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1.
BMC Bioinformatics ; 22(1): 7, 2021 Jan 06.
Artigo em Inglês | MEDLINE | ID: mdl-33407098

RESUMO

BACKGROUND: Accurate prediction of binding between class I human leukocyte antigen (HLA) and neoepitope is critical for target identification within personalized T-cell based immunotherapy. Many recent prediction tools developed upon the deep learning algorithms and mass spectrometry data have indeed showed improvement on the average predicting power for class I HLA-peptide interaction. However, their prediction performances show great variability over individual HLA alleles and peptides with different lengths, which is particularly the case for HLA-C alleles due to the limited amount of experimental data. To meet the increasing demand for attaining the most accurate HLA-peptide binding prediction for individual patient in the real-world clinical studies, more advanced deep learning framework with higher prediction accuracy for HLA-C alleles and longer peptides is highly desirable. RESULTS: We present a pan-allele HLA-peptide binding prediction framework-MATHLA which integrates bi-directional long short-term memory network and multiple head attention mechanism. This model achieves better prediction accuracy in both fivefold cross-validation test and independent test dataset. In addition, this model is superior over existing tools regarding to the prediction accuracy for longer ligand ranging from 11 to 15 amino acids. Moreover, our model also shows a significant improvement for HLA-C-peptide-binding prediction. By investigating multiple-head attention weight scores, we depicted possible interaction patterns between three HLA I supergroups and their cognate peptides. CONCLUSION: Our method demonstrates the necessity of further development of deep learning algorithm in improving and interpreting HLA-peptide binding prediction in parallel to increasing the amount of high-quality HLA ligandome data.


Assuntos
Biologia Computacional/métodos , Antígenos de Histocompatibilidade Classe I , Redes Neurais de Computação , Peptídeos , Ligação Proteica , Algoritmos , Antígenos de Histocompatibilidade Classe I/química , Antígenos de Histocompatibilidade Classe I/metabolismo , Humanos , Modelos Estatísticos , Peptídeos/química , Peptídeos/metabolismo
2.
Vaccine ; 38(48): 7612-7628, 2020 11 10.
Artigo em Inglês | MEDLINE | ID: mdl-33082015

RESUMO

SARS-CoV-2 causes a severe respiratory disease called COVID-19. Currently, global health is facing its devastating outbreak. However, there is no vaccine available against this virus up to now. In this study, a novel multi-epitope vaccine against SARS-CoV-2 was designed to provoke both innate and adaptive immune responses. The immunodominant regions of six non-structural proteins (nsp7, nsp8, nsp9, nsp10, nsp12 and nsp14) of SARS-CoV-2 were selected by multiple immunoinformatic tools to provoke T cell immune response. Also, immunodominant fragment of the functional region of SARS-CoV-2 spike (400-510 residues) protein was selected for inducing neutralizing antibodies production. The selected regions' sequences were connected to each other by furin-sensitive linker (RVRR). Moreover, the functional region of ß-defensin as a well-known agonist for the TLR-4/MD complex was added at the N-terminus of the vaccine using (EAAAK)3 linker. Also, a CD4 + T-helper epitope, PADRE, was used at the C-terminal of the vaccine by GPGPG and A(EAAAK)2A linkers to form the final vaccine construct. The physicochemical properties, allergenicity, antigenicity, functionality and population coverage of the final vaccine construct were analyzed. The final vaccine construct was an immunogenic, non-allergen and unfunctional protein which contained multiple CD8 + and CD4 + overlapping epitopes, IFN-γ inducing epitopes, linear and conformational B cell epitopes. It could form stable and significant interactions with TLR-4/MD according to molecular docking and dynamics simulations. Global population coverage of the vaccine for HLA-I and II were estimated 96.2% and 97.1%, respectively. At last, the final vaccine construct was reverse translated to design the DNA vaccine. Although the designed vaccine exhibited high efficacy in silico, further experimental validation is necessary.


Assuntos
Anticorpos Antivirais/biossíntese , Betacoronavirus/imunologia , Infecções por Coronavirus/prevenção & controle , Pandemias/prevenção & controle , Pneumonia Viral/prevenção & controle , Glicoproteína da Espícula de Coronavírus/imunologia , Proteínas não Estruturais Virais/imunologia , Vacinas Virais/biossíntese , Sequência de Aminoácidos , Betacoronavirus/patogenicidade , Biologia Computacional , Infecções por Coronavirus/epidemiologia , Infecções por Coronavirus/genética , Infecções por Coronavirus/imunologia , Infecções por Coronavirus/metabolismo , Infecções por Coronavirus/virologia , Epitopos de Linfócito B/química , Epitopos de Linfócito B/genética , Epitopos de Linfócito B/imunologia , Epitopos de Linfócito T/química , Epitopos de Linfócito T/genética , Epitopos de Linfócito T/imunologia , Antígenos de Histocompatibilidade Classe I/química , Antígenos de Histocompatibilidade Classe I/genética , Antígenos de Histocompatibilidade Classe I/imunologia , Antígenos de Histocompatibilidade Classe II/química , Antígenos de Histocompatibilidade Classe II/genética , Antígenos de Histocompatibilidade Classe II/imunologia , Humanos , Imunidade Inata/efeitos dos fármacos , Imunogenicidade da Vacina , Simulação de Acoplamento Molecular , Pneumonia Viral/epidemiologia , Pneumonia Viral/imunologia , Pneumonia Viral/virologia , Ligação Proteica , Estrutura Secundária de Proteína , Glicoproteína da Espícula de Coronavírus/química , Glicoproteína da Espícula de Coronavírus/genética , Vacinas Atenuadas , Vacinas de DNA , Vacinas de Subunidades , Proteínas não Estruturais Virais/química , Proteínas não Estruturais Virais/genética , Vacinas Virais/genética , Vacinas Virais/metabolismo
3.
Cell Syst ; 11(4): 412-417.e2, 2020 10 21.
Artigo em Inglês | MEDLINE | ID: mdl-32916095

