scholarly journals The pockets guide to HLA class I molecules

2021 ◽  
Author(s):  
Andrea T. Nguyen ◽  
Christopher Szeto ◽  
Stephanie Gras
Keyword(s):  
T Cells ◽  
Cell Surface ◽  
Peptide Binding ◽  
Hla Class I ◽  
Class I ◽  
Side Chains ◽  
Binding Prediction ◽  
Peptide Residue ◽  
Hla Class I Molecules ◽  
Binding Pockets

Human leukocyte antigens (HLA) are cell-surface proteins that present peptides to T cells. These peptides are bound within the peptide binding cleft of HLA, and together as a complex, are recognised by T cells using their specialised T cell receptors. Within the cleft, the peptide residue side chains bind into distinct pockets. These pockets ultimately determine the specificity of peptide binding. As HLAs are the most polymorphic molecules in humans, amino acid variants in each binding pocket influences the peptide repertoire that can be presented on the cell surface. Here, we review each of the 6 HLA binding pockets of HLA class I (HLA-I) molecules. The binding specificity of pockets B and F are strong determinants of peptide binding and have been used to classify HLA into supertypes, a useful tool to predict peptide binding to a given HLA. Over the years, peptide binding prediction has also become more reliable by using binding affinity and mass spectrometry data. Crystal structures of peptide-bound HLA molecules provide a means to interrogate the interactions between binding pockets and peptide residue side chains. We find that most of the bound peptides from these structures conform to binding motifs determined from prediction software and examine outliers to learn how these HLAs are stabilised from a structural perspective.

scholarly journals Amino acid substitution at peptide-binding pockets of HLA class I molecules increases risk of severe acute GVHD and mortality

Blood ◽  
2013 ◽  
Vol 122 (22) ◽  
pp. 3651-3658 ◽  
Author(s):  
Joseph Pidala ◽  
Tao Wang ◽  
Michael Haagenson ◽  
Stephen R. Spellman ◽  
Medhat Askar ◽  
...  
Keyword(s):  
Amino Acid ◽  
Amino Acid Substitution ◽  
Peptide Binding ◽  
Hla Class I ◽  
Class I ◽  
Graft Versus Host ◽  
Hla Class I Molecules ◽  
Key Points ◽  
Binding Pockets ◽  
Binding Residues

Key Points Amino acid substitution at peptide-binding residues of the HLA class I molecule is associated with graft-versus-host disease and mortality. Avoidance of donor-recipient combinations that result in amino acid substitution at peptide-binding residues may improve transplant outcomes.

scholarly journals A comprehensive review and performance evaluation of bioinformatics tools for HLA class I peptide-binding prediction

2020 ◽  
Vol 21 (4) ◽  
pp. 1119-1135 ◽  
Author(s):  
Shutao Mei ◽  
Fuyi Li ◽  
André Leier ◽  
Tatiana T Marquez-Lago ◽  
Kailin Giam ◽  
...  
Keyword(s):  
Machine Learning ◽  
T Cell ◽  
Peptide Binding ◽  
Hla Class I ◽  
Machine Learning Algorithms ◽  
Class I ◽  
Target Cells ◽  
Binding Prediction ◽  
Validation Data ◽  
Data Set

