immunity, German Cancer Research Center (DKFZ), Im Neuenheimer Feld 280, 69120 Heidelberg, Germany.
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16
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Abella JR, Antunes D, Jackson K, Lizée G, Clementi C, Kavraki LE. Markov state modeling reveals alternative unbinding pathways for peptide-MHC complexes. Proc Natl Acad Sci U S A 2020; 117:30610-30618. [PMID: 33184174 PMCID: PMC7720115 DOI: 10.1073/pnas.2007246117] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/11/2023] Open
Abstract
Peptide binding to major histocompatibility complexes (MHCs) is a central component of the immune system, and understanding the mechanism behind stable peptide-MHC binding will aid the development of immunotherapies. While MHC binding is mostly influenced by the identity of the so-called anchor positions of the peptide, secondary interactions from nonanchor positions are known to play a role in complex stability. However, current MHC-binding prediction methods lack an analysis of the major conformational states and might underestimate the impact of secondary interactions. In this work, we present an atomically detailed analysis of peptide-MHC binding that can reveal the contributions of any interaction toward stability. We propose a simulation framework that uses both umbrella sampling and adaptive sampling to generate a Markov state model (MSM) for a coronavirus-derived peptide (QFKDNVILL), bound to one of the most prevalent MHC receptors in humans (HLA-A24:02). While our model reaffirms the importance of the anchor positions of the peptide in establishing stable interactions, our model also reveals the underestimated importance of position 4 (p4), a nonanchor position. We confirmed our results by simulating the impact of specific peptide mutations and validated these predictions through competitive binding assays. By comparing the MSM of the wild-type system with those of the D4A and D4P mutations, our modeling reveals stark differences in unbinding pathways. The analysis presented here can be applied to any peptide-MHC complex of interest with a structural model as input, representing an important step toward comprehensive modeling of the MHC class I pathway.
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Affiliation(s)
- Jayvee R Abella
- Department of Computer Science, Rice University, Houston, TX 77005
| | - Dinler Antunes
- Department of Computer Science, Rice University, Houston, TX 77005
| | - Kyle Jackson
- Department of Melanoma Medical Oncology-Research, The University of Texas MD Anderson Cancer Center, Houston, TX 77030
| | - Gregory Lizée
- Department of Melanoma Medical Oncology-Research, The University of Texas MD Anderson Cancer Center, Houston, TX 77030
| | - Cecilia Clementi
- Center for Theoretical Biological Physics, Rice University, Houston, TX 77005
- Department of Chemistry, Rice University, Houston, TX 77005
| | - Lydia E Kavraki
- Department of Computer Science, Rice University, Houston, TX 77005;
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17
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Arns T, Antunes DA, Abella JR, Rigo MM, Kavraki LE, Giuliatti S, Donadi EA. Structural Modeling and Molecular Dynamics of the Immune Checkpoint Molecule HLA-G. Front Immunol 2020; 11:575076. [PMID: 33240264 PMCID: PMC7677236 DOI: 10.3389/fimmu.2020.575076] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/22/2020] [Accepted: 10/13/2020] [Indexed: 02/01/2023] Open
Abstract
HLA-G is considered to be an immune checkpoint molecule, a function that is closely linked to the structure and dynamics of the different HLA-G isoforms. Unfortunately, little is known about the structure and dynamics of these isoforms. For instance, there are only seven crystal structures of HLA-G molecules, being all related to a single isoform, and in some cases lacking important residues associated to the interaction with leukocyte receptors. In addition, they lack information on the dynamics of both membrane-bound HLA-G forms, and soluble forms. We took advantage of in silico strategies to disclose the dynamic behavior of selected HLA-G forms, including the membrane-bound HLA-G1 molecule, soluble HLA-G1 dimer, and HLA-G5 isoform. Both the membrane-bound HLA-G1 molecule and the soluble HLA-G1 dimer were quite stable. Residues involved in the interaction with ILT2 and ILT4 receptors (α3 domain) were very close to the lipid bilayer in the complete HLA-G1 molecule, which might limit accessibility. On the other hand, these residues can be completely exposed in the soluble HLA-G1 dimer, due to the free rotation of the disulfide bridge (Cys42/Cys42). In fact, we speculate that this free rotation of each protomer (i.e., the chains composing the dimer) could enable alternative binding modes for ILT2/ILT4 receptors, which in turn could be associated with greater affinity of the soluble HLA-G1 dimer. Structural analysis of the HLA-G5 isoform demonstrated higher stability for the complex containing the peptide and coupled β2-microglobulin, while structures lacking such domains were significantly unstable. This study reports for the first time structural conformations for the HLA-G5 isoform and the dynamic behavior of HLA-G1 molecules under simulated biological conditions. All modeled structures were made available through GitHub (https://github.com/KavrakiLab/), enabling their use as templates for modeling other alleles and isoforms, as well as for other computational analyses to investigate key molecular interactions.
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Affiliation(s)
- Thais Arns
- Department of Basic and Applied Immunology, Ribeirão Preto Medical School, University of São Paulo, Ribeirão Preto, Brazil
| | - Dinler A. Antunes
- Department of Computer Science, Rice University, Houston, TX, United States
| | - Jayvee R. Abella
- Department of Computer Science, Rice University, Houston, TX, United States
| | - Maurício M. Rigo
- Department of Computer Science, Rice University, Houston, TX, United States
| | - Lydia E. Kavraki
- Department of Computer Science, Rice University, Houston, TX, United States
| | - Silvana Giuliatti
- Department of Genetics, Ribeirão Preto Medical School, University of São Paulo, Ribeirão Preto, Brazil
| | - Eduardo A. Donadi
- Department of Basic and Applied Immunology, Ribeirão Preto Medical School, University of São Paulo, Ribeirão Preto, Brazil
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18
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Warner JL, Klemm JD. Informatics Tools for Cancer Research and Care: Bridging the Gap Between Innovation and Implementation. JCO Clin Cancer Inform 2020; 4:784-786. [PMID: 32870722 DOI: 10.1200/cci.20.00086] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/20/2022] Open
Affiliation(s)
| | - Juli D Klemm
- National Institutes of Health, National Cancer Institute, Bethesda, MD
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