RESUMO

Epidemiological studies show that SARS-CoV-2 infection leads to severe symptoms only in a fraction of patients, but the determinants of individual susceptibility to the virus are still unknown. The major histocompatibility complex (MHC) class I exposes viral peptides in all nucleated cells and is involved in the susceptibility to many human diseases. Here, we use artificial neural networks to analyze the binding of SARS-CoV-2 peptides with polymorphic human MHC class I molecules. In this way, we identify two sets of haplotypes present in specific human populations: the first displays weak binding with SARS-CoV-2 peptides, while the second shows strong binding and T cell propensity. Our work offers a useful support to identify the individual susceptibility to COVID-19 and illustrates a mechanism underlying variations in the immune response to SARS-CoV-2. A record of this paper's transparent peer review process is included in the Supplemental Information.


Assuntos
Betacoronavirus/imunologia , Antígenos de Histocompatibilidade Classe I/imunologia , Redes Neurais de Computação , Peptídeos/imunologia , Polimorfismo Genético , Proteínas Virais/imunologia , Haplótipos , Antígenos de Histocompatibilidade Classe I/química , Antígenos de Histocompatibilidade Classe I/genética , Humanos , Peptídeos/química , Ligação Proteica , Proteínas Virais/química
4.
Cell Syst ; 11(2): 131-144.e6, 2020 08 26.
Artigo em Inglês | MEDLINE | ID: mdl-32721383

RESUMO

We present a combinatorial machine learning method to evaluate and optimize peptide vaccine formulations for SARS-CoV-2. Our approach optimizes the presentation likelihood of a diverse set of vaccine peptides conditioned on a target human-population HLA haplotype distribution and expected epitope drift. Our proposed SARS-CoV-2 MHC class I vaccine formulations provide 93.21% predicted population coverage with at least five vaccine peptide-HLA average hits per person (≥ 1 peptide: 99.91%) with all vaccine peptides perfectly conserved across 4,690 geographically sampled SARS-CoV-2 genomes. Our proposed MHC class II vaccine formulations provide 97.21% predicted coverage with at least five vaccine peptide-HLA average hits per person with all peptides having an observed mutation probability of ≤ 0.001. We provide an open-source implementation of our design methods (OptiVax), vaccine evaluation tool (EvalVax), as well as the data used in our design efforts here: https://github.com/gifford-lab/optivax.


Assuntos
Betacoronavirus/imunologia , Haplótipos , Antígenos de Histocompatibilidade Classe II/genética , Antígenos de Histocompatibilidade Classe I/genética , Análise de Sequência de DNA/métodos , Vacinas de Subunidades/imunologia , Vacinas Virais/imunologia , Betacoronavirus/genética , Infecções por Coronavirus/genética , Infecções por Coronavirus/imunologia , Infecções por Coronavirus/prevenção & controle , Epitopos/química , Epitopos/genética , Epitopos/imunologia , Antígenos de Histocompatibilidade Classe I/química , Antígenos de Histocompatibilidade Classe I/imunologia , Antígenos de Histocompatibilidade Classe II/química , Antígenos de Histocompatibilidade Classe II/imunologia , Humanos , Aprendizado de Máquina , Análise de Sequência de DNA/normas , Vacinas de Subunidades/química , Vacinas de Subunidades/genética , Vacinas Virais/química , Vacinas Virais/genética
5.
J Virol ; 94(17)2020 08 17.
Artigo em Inglês | MEDLINE | ID: mdl-32522857

RESUMO

Rabbits are pivotal domestic animals for both the economy and as an animal model for human diseases. A large number of rabbits have been infected by rabbit hemorrhagic disease virus (RHDV) in natural and artificial pandemics in the past. Differences in presentation of antigenic peptides by polymorphic major histocompatibility complex (MHC) molecules to T-cell receptors (TCR) on T lymphocytes are associated with viral clearance in mammals. Here, we screened and identified a series of peptides derived from RHDV binding to the rabbit MHC class I molecule, RLA-A1. The small, hydrophobic B and F pockets of RLA-A1 capture a peptide motif analogous to that recognized by human class I molecule HLA-A*0201, with more restricted aliphatic anchors at P2 and PΩ positions. Moreover, the rabbit molecule is characterized by an uncommon residue combination of Gly53, Val55, and Glu56, making the 310 helix and the loop between the 310 and α1 helices closer to the α2 helix. A wider A pocket in RLA-A1 can induce a special conformation of the P1 anchor and may play a pivotal role in peptide assembly and TCR recognition. Our study broadens the knowledge of T-cell immunity in domestic animals and also provides useful insights for vaccine development to prevent infectious diseases in rabbits.IMPORTANCE We screened rabbit MHC class I RLA-A1-restricted peptides from the capsid protein VP60 of rabbit hemorrhagic disease virus (RHDV) and determined the structures of RLA-A1 complexed with three peptides, VP60-1, VP60-2, and VP60-10. From the structures, we found that the peptide binding motifs of RLA-A1 are extremely constraining. Thus, there is a generally restricted peptide selection for RLA-A1 compared to that for human HLA-A*0201. In addition, uncommon residues Gly53, Val55, and Glu56 of RLA-A1 are located between the 310 helix and α1 helix, which makes the steric position of the 310 helix in RLA-A1 much closer to the α2 helix than that found in other mammalian MHC class I molecules. This special conformation between the 310 helix and α1 helix plays a pivotal role in rabbit MHC class I assembly. Our results provide new insights into MHC class I molecule assembly and peptide presentation of domestic mammals. Furthermore, these data also broaden our knowledge on T-cell immunity in rabbits and may also provide useful information for vaccine development to prevent infectious diseases in rabbits.