Abstract Human leukocyte antigen class I (HLA-I) molecules are encoded by major histocompatibility complex (MHC) class I loci in humans. The binding and interaction between HLA-I molecules and intracellular peptides derived from a variety of proteolytic mechanisms play a crucial role in subsequent T-cell recognition of target cells and the specificity of the immune response. In this context, tools that predict the likelihood for a peptide to bind to specific HLA class I allotypes are important for selecting the most promising antigenic targets for immunotherapy. In this article, we comprehensively review a variety of currently available tools for predicting the binding of peptides to a selection of HLA-I allomorphs. Specifically, we compare their calculation methods for the prediction score, employed algorithms, evaluation strategies and software functionalities. In addition, we have evaluated the prediction performance of the reviewed tools based on an independent validation data set, containing 21 101 experimentally verified ligands across 19 HLA-I allotypes. The benchmarking results show that MixMHCpred 2.0.1 achieves the best performance for predicting peptides binding to most of the HLA-I allomorphs studied, while NetMHCpan 4.0 and NetMHCcons 1.1 outperform the other machine learning-based and consensus-based tools, respectively. Importantly, it should be noted that a peptide predicted with a higher binding score for a specific HLA allotype does not necessarily imply it will be immunogenic. That said, peptide-binding predictors are still very useful in that they can help to significantly reduce the large number of epitope candidates that need to be experimentally verified. Several other factors, including susceptibility to proteasome cleavage, peptide transport into the endoplasmic reticulum and T-cell receptor repertoire, also contribute to the immunogenicity of peptide antigens, and some of them can be considered by some predictors. Therefore, integrating features derived from these additional factors together with HLA-binding properties by using machine-learning algorithms may increase the prediction accuracy of immunogenic peptides. As such, we anticipate that this review and benchmarking survey will assist researchers in selecting appropriate prediction tools that best suit their purposes and provide useful guidelines for the development of improved antigen predictors in the future.

scholarly journals T-cell malignancies with mature phenotypes: altered cell cycle regulation by HLA class I molecules

Blood ◽  
1991 ◽  
Vol 78 (8) ◽  
pp. 2045-2052 ◽  
Author(s):  
MC Turco ◽  
F Alfinito ◽  
M De Felice ◽  
A Lamberti ◽  
S Ferrone ◽  
...  
Keyword(s):  
Cell Proliferation ◽  
T Cells ◽  
T Cell ◽  
Dna Synthesis ◽  
Hla Class I ◽  
Class I ◽  
Mitogenic Effect ◽  
Prolymphocytic Leukemia ◽  
T Lymphocyte Proliferation ◽  
Hla Class I Molecules

Abstract Soluble anti-HLA class I monoclonal antibodies (MoAbs) modulate normal T-lymphocyte proliferation induced via the CD3/Ti and the CD2 pathway, but do not induce proliferation of normal T lymphocytes in the absence of additional mitogenic stimuli. In this report, we show that anti-HLA class I MoAbs induce DNA synthesis in peripheral blood mononuclear cells from a patient with a CD4+CD8+T-prolymphocytic leukemia (T-PLL) and from a patient with a CD4-CD8+ T-chronic lymphocytic leukemia (T- CLL), in the absence of detectable additional mitogenic stimuli. Proliferation of leukemic T cells is induced by both whole Igs and Fab' fragments of anti-HLA class I MoAbs, arguing in favor of their direct interactions with the proliferating cells as the mechanism underlying the mitogenic effect. This interpretation is also supported by the ability of anti-HLA class I MoAbs to induce proliferation of leukemic T- cell preparations, depleted of accessory cells. DNA synthesis in T-CLL and T-PLL cells is preceded by expression of G1-specific messenger RNAs, ie. c-myc, 2F1, Tac, and interferon-gamma, in activated cells. Cell proliferation is inhibited by the protein kinase C inhibitor H7, indicating that activation of this enzyme is required for the mitogenic effect of anti-HLA class I MoAbs. The latter inhibit the proliferation of T-CLL cells as well as that of normal T cells stimulated with anti- CD3 MoAbs and enhance that of both types of cells stimulated with anti- CD2 MoAbs. In addition, anti-HLA class I MoAb Q6/64 in combination with anti-CD2 MoAb 9.6 or MoAb 9–1 induces proliferation of leukemic T cells to a greater extent than the individual MoAbs, but is not mitogenic for normal T cells. Anti-HLA class I MoAbs restore the cytolytic activity of T-CLL cells that is lost after 5 days of incubation of control medium, suggesting that HLA class I antigens may mediate a signal contributing to the activation state. The present results indicate that leukemic T-cell proliferation can be triggered via HLA class I molecules and suggest a potential role for these antigens in the in vivo growth of malignant clones.

scholarly journals Fas-Independent Apoptosis of Activated T Cells Induced by Antibodies to the HLA Class I α1 Domain

