Characterization of the binding profile of peptide to transporter associated with antigen processing (TAP) using Gaussian process regression

Ren, Yonggang; Wu, Bin; Pan, Yuzhu; Lv, Fenglin; Kong, Xiangjun; Luo, Xiao; Li, Yuanchao; Yang, Qingwu

doi:10.1016/j.compbiomed.2011.07.004

Cited by 11 publications

(8 citation statements)

References 32 publications

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“…Multiple molecules are involved in HLA class I-restricted antigen presentation, among which transporters associated with antigen processing (TAPs) are responsible for the transportation of cytosol peptides into endoplasmic reticulum. A recent investigation on Gaussian process (GP) model has shown that the P1, P2, P3, and P9 residues are pivotal positions that dominate TAP-peptide recognition (Ren et al, 2011) while P2 and P9 residues are anchoring residues for binding to HLA class I molecules. We then evaluated the hydrophobicity and hydrophilicity of amino acid residues on these sites (Zhu et al, 2016).…”

Section: Discussionmentioning

confidence: 99%

Identification of HLA-A2-Restricted Mycobacterial Lipoprotein Z Peptides Recognized by T CellsFrom Patients With ActiveTuberculosis Infection

Liu

Wang

et al. 2018

Front. Microbiol.

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Identification of HLA-restricted peptides derived from mycobacterial antigens that are endowed with high affinity and strong antigenicity is not only of interest in tuberculosis (TB) diagnostics and treatment efficacy evaluation, but might also provide potential candidates for the development of therapeutic vaccines against drug-resistant TB. Our previous work demonstrated that lipoprotein Z (LppZ) displayed high immunogenicity and antigenicity in active TB patients. In the present study, ten HLA-A2-restricted LppZ peptides (LppZp1-10) were predicted by bioinformatics, among which LppZp7 and LppZp10 were verified to possess high affinity to HLA-A2 molecules using T2 cell-based affinity binding assay. Moreover, results from ELISpot assay showed that both LppZp7 and LppZp10 peptides were able to induce more IFN-γ producing cells upon ex vivo stimulation of PBMC from HLA-A2+ active TB (ATB) patients as compared to those from healthy controls (HCs). Also, the numbers of LppZp7 and LppZp10-specific IFN-γ producing cells exhibited positive correlations with those of ESAT-6 peptide (E6p) or CFP-10 peptide (C10p) in ATB. Interestingly, stimulation with LppZp7/p10 mixture was able to induce higher intracellular expression of IFN-γ and IL-2 cytokines in CD8+ and CD4+ T cells from ATB as compared to HC, associated with lower expression of TNF-α in both CD8+ and CD4+ T cells. Taken together, HLA-A2-restricted LppZp7 and LppZp10 peptides display high immunoreactivity in HLA-matched ATB patients demonstrated by high responsiveness in both CD8+ and CD4+ T cells. With the ability to induce strong antigen-specific cellular responses, LppZp7 and LppZp10 are of potential value for the future applications in the prevention and control of TB.

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Section: Discussionmentioning

confidence: 99%

Identification of HLA-A2-Restricted Mycobacterial Lipoprotein Z Peptides Recognized by T CellsFrom Patients With ActiveTuberculosis Infection

Liu

Wang

et al. 2018

Front. Microbiol.

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“…GP were firstly introduced in the field of QSAR modeling by Burden et al [30]. Later on, GP were also used for: (i) the modeling of ADMET properties, [31,32] (ii) the prediction of electrolyte solubility, [33] (iii) the bioactivity prediction of small peptide datasets, [34-36] (iv) protein engineering, [37] and (v) the bioactivity prediction of bioactivity-focused (GPCRs) combinatorial chemolibraries [38]. The purpose of the present study is to propose Gaussian Process (GP) to simultaneously model chemical and multispecies protein information in the frame of PCM.…”