Assuntos
Vírus da Doença Hemorrágica de Coelhos/imunologia , Vírus da Doença Hemorrágica de Coelhos/metabolismo , Antígenos de Histocompatibilidade Classe I/química , Antígenos de Histocompatibilidade Classe I/imunologia , Peptídeos/química , Peptídeos/imunologia , Animais , Antígenos HLA/imunologia , Antígenos de Histocompatibilidade/imunologia , Antígenos de Histocompatibilidade Classe I/genética , Modelos Moleculares , Peptídeos/genética , Ligação Proteica/imunologia , Conformação Proteica , Coelhos , Receptores de Antígenos de Linfócitos T/metabolismo , Alinhamento de Sequência , Linfócitos T/imunologia
6.
Immunogenetics ; 72(5): 295-304, 2020 07.
Artigo em Inglês | MEDLINE | ID: mdl-32577798

RESUMO

Tumor-specific neoantigens are mutated self-peptides presented by tumor cell major histocompatibility complex (MHC) molecules and are necessary to elicit host's anti-cancer cytotoxic T cell responses. It could be specifically recognized by neoantigen-specific T cell receptors (TCRs). However, current wet-lab assays for identifying peptide MHC binding are too expensive and time-consuming to meet the clinical needs. In this study, we developed an in silico method with a deep convolutional neural network (CNN) model, iConMHC, to predict peptide MHC binding affinity. Unlike other in silico methods that only learn from properties of amino acid in neoantigen peptides alone and/or MHCs alone, iConMHC learns from physical and chemical interaction properties between pairwise amino acids from the two molecules. These properties, such as contact potentials and distances in folded proteins, directly affect neoantigen-MHC binding affinity. In addition, IConMHC is a pan-allele model that is capable of making predictions for all the MHC alleles. Even for those rare MHC alleles without training data, iConMHC can make predictions with reasonable accuracy. We benchmarked iConMHC with other commonly used MHC-I binding predictors and found our model performs better than most of the pan-allele models.


Assuntos
Aprendizado Profundo , Antígenos de Histocompatibilidade Classe I/metabolismo , Peptídeos/metabolismo , Alelos , Sequência de Aminoácidos , Antígenos de Neoplasias/química , Antígenos de Neoplasias/metabolismo , Simulação por Computador , Bases de Dados de Proteínas , Antígenos de Histocompatibilidade Classe I/química , Antígenos de Histocompatibilidade Classe I/genética , Humanos , Redes Neurais de Computação , Peptídeos/química , Ligação Proteica , Reprodutibilidade dos Testes
7.
PLoS One ; 15(5): e0232849, 2020.
Artigo em Inglês | MEDLINE | ID: mdl-32421728

RESUMO

Class I Major Histocompatibility Complex (MHC) binds short antigenic peptides with the help of Peptide Loading Complex (PLC), and presents them to T-cell Receptors (TCRs) of cytotoxic T-cells and Killer-cell Immunglobulin-like Receptors (KIRs) of Natural Killer (NK) cells. With more than 10000 alleles, human MHC (Human Leukocyte Antigen, HLA) is the most polymorphic protein in humans. This allelic diversity provides a wide coverage of peptide sequence space, yet does not affect the three-dimensional structure of the complex. Moreover, TCRs mostly interact with HLA in a common diagonal binding mode, and KIR-HLA interaction is allele-dependent. With the aim of establishing a framework for understanding the relationships between polymorphism (sequence), structure (conserved fold) and function (protein interactions) of the human MHC, we performed here a local frustration analysis on pMHC homology models covering 1436 HLA I alleles. An analysis of local frustration profiles indicated that (1) variations in MHC fold are unlikely due to minimally-frustrated and relatively conserved residues within the HLA peptide-binding groove, (2) high frustration patches on HLA helices are either involved in or near interaction sites of MHC with the TCR, KIR, or tapasin of the PLC, and (3) peptide ligands mainly stabilize the F-pocket of HLA binding groove.