Blood ◽  
1997 ◽  
Vol 90 (9) ◽  
pp. 3629-3639 ◽  
Author(s):  
Laurent Genestier ◽  
Romain Paillot ◽  
Nathalie Bonnefoy-Berard ◽  
Geneviéve Meffre ◽  
Monique Flacher ◽  
...  
Keyword(s):  
Cell Proliferation ◽  
T Cells ◽  
T Cell ◽  
T Lymphocytes ◽  
Heavy Chain ◽  
Hla Class I ◽  
Class I ◽  
T Cell Proliferation ◽  
Activated T Cells ◽  
Hla Class I Molecules

Abstract In addition to their major function in antigen presentation and natural killer cell activity regulation, HLA class I molecules may modulate T-cell activation and proliferation. Monoclonal antibodies (MoAbs) that recognize distinct epitopes of HLA class I molecules were reported to interfere with T-cell proliferation. We show here that two MoAbs (mouse MoAb90 and rat YTH862) that bind to an epitope of the α1 domain of HLA class I heavy chain induce apoptotic cell death of activated, but not resting, peripheral T lymphocytes. Other reference anti-HLA class I antibodies specific for distinct epitopes of the α1 (B9.12.1), α2 (W6/32), or α3 (TP25.99) domains of the heavy chain decreased T-cell proliferation but had little or no apoptotic effect. Apoptosis shown by DNA fragmentation, phosphatidylserine externalization, and decrease of mitochondrial transmembrane potential was observed whatever the type of T-cell activator. Apoptosis did not result from Fas/Fas-L interaction and distinct though partly overlapping populations of activated T cells were susceptible to Fas– and HLA class I–mediated apoptosis, respectively. Induction of apoptosis did not require HLA class I cross-linking inasmuch as it could be observed with monovalent Fab′ fragments. The data indicate that MoAb90 and YTH862 directed against the α1 domain of HLA class I trigger apoptosis of activated T lymphocytes by a pathway which does not involve Fas-ligand.

scholarly journals Human CD8 transgene regulation of HLA recognition by murine T cells.

1995 ◽  
Vol 182 (5) ◽  
pp. 1315-1325 ◽  
Author(s):  
D M LaFace ◽  
M Vestberg ◽  
Y Yang ◽  
R Srivastava ◽  
J DiSanto ◽  
...  
Keyword(s):  
T Cells ◽  
T Cell ◽  
Hla Class I ◽  
Cell Receptor ◽  
Class I ◽  
Antigenic Peptides ◽  
Cell Repertoire ◽  
Repertoire Selection ◽  
Hla Class I Molecules ◽  
Cell Expression

A series of human CD8 transgenic (hCD8 Tg) mice with differential expression in the thymus and periphery were produced to investigate CD8 coreceptor regulation of repertoire selection and T cell responses. Expression of hCD8 markedly enhanced responses to both HLA class I molecules and hybrid A2/Kb molecules providing functional evidence for a second interaction site, outside of the alpha 3 domain, which is essential for optimal coreceptor function. Peripheral T cell expression of hCD8 was sufficient to augment responsiveness to HLA class I, as hCD8 Tg mice which lacked thymic expression responded as well as mice expressing hCD8 in the thymus and periphery. Both murine CD8+ and CD4+ T cells expressing hCD8 transgenes exhibited markedly enhanced responses to foreign HLA class I, revealing the ability of T cell receptor repertoires selected on either murine class I or class II to recognize human class I major histocompatibility complex (MHC). In contrast to recognition of foreign class I, thymic expression of hCD8 transgenes was absolutely required to enhance recognition of antigenic peptide restricted by self-HLA class I. Thus, our studies revealed disparate requirements for CD8 coreceptor expression in the thymus for selection of a T cell repertoire responsive to foreign MHC and to antigenic peptides bound to self-MHC, providing a novel demonstration of positive selection that is dependent on human CD8.

scholarly journals HLA class I binding prediction via convolutional neural networks