Section: Introductionmentioning

confidence: 99%

Proteochemometric modeling in a Bayesian framework

et al. 2014

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Proteochemometrics (PCM) is an approach for bioactivity predictive modeling which models the relationship between protein and chemical information. Gaussian Processes (GP), based on Bayesian inference, provide the most objective estimation of the uncertainty of the predictions, thus permitting the evaluation of the applicability domain (AD) of the model. Furthermore, the experimental error on bioactivity measurements can be used as input for this probabilistic model.In this study, we apply GP implemented with a panel of kernels on three various (and multispecies) PCM datasets. The first dataset consisted of information from 8 human and rat adenosine receptors with 10,999 small molecule ligands and their binding affinity. The second consisted of the catalytic activity of four dengue virus NS3 proteases on 56 small peptides. Finally, we have gathered bioactivity information of small molecule ligands on 91 aminergic GPCRs from 9 different species, leading to a dataset of 24,593 datapoints with a matrix completeness of only 2.43%.GP models trained on these datasets are statistically sound, at the same level of statistical significance as Support Vector Machines (SVM), with R02 values on the external dataset ranging from 0.68 to 0.92, and RMSEP values close to the experimental error. Furthermore, the best GP models obtained with the normalized polynomial and radial kernels provide intervals of confidence for the predictions in agreement with the cumulative Gaussian distribution. GP models were also interpreted on the basis of individual targets and of ligand descriptors. In the dengue dataset, the model interpretation in terms of the amino-acid positions in the tetra-peptide ligands gave biologically meaningful results.

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“…a transporter associated with antigen processing protein before they can bind MHC groove and trigger an immune response. 13 The correlation between MHC-I and the severity of the disease in patients with COVID-19 has been previously hypothesised. 14 The understanding of which SARS-CoV-2 epitopes are immunogenic may help advances in the development of diagnostic kits and prophylactic vaccines.…”

Section: Original Researchmentioning

confidence: 94%

“…During the process of presentation of antigenic peptides to CD8 + (cytotoxic) T cells by Class I Major Histocompatibility Complex (MHC-I) molecules, peptides are generated by proteasomal cleavage in the cytosol and transported to the lumen of the endoplasmic reticulum by a transporter associated with antigen processing protein before they can bind MHC groove and trigger an immune response. 13 The correlation between MHC-I and the severity of the disease in patients with COVID-19 has been previously hypothesised. 14 …”

Section: Introductionmentioning

confidence: 94%

Immunoinformatic approach to assess SARS-CoV-2 protein S epitopes recognised by the most frequent MHC-I alleles in the Brazilian population

et al. 2020

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AimsBrazil is nowadays one of the epicentres of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) pandemic and new therapies are needed to face it. In the context of specific immune response against the virus, a correlation between Major Histocompatibility Complex Class I (MHC-I) and the severity of the disease in patients with COVID-19 has been suggested. Aiming at better understanding the biology of the infection and the immune response against the virus in the Brazilian population, we analysed SARS-CoV-2 protein S peptides in order to identify epitopes able to elicit an immune response mediated by the most frequent MHC-I alleles using in silico methods.MethodsOur analyses consisted in searching for the most frequent Human Leukocyte Antigen (HLA)-A, HLA-B and HLA-C alleles in the Brazilian population, excluding the genetic isolates; then, we performed: molecular modelling for unsolved structures, MHC-I binding affinity and antigenicity prediction, peptide docking and molecular dynamics of the best fitted MHC-I/protein S complexes.ResultsWe identified 24 immunogenic epitopes in the SARS-CoV-2 protein S that could interact with 17 different MHC-I alleles (namely, HLA-A*01:01; HLA-A*02:01; HLA-A*11:01; HLA-A*24:02; HLA-A*68:01; HLA-A*23:01; HLA-A*26:01; HLA-A*30:02; HLA-A*31:01; HLA-B*07:02; HLA-B*51:01; HLA-B*35:01; HLA-B*44:02; HLA-B*35:03; HLA-C*05:01; HLA-C*07:01 and HLA-C*15:02) in the Brazilian population.ConclusionsBeing aware of the intrinsic limitations of in silico analysis (mainly the differences between the real and the Protein Data Bank (PDB) structure; and accuracy of the methods for simulate proteasome cleavage), we identified 24 epitopes able to interact with 17 MHC-I more frequent alleles in the Brazilian population that could be useful for the development of strategic methods for vaccines against SARS-CoV-2.

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Characterization of the binding profile of peptide to transporter associated with antigen processing (TAP) using Gaussian process regression

Cited by 11 publications

References 32 publications

Identification of HLA-A2-Restricted Mycobacterial Lipoprotein Z Peptides Recognized by T CellsFrom Patients With ActiveTuberculosis Infection

Identification of HLA-A2-Restricted Mycobacterial Lipoprotein Z Peptides Recognized by T CellsFrom Patients With ActiveTuberculosis Infection

Proteochemometric modeling in a Bayesian framework

Immunoinformatic approach to assess SARS-CoV-2 protein S epitopes recognised by the most frequent MHC-I alleles in the Brazilian population

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