Assuntos
Antígenos de Histocompatibilidade Classe I/química , Alelos , Sequência de Aminoácidos , Sítios de Ligação , Sequência Conservada , Genes MHC Classe I , Antígenos de Histocompatibilidade Classe I/imunologia , Humanos , Modelos Moleculares , Fragmentos de Peptídeos/química , Polimorfismo Genético , Ligação Proteica , Conformação Proteica , Dobramento de Proteína , Mapeamento de Interação de Proteínas , Receptores de Antígenos de Linfócitos T/química , Receptores KIR/química , Relação Estrutura-Atividade
8.
Proc Natl Acad Sci U S A ; 117(21): 11636-11647, 2020 05 26.
Artigo em Inglês | MEDLINE | ID: mdl-32404419

RESUMO

Micropolymorphisms within human leukocyte antigen (HLA) class I molecules can change the architecture of the peptide-binding cleft, leading to differences in peptide presentation and T cell recognition. The impact of such HLA variation on natural killer (NK) cell recognition remains unclear. Given the differential association of HLA-B*57:01 and HLA-B*57:03 with the control of HIV, recognition of these HLA-B57 allomorphs by the killer cell immunoglobulin-like receptor (KIR) 3DL1 was compared. Despite differing by only two polymorphic residues, both buried within the peptide-binding cleft, HLA-B*57:01 more potently inhibited NK cell activation. Direct-binding studies showed KIR3DL1 to preferentially recognize HLA-B*57:01, particularly when presenting peptides with positively charged position (P)Ω-2 residues. In HLA-B*57:01, charged PΩ-2 residues were oriented toward the peptide-binding cleft and away from KIR3DL1. In HLA-B*57:03, the charged PΩ-2 residues protruded out from the cleft and directly impacted KIR3DL1 engagement. Accordingly, KIR3DL1 recognition of HLA class I ligands is modulated by both the peptide sequence and conformation, as determined by the HLA polymorphic framework, providing a rationale for understanding differences in clinical associations.


Assuntos
Antígenos de Histocompatibilidade Classe I/genética , Células Matadoras Naturais/fisiologia , Polimorfismo Genético/genética , Antígenos de Histocompatibilidade Classe I/química , Antígenos de Histocompatibilidade Classe I/fisiologia , Humanos , Ativação Linfocitária/genética , Modelos Moleculares , Polimorfismo Genético/fisiologia , Receptores KIR/genética
9.
Nucleic Acids Res ; 48(W1): W449-W454, 2020 07 02.
Artigo em Inglês | MEDLINE | ID: mdl-32406916

RESUMO

Major histocompatibility complex (MHC) molecules are expressed on the cell surface, where they present peptides to T cells, which gives them a key role in the development of T-cell immune responses. MHC molecules come in two main variants: MHC Class I (MHC-I) and MHC Class II (MHC-II). MHC-I predominantly present peptides derived from intracellular proteins, whereas MHC-II predominantly presents peptides from extracellular proteins. In both cases, the binding between MHC and antigenic peptides is the most selective step in the antigen presentation pathway. Therefore, the prediction of peptide binding to MHC is a powerful utility to predict the possible specificity of a T-cell immune response. Commonly MHC binding prediction tools are trained on binding affinity or mass spectrometry-eluted ligands. Recent studies have however demonstrated how the integration of both data types can boost predictive performances. Inspired by this, we here present NetMHCpan-4.1 and NetMHCIIpan-4.0, two web servers created to predict binding between peptides and MHC-I and MHC-II, respectively. Both methods exploit tailored machine learning strategies to integrate different training data types, resulting in state-of-the-art performance and outperforming their competitors. The servers are available at http://www.cbs.dtu.dk/services/NetMHCpan-4.1/ and http://www.cbs.dtu.dk/services/NetMHCIIpan-4.0/.


Assuntos
Apresentação do Antígeno , Antígenos de Histocompatibilidade Classe II/metabolismo , Antígenos de Histocompatibilidade Classe I/metabolismo , Software , Motivos de Aminoácidos , Antígenos de Histocompatibilidade Classe I/química , Antígenos de Histocompatibilidade Classe II/química , Ligantes , Aprendizado de Máquina , Peptídeos/metabolismo
10.
J Hum Genet ; 65(7): 569-575, 2020 Jul.
Artigo em Inglês | MEDLINE | ID: mdl-32372051

RESUMO

To control and prevent the current COVID-19 pandemic, the development of novel vaccines is an emergent issue. In addition, we need to develop tools that can measure/monitor T-cell and B-cell responses to know how our immune system is responding to this deleterious virus. However, little information is currently available about the immune target epitopes of novel coronavirus (SARS-CoV-2) to induce host immune responses. Through a comprehensive bioinformatic screening of potential epitopes derived from the SARS-CoV-2 sequences for HLAs commonly present in the Japanese population, we identified 2013 and 1399 possible peptide epitopes that are likely to have the high affinity (<0.5%- and 2%-rank, respectively) to HLA class I and II molecules, respectively, that may induce CD8+ and CD4+ T-cell responses. These epitopes distributed across the structural (spike, envelope, membrane, and nucleocapsid proteins) and the nonstructural proteins (proteins corresponding to six open reading frames); however, we found several regions where high-affinity epitopes were significantly enriched. By comparing the sequences of these predicted T cell epitopes to the other coronaviruses, we identified 781 HLA-class I and 418 HLA-class II epitopes that have high homologies to SARS-CoV. To further select commonly-available epitopes that would be applicable to larger populations, we calculated population coverages based on the allele frequencies of HLA molecules, and found 2 HLA-class I epitopes covering 83.8% of the Japanese population. The findings in the current study provide us valuable information to design widely-available vaccine epitopes against SARS-CoV-2 and also provide the useful information for monitoring T-cell responses.