2017 ◽  
Author(s):  
Yeeleng S. Vang ◽  
Xiaohui Xie
Keyword(s):  
Machine Learning ◽  
Network Architecture ◽  
Peptide Binding ◽  
Hla Class I ◽  
Class I ◽  
Machine Learning Techniques ◽  
Binding Potential ◽  
Distributed Representation ◽  
Binding Prediction ◽  
Peptide Binding Prediction

AbstractMany biological processes are governed by protein-ligand interactions. One such example is the recognition of self and non-self cells by the immune system. This immune response process is regulated by the major histocompatibility complex (MHC) protein which is encoded by the human leukocyte antigen (HLA) complex. Understanding the binding potential between MHC and peptides can lead to the design of more potent, peptide-based vaccines and immunotherapies for infectious autoimmune diseases.We apply machine learning techniques from the natural language processing (NLP) domain to address the task of MHC-peptide binding prediction. More specifically, we introduce a new distributed representation of amino acids, name HLA-Vec, that can be used for a variety of downstream proteomic machine learning tasks. We then propose a deep convolutional neural network architecture, name HLA-CNN, for the task of HLA class I-peptide binding prediction. Experimental results show combining the new distributed representation with our HLA-CNN architecture acheives state-of-the-art results in the majority of the latest two Immune Epitope Database (IEDB) weekly automated benchmark datasets. We further apply our model to predict binding on the human genome and identify 15 genes with potential for self binding. Codes are available at https://github.com/uci-cbcl/HLA-bind.

scholarly journals EXTH-73. HETEROGENEITY IN CELL SURFACE ANTIGEN AND HLA CLASS I EXPRESSION IN PEDIATRIC BRAIN TUMORS AND ITS IMPACT ON T-CELL IMMUNOTHERAPY

2020 ◽  
Vol 22 (Supplement_2) ◽  
pp. ii103-ii103
Author(s):  
Haley Houke ◽  
Xiaoyan Zhu ◽  
Kimberly S Mercer ◽  
Jennifer L Stripay ◽  
Jason Chiang ◽  
...  
Keyword(s):  
T Cells ◽  
Brain Tumor ◽  
T Cell ◽  
Brain Tumors ◽  
Cell Surface ◽  
Hla Class I ◽  
Pediatric Brain Tumors ◽  
Class I ◽  
Car T ◽  
Pediatric Brain

Abstract Immunotherapy with tumor antigen-specific chimeric antigen receptor (CAR) and/or ab (T-cell receptor) TCR T-cells has the potential to improve clinical outcomes of patients with pediatric brain tumors. As a prerequisite for successful T-cell therapies, we must determine which cell surface antigens are expressed and targetable in these tumors, and if HLA Class I is present, which is necessary for ab TCR T-cell recognition. Therefore, in this study we systematically analyzed pediatric patient-derived orthotopic xenograft (PDOX) brain tumor samples for cell surface expression of five known CAR targets: IL13Ra2, HER2, EphA2, B7-H3, and GD2, as well as HLA Class I. We established and validated a flow cytometry-based method of profiling tumor-associated antigens. Fifty-three PDOX samples have been profiled to date, including medulloblastoma, high grade glioma (HGG), diffuse intrinsic pontine glioma (DIPG), atypical teratoid rhabdoid tumor (ATRT), and ependymoma, among others. Our results showed high variability within and between individual samples. B7-H3 was the most consistently expressed, seen in 98% of the samples tested. We validated these results by conventional immunohistochemistry staining for B7-H3 and found comparable RESULTS: HLA Class I was highly expressed on all HGG samples but was undetectable on 47.8% of other brain tumor samples. This suggests that down-regulation of HLA class I is one mechanism by which brain tumors evade conventional T-cells, and that HLA-independent CAR T-cells would be useful therapies. We also compared expression of antigens in fresh patient samples and corresponding PDOX tumors and saw that they were indeed similar. To our knowledge, this is the largest group of pediatric brain tumor PDOX samples methodically analyzed for potential CAR target antigens and HLA Class I. Taken together, our data demonstrate that elimination of tumors by CAR T-cell immunotherapies will require targeting multiple antigens, and our profiling method could inform how to circumvent antigen-negative relapse.

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