Assuntos
Epitopos de Linfócito T/genética , Epitopos de Linfócito T/imunologia , Grupo com Ancestrais do Continente Asiático/genética , Sequência de Bases , Betacoronavirus/imunologia , Coronavirus/genética , Infecções por Coronavirus/imunologia , Infecções por Coronavirus/prevenção & controle , Epitopos de Linfócito T/química , Antígenos de Histocompatibilidade Classe I/química , Antígenos de Histocompatibilidade Classe I/genética , Antígenos de Histocompatibilidade Classe I/imunologia , Antígenos de Histocompatibilidade Classe II/química , Antígenos de Histocompatibilidade Classe II/genética , Antígenos de Histocompatibilidade Classe II/imunologia , Humanos , Mutação , Fases de Leitura Aberta , Vírus da SARS/imunologia , Vacinas Virais/imunologia
11.
Sci Rep ; 10(1): 7667, 2020 05 06.
Artigo em Inglês | MEDLINE | ID: mdl-32376973

RESUMO

Lassa virus (LASV), a member of the Arenaviridae, is an ambisense RNA virus that causes severe hemorrhagic fever with a high fatality rate in humans in West and Central Africa. Currently, no FDA approved drugs or vaccines are available for the treatment of LASV fever. The LASV glycoprotein complex (GP) is a promising target for vaccine or drug development. It is situated on the virion envelope and plays key roles in LASV growth, cell tropism, host range, and pathogenicity. In an effort to discover new LASV vaccines, we employ several sequence-based computational prediction tools to identify LASV GP major histocompatibility complex (MHC) class I and II T-cell epitopes. In addition, many sequence- and structure-based computational prediction tools were used to identify LASV GP B-cell epitopes. The predicted T- and B-cell epitopes were further filtered based on the consensus approach that resulted in the identification of thirty new epitopes that have not been previously tested experimentally. Epitope-allele complexes were obtained for selected strongly binding alleles to the MHC-I T-cell epitopes using molecular docking and the complexes were relaxed with molecular dynamics simulations to investigate the interaction and dynamics of the epitope-allele complexes. These predictions provide guidance to the experimental investigations and validation of the epitopes with the potential for stimulating T-cell responses and B-cell antibodies against LASV and allow the design and development of LASV vaccines.


Assuntos
Mapeamento de Epitopos , Epitopos/química , Epitopos/imunologia , Febre Lassa/imunologia , Vírus Lassa/imunologia , Modelos Moleculares , Alelos , Sequência de Aminoácidos , Mapeamento de Epitopos/métodos , Epitopos/genética , Epitopos de Linfócito B/química , Epitopos de Linfócito B/imunologia , Epitopos de Linfócito T/química , Epitopos de Linfócito T/imunologia , Antígenos de Histocompatibilidade Classe I/química , Antígenos de Histocompatibilidade Classe I/imunologia , Humanos , Febre Lassa/prevenção & controle , Vírus Lassa/genética , Conformação Proteica , Proteínas Virais/química , Proteínas Virais/imunologia , Vacinas Virais/genética , Vacinas Virais/imunologia , Fluxo de Trabalho
12.
PLoS Comput Biol ; 16(5): e1007757, 2020 05.
Artigo em Inglês | MEDLINE | ID: mdl-32453790

RESUMO

T cell epitope candidates are commonly identified using computational prediction tools in order to enable applications such as vaccine design, cancer neoantigen identification, development of diagnostics and removal of unwanted immune responses against protein therapeutics. Most T cell epitope prediction tools are based on machine learning algorithms trained on MHC binding or naturally processed MHC ligand elution data. The ability of currently available tools to predict T cell epitopes has not been comprehensively evaluated. In this study, we used a recently published dataset that systematically defined T cell epitopes recognized in vaccinia virus (VACV) infected C57BL/6 mice (expressing H-2Db and H-2Kb), considering both peptides predicted to bind MHC or experimentally eluted from infected cells, making this the most comprehensive dataset of T cell epitopes mapped in a complex pathogen. We evaluated the performance of all currently publicly available computational T cell epitope prediction tools to identify these major epitopes from all peptides encoded in the VACV proteome. We found that all methods were able to improve epitope identification above random, with the best performance achieved by neural network-based predictions trained on both MHC binding and MHC ligand elution data (NetMHCPan-4.0 and MHCFlurry). Impressively, these methods were able to capture more than half of the major epitopes in the top N = 277 predictions within the N = 767,788 predictions made for distinct peptides of relevant lengths that can theoretically be encoded in the VACV proteome. These performance metrics provide guidance for immunologists as to which prediction methods to use, and what success rates are possible for epitope predictions when considering a highly controlled system of administered immunizations to inbred mice. In addition, this benchmark was implemented in an open and easy to reproduce format, providing developers with a framework for future comparisons against new tools.


Assuntos
Alergia e Imunologia/normas , Epitopos de Linfócito T/imunologia , Antígenos de Histocompatibilidade Classe I/química , Algoritmos , Alelos , Animais , Área Sob a Curva , Automação , Epitopos de Linfócito T/química , Sistema Imunitário , Ligantes , Aprendizado de Máquina , Camundongos , Camundongos Endogâmicos C57BL , Redes Neurais de Computação , Peptídeos/química , Ligação Proteica , Proteoma , Curva ROC , Vírus Vaccinia
13.
Nat Commun ; 11(1): 1909, 2020 04 20.
Artigo em Inglês | MEDLINE | ID: mdl-32312993

RESUMO

Peptide exchange technologies are essential for the generation of pMHC-multimer libraries used to probe diverse, polyclonal TCR repertoires in various settings. Here, using the molecular chaperone TAPBPR, we develop a robust method for the capture of stable, empty MHC-I molecules comprising murine H2 and human HLA alleles, which can be readily tetramerized and loaded with peptides of choice in a high-throughput manner. Alternatively, catalytic amounts of TAPBPR can be used to exchange placeholder peptides with high affinity peptides of interest. Using the same system, we describe high throughput assays to validate binding of multiple candidate peptides on empty MHC-I/TAPBPR complexes. Combined with tetramer-barcoding via a multi-modal cellular indexing technology, ECCITE-seq, our approach allows a combined analysis of TCR repertoires and other T cell transcription profiles together with their cognate antigen specificities in a single experiment. The new approach allows TCR/pMHC interactions to be interrogated easily at large scale.


Assuntos
Proteínas de Transporte/química , Antígenos de Histocompatibilidade Classe I/química , Proteínas de Membrana Transportadoras/química , Chaperonas Moleculares/química , Peptídeos/química , Domínios e Motivos de Interação entre Proteínas , Alelos , Animais , Proteínas de Transporte/genética , Proteínas de Transporte/metabolismo , Biblioteca Gênica , Antígenos de Histocompatibilidade Classe I/genética , Antígenos de Histocompatibilidade Classe I/metabolismo , Humanos , Imunidade Celular , Imunoquímica , Proteínas de Membrana Transportadoras/genética , Proteínas de Membrana Transportadoras/metabolismo , Camundongos , Modelos Moleculares , Chaperonas Moleculares/metabolismo , Linfócitos T
14.
Mol Immunol ; 120: 101-112, 2020 04.
Artigo em Inglês | MEDLINE | ID: mdl-32113130

RESUMO

Histocompatibility Leukocyte Antigens, or HLAs, are one of the most polymorphic molecules in humans. This high degree of polymorphism endows HLA molecules with the ability to present a vast array of peptides, an essential trait for responding to ever-evolving pathogens. Unlike classical HLA molecules (HLA-Ia), some non-classical HLA-Ib molecules, including HLA-E, are almost monomorphic. Several studies show HLA-E can present self-peptides originating from the leader sequence of other HLA molecules, which signals to our immune system that the cell is healthy. Therefore, it was traditionally thought that the chief role of HLA-E in the body was in immune surveillance. However, there is emerging evidence that HLA-E is also able to present pathogen-derived peptides to the adaptive immune system, namely T cells, in a manner that is similar to classical HLA-Ia molecules. Here we describe the early findings of this less conventional role of HLA-E in the adaptive immune system and its importance for immunity.


Assuntos
Antígenos de Histocompatibilidade Classe I/imunologia , Imunidade Adaptativa , Sequência de Aminoácidos , Apresentação do Antígeno/imunologia , Sítios de Ligação , Infecções por Citomegalovirus/imunologia , Infecções por Vírus Epstein-Barr/imunologia , Infecções por HIV/imunologia , Antígeno HLA-A2/química , Antígeno HLA-A2/genética , Antígeno HLA-A2/imunologia , Antígenos de Histocompatibilidade Classe I/química , Antígenos de Histocompatibilidade Classe I/genética , Humanos , Vigilância Imunológica , Células Matadoras Naturais/imunologia , Modelos Moleculares , Polimorfismo Genético , Conformação Proteica , Infecções por Salmonella/imunologia , Homologia de Sequência de Aminoácidos , Linfócitos T/imunologia , Tuberculose/imunologia
15.
J Med Virol ; 92(6): 618-631, 2020 06.
Artigo em Inglês | MEDLINE | ID: mdl-32108359

RESUMO

Recently, a novel coronavirus (SARS-COV-2) emerged which is responsible for the recent outbreak in Wuhan, China. Genetically, it is closely related to SARS-CoV and MERS-CoV. The situation is getting worse and worse, therefore, there is an urgent need for designing a suitable peptide vaccine component against the SARS-COV-2. Here, we characterized spike glycoprotein to obtain immunogenic epitopes. Next, we chose 13 Major Histocompatibility Complex-(MHC) I and 3 MHC-II epitopes, having antigenic properties. These epitopes are usually linked to specific linkers to build vaccine components and molecularly dock on toll-like receptor-5 to get binding affinity. Therefore, to provide a fast immunogenic profile of these epitopes, we performed immunoinformatics analysis so that the rapid development of the vaccine might bring this disastrous situation to the end earlier.


Assuntos
Betacoronavirus/imunologia , Infecções por Coronavirus/prevenção & controle , Epitopos de Linfócito B/química , Epitopos de Linfócito T/química , Pandemias/prevenção & controle , Pneumonia Viral/prevenção & controle , Glicoproteína da Espícula de Coronavírus/química , Receptor 5 Toll-Like/química , Vacinas Virais/química , Sequência de Aminoácidos , Betacoronavirus/genética , Betacoronavirus/patogenicidade , Sítios de Ligação , Biologia Computacional/métodos , Infecções por Coronavirus/imunologia , Infecções por Coronavirus/virologia , Epitopos/química , Epitopos/genética , Epitopos/imunologia , Epitopos de Linfócito B/genética , Epitopos de Linfócito B/imunologia , Epitopos de Linfócito T/genética , Epitopos de Linfócito T/imunologia , Antígenos de Histocompatibilidade Classe I/química , Antígenos de Histocompatibilidade Classe I/genética , Antígenos de Histocompatibilidade Classe I/imunologia , Antígenos de Histocompatibilidade Classe II/química , Antígenos de Histocompatibilidade Classe II/genética , Antígenos de Histocompatibilidade Classe II/imunologia , Humanos , Coronavírus da Síndrome Respiratória do Oriente Médio/genética , Coronavírus da Síndrome Respiratória do Oriente Médio/imunologia , Coronavírus da Síndrome Respiratória do Oriente Médio/patogenicidade , Simulação de Acoplamento Molecular , Pneumonia Viral/imunologia , Pneumonia Viral/virologia , Ligação Proteica , Domínios e Motivos de Interação entre Proteínas , Estrutura Terciária de Proteína , Vírus da SARS/genética , Vírus da SARS/imunologia , Vírus da SARS/patogenicidade , Glicoproteína da Espícula de Coronavírus/genética , Glicoproteína da Espícula de Coronavírus/imunologia , Receptor 5 Toll-Like/genética , Receptor 5 Toll-Like/imunologia , Vacinas de Subunidades , Vacinas Virais/imunologia
16.
Biochim Biophys Acta Gen Subj ; 1864(4): 129535, 2020 04.
Artigo em Inglês | MEDLINE | ID: mdl-31954798

RESUMO

Selecting peptides that bind strongly to the major histocompatibility complex (MHC) for inclusion in a vaccine has therapeutic potential for infections and tumors. Machine learning models trained on sequence data exist for peptide:MHC (p:MHC) binding predictions. Here, we train support vector machine classifier (SVMC) models on physicochemical sequence-based and structure-based descriptor sets to predict peptide binding to a well-studied model mouse MHC I allele, H-2Db. Recursive feature elimination and two-way forward feature selection were also performed. Although low on sensitivity compared to the current state-of-the-art algorithms, models based on physicochemical descriptor sets achieve specificity and precision comparable to the most popular sequence-based algorithms. The best-performing model is a hybrid descriptor set containing both sequence-based and structure-based descriptors. Interestingly, close to half of the physicochemical sequence-based descriptors remaining in the hybrid model were properties of the anchor positions, residues 5 and 9 in the peptide sequence. In contrast, residues flanking position 5 make little to no residue-specific contribution to the binding affinity prediction. The results suggest that machine-learned models incorporating both sequence-based descriptors and structural data may provide information on specific physicochemical properties determining binding affinities.


Assuntos
Antígenos de Histocompatibilidade Classe I/química , Aprendizado de Máquina , Peptídeos/química , Algoritmos , Alelos , Sequência de Aminoácidos , Animais , Antígenos de Histocompatibilidade Classe I/genética , Camundongos , Ligação Proteica , Conformação Proteica
17.
Biochem Biophys Res Commun ; 521(4): 894-899, 2020 01 22.
Artigo em Inglês | MEDLINE | ID: mdl-31711644

RESUMO

Cumulative studies on human immunodeficiency virus (HIV)-infected individuals have shown association of major histocompatibility complex class I (MHC-I) polymorphisms with lower viral load and delayed AIDS progression, suggesting that HIV replication can be controlled by potent CD8+ T-cell responses. We have previously established an AIDS model of simian immunodeficiency virus (SIV) infection in Burmese rhesus macaques and found a potent CD8+ T cell targeting the Mamu-A1*065:01-restricted Gag241-249 epitope, which is located in a region corresponding to the HIV Gag240-249 TW10 epitope restricted by a protective MHC-I allele, HLA-B*57. In the present study, we determined a T cell receptor (TCR) of this Gag241-249 epitope-specific CD8+ T cell. cDNA clones encoding TCR-α and TCR-ß chains were obtained from a Gag241-249-specific CD8+ T-cell clone. Coexpression of these TCR-α and TCR-ß cDNAs resulted in reconstitution of a functional TCR specifically detected by Gag241-249 epitope-Mamu-A1*065:01 tetramer. Two of three previously-reported CD8+ T-cell escape mutations reduced binding affinity of Gag241-249 peptide to Mamu-A1*065:01 but the remaining one not. This is consistent with the data obtained by molecular modeling of the epitope-MHC-I complex and TCR. These results would contribute to understanding how viral CD8+ T-cell escape mutations are selected under structural constraint of viral proteins.


Assuntos
Linfócitos T CD8-Positivos/virologia , Receptores de Antígenos de Linfócitos T/metabolismo , Síndrome de Imunodeficiência Adquirida dos Símios/imunologia , Animais , Linfócitos T CD8-Positivos/imunologia , Clonagem Molecular , Modelos Animais de Doenças , Epitopos/química , Epitopos/genética , Epitopos/metabolismo , Produtos do Gene gag/imunologia , Genes MHC Classe I/imunologia , Antígenos de Histocompatibilidade Classe I/química , Antígenos de Histocompatibilidade Classe I/genética , Antígenos de Histocompatibilidade Classe I/imunologia , Antígenos de Histocompatibilidade Classe I/metabolismo , Macaca mulatta , Receptores de Antígenos de Linfócitos T/química , Receptores de Antígenos de Linfócitos T/genética , Vírus da Imunodeficiência Símia/patogenicidade
18.
Front Immunol ; 10: 2731, 2019.
Artigo em Inglês | MEDLINE | ID: mdl-31824508

RESUMO

Recent clinical developments in antitumor immunotherapy involving T-cell related therapeutics have led to a renewed interest for human leukocyte antigen class I (HLA-I) binding peptides, given their potential use as peptide vaccines. Databases of HLA-I binding peptides hold therefore information on therapeutic targets essential for understanding immunity. In this work, we use in depth and accurate HLA-I peptidomics datasets determined by mass-spectrometry (MS) and analyze properties of the HLA-I binding peptides with structure-based computational approaches. HLA-I binding peptides are studied grouping all alleles together or in allotype-specific contexts. We capitalize on the increasing number of structurally determined proteins to (1) map the 3D structure of HLA-I binding peptides into the source proteins for analyzing their secondary structure and solvent accessibility in the protein context, and (2) search for potential differences between these properties in HLA-I binding peptides and in a reference dataset of HLA-I motif-like peptides. This is performed by an in-house developed heuristic search that considers peptides across all the human proteome and converges to a collection of peptides that exhibit exactly the same motif as the HLA-I peptides. Our results, based on 9-mers matched to protein 3D structures, clearly show enriched sampling for HLA-I presentation of helical fragments in the source proteins. This enrichment is significant, as compared to 9-mer HLA-I motif-like peptides, and is not entirely explained by the helical propensity of the preferred residues in the HLA-I motifs. We give possible hypothesis for the secondary structure biases observed in HLA-I peptides. This contribution is of potential interest for researchers working in the field of antigen presentation and proteolysis. This knowledge refines the understanding of the rules governing antigen presentation and could be added to the parameters of the current peptide-MHC class I binding predictors to increase their antigen predictive ability.


Assuntos
Bases de Dados de Proteínas , Antígenos de Histocompatibilidade Classe I/química , Peptídeos/química , Motivos de Aminoácidos , Antígenos de Histocompatibilidade Classe I/imunologia , Humanos , Ligantes , Espectrometria de Massas , Peptídeos/imunologia
19.
Proc Natl Acad Sci U S A ; 116(51): 25602-25613, 2019 12 17.
Artigo em Inglês | MEDLINE | ID: mdl-31796585

RESUMO

The interplay between a highly polymorphic set of MHC-I alleles and molecular chaperones shapes the repertoire of peptide antigens displayed on the cell surface for T cell surveillance. Here, we demonstrate that the molecular chaperone TAP-binding protein related (TAPBPR) associates with a broad range of partially folded MHC-I species inside the cell. Bimolecular fluorescence complementation and deep mutational scanning reveal that TAPBPR recognition is polarized toward the α2 domain of the peptide-binding groove, and depends on the formation of a conserved MHC-I disulfide epitope in the α2 domain. Conversely, thermodynamic measurements of TAPBPR binding for a representative set of properly conformed, peptide-loaded molecules suggest a narrower MHC-I specificity range. Using solution NMR, we find that the extent of dynamics at "hotspot" surfaces confers TAPBPR recognition of a sparsely populated MHC-I state attained through a global conformational change. Consistently, restriction of MHC-I groove plasticity through the introduction of a disulfide bond between the α1/α2 helices abrogates TAPBPR binding, both in solution and on a cellular membrane, while intracellular binding is tolerant of many destabilizing MHC-I substitutions. Our data support parallel TAPBPR functions of 1) chaperoning unstable MHC-I molecules with broad allele-specificity at early stages of their folding process, and 2) editing the peptide cargo of properly conformed MHC-I molecules en route to the surface, which demonstrates a narrower specificity. Our results suggest that TAPBPR exploits localized structural adaptations, both near and distant to the peptide-binding groove, to selectively recognize discrete conformational states sampled by MHC-I alleles, toward editing the repertoire of displayed antigens.


Assuntos
Antígenos de Histocompatibilidade Classe I , Chaperonas Moleculares , Peptídeos , Dissulfetos/química , Antígenos de Histocompatibilidade Classe I/química , Antígenos de Histocompatibilidade Classe I/metabolismo , Humanos , Imunoglobulinas/química , Imunoglobulinas/metabolismo , Proteínas de Membrana/química , Proteínas de Membrana/metabolismo , Modelos Moleculares , Chaperonas Moleculares/química , Chaperonas Moleculares/metabolismo , Ressonância Magnética Nuclear Biomolecular , Peptídeos/química , Peptídeos/metabolismo , Conformação Proteica , Domínios Proteicos
20.
J Biol Chem ; 294(49): 18545-18546, 2019 12 06.
Artigo em Inglês | MEDLINE | ID: mdl-31811048

RESUMO

A critical step in antigen presentation is the degradative processing of peptides by aminopeptidases in the endoplasmic reticulum. It is unclear whether these enzymes act only on free peptides or on those bound to their major histocompatibility complex (MHC)-I-presenting molecules. A recent study examined the structure and biophysics of N-terminally extended peptides in complex with MHC-I, revealing the conformational adjustment of MHC to permit both binding of the peptide core and exposure of the peptide N terminus. These data suggest a mechanism by which aminopeptidase access is determined and offer an explanation for how longer peptides may be displayed at the cell surface.


Assuntos
Antígenos de Histocompatibilidade Classe I/química , Antígenos de Histocompatibilidade Classe I/metabolismo , Peptídeos/química , Peptídeos/metabolismo , Aminopeptidases/química , Aminopeptidases/metabolismo , Animais , Apresentação do Antígeno/fisiologia , Retículo Endoplasmático/metabolismo , Humanos , Ligação Proteica , Conformação Proteica , Especificidade por Substrato
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