scholarly journals TransMut: a program to predict HLA-I peptide binding and optimize mutated peptides for vaccine design by the Transformer-derived self-attention model

2021 ◽  
Author(s):  
Yanyi Chu ◽  
Yan Zhang ◽  
Qiankun Wang ◽  
Lingfeng Zhang ◽  
Xuhong Wang ◽  
...  
Keyword(s):  
Peptide Binding ◽  
Human Leukocyte ◽  
Vaccine Design ◽  
Computational Prediction ◽  
Human Papilloma Virus Vaccine ◽  
Binding Prediction ◽  
Papilloma Virus ◽  
Attention Model ◽  
Leukocyte Antigen ◽  
Speed Up

Abstract Computational prediction of the interaction between human leukocyte antigen (HLA) and peptide (pHLA) can speed up epitope screening and vaccine design. Here, we develop the TransMut framework composed of TransPHLA for pHLA binding prediction and AOMP for mutated peptide optimization, which can be generalized to any binding and mutation task of biomolecules. Firstly, TransPHLA is developed by using a Transformer-derived self-attention model to predict pHLA binding, which is significantly superior to 11 previous methods on pHLA binding prediction, neoantigen and human papilloma virus vaccine identification. For vaccine design, the AOMP program is then developed to automatically optimize mutated peptides to search for mutant peptides with higher affinity to the target HLA and with high homology to the source peptide. Among 3660 non-binding pHLAs, 3630 were successfully mutated. Of these, 94% were verified by the IEDB recommended method, and 88% have homology higher than 80% to the source peptide.

scholarly journals The molecular basis of how buried human leukocyte antigen polymorphism modulates natural killer cell function

2020 ◽  
Vol 117 (21) ◽  
pp. 11636-11647 ◽  
Author(s):  
Philippa M. Saunders ◽  
Bruce J. MacLachlan ◽  
Phillip Pymm ◽  
Patricia T. Illing ◽  
Yuanchen Deng ◽  
...  
Keyword(s):  
Human Leukocyte Antigen ◽  
Natural Killer ◽  
Nk Cell ◽  
Killer Cell ◽  
Peptide Binding ◽  
Human Leukocyte ◽  
Hla Class I ◽  
Cell Recognition ◽  
Leukocyte Antigen ◽  
Binding Cleft

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.

scholarly journals Negative trade-off between neoantigen repertoire breadth and the specificity of HLA-I molecules shapes antitumour immunity

2020 ◽  
Author(s):  
Máté Manczinger ◽  
Gergő Balogh ◽  
Benjamin Tamás Papp ◽  
Balázs Koncz ◽  
Leó Asztalos ◽  
...  
Keyword(s):  
Immune Response ◽  
Viral Infections ◽  
Peptide Binding ◽  
Human Leukocyte ◽  
Tumour Microenvironment ◽  
Trade Off ◽  
Leukocyte Antigen ◽  
Peptide Binding Specificity ◽  
Worse Prognosis ◽  
Inhibitor Treatment

AbstractThe human leukocyte antigen class I (HLA-I) genes shape our immune response against pathogens and cancer. Certain HLA-I variants can bind a much wider range of peptides than others, a feature that could be favorable against a range of viral diseases. However, the implications of this phenomenon on cancer immune response is unknown. In this paper, we quantified peptide repertoire breadth (or promiscuity) of a representative set of HLA-I alleles, and found that cancer patients that carry HLA-I alleles with high peptide binding promiscuity are characterized by significantly worse prognosis after immune checkpoint inhibitor treatment. This trend can be explained by a reduced capacity of promiscuous HLA-I molecules to discriminate between human self and tumour peptides, yielding a shift in regulation of T-cells in the tumour microenvironment from activation to tolerance. In summary, HLA-I peptide binding specificity shapes neopeptide immunogenicity and the self-immunopeptidome repertoire in an antagonistic manner. It could also underlie a negative trade-off between antitumour immunity and the genetic susceptibility to viral infections.

scholarly journals Vaccinomics and Adversomics in the Era of Precision Medicine: A Review Based on HBV, MMR, HPV, and COVID-19 Vaccines

2020 ◽  
Vol 9 (11) ◽  
pp. 3561
Author(s):  
Jasna Omersel ◽  
Nataša Karas Kuželički
Keyword(s):  
Hepatitis B ◽  
Precision Medicine ◽  
Scientific Literature ◽  
Human Leukocyte ◽  
Vaccine Safety ◽  
Public Confidence ◽  
Serious Adverse Events ◽  
Papilloma Virus ◽  
Vaccination Rates ◽  
Leukocyte Antigen

Precision medicine approaches based on pharmacogenomics are now being successfully implemented to enable physicians to predict more efficient treatments and prevention strategies for a given disease based on the genetic background of the patient. This approach has already been proposed for vaccines, but research is lagging behind the needs of society, and precision medicine is far from being implemented here. While vaccinomics concerns the effectiveness of vaccines, adversomics concerns their side effects. This area has great potential to address public concerns about vaccine safety and to promote increased public confidence, higher vaccination rates, and fewer serious adverse events in genetically predisposed individuals. The aim here is to explore the contemporary scientific literature related to the vaccinomic and adversomic aspects of the three most-controversial vaccines: those against hepatitis B, against measles, mumps, and rubella, and against human Papilloma virus. We provide detailed information on the genes that encode human leukocyte antigen, cytokines and their receptors, and transcription factors and regulators associated with the efficacy and safety of the Hepatitis B and Measles, Mumps and Rubella virus vaccines. We also investigate the future prospects of vaccinomics and adversomics of a COVID-19 vaccine, which might represent the fastest development of a vaccine ever.

scholarly journals Electrostatic modifications of the human leukocyte antigen-DR P9 peptide-binding pocket and susceptibility to primary sclerosing cholangitis

Hepatology ◽  
2011 ◽  
Vol 53 (6) ◽  
pp. 1967-1976 ◽  
Author(s):  
Johannes R. Hov ◽  
Vasilis Kosmoliaptsis ◽  
James A. Traherne ◽  
Marita Olsson ◽  
Kirsten M. Boberg ◽  
...  
Keyword(s):  
Human Leukocyte Antigen ◽  
Primary Sclerosing Cholangitis ◽  
Sclerosing Cholangitis ◽  
Peptide Binding ◽  
Human Leukocyte ◽  
Binding Pocket ◽  
Leukocyte Antigen

MHC2NNZ: A novel peptide binding prediction approach for HLA DQ molecules

2017 ◽  
Vol 31 (19-21) ◽  
pp. 1740086 ◽  
Author(s):  
Jiang Xie ◽  
Xu Zeng ◽  
Dongfang Lu ◽  
Zhixiang Liu ◽  
Jiao Wang
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Sequence Similarity ◽  
Human Leukocyte ◽  
Computational Prediction ◽  
Binding Prediction ◽  
Mhc Ii ◽  
Hla Dq ◽  
Artificial Neural ◽  
Prediction Approach

The major histocompatibility complex class II (MHC-II) molecule plays a crucial role in immunology. Computational prediction of MHC-II binding peptides can help researchers understand the mechanism of immune systems and design vaccines. Most of the prediction algorithms for MHC-II to date have made large efforts in human leukocyte antigen (HLA, the name of MHC in Human) molecules encoded in the DR locus. However, HLA DQ molecules are equally important and have only been made less progress because it is more difficult to handle them experimentally. In this study, we propose an artificial neural network-based approach called MHC2NNZ to predict peptides binding to HLA DQ molecules. Unlike previous artificial neural network-based methods, MHC2NNZ not only considers sequence similarity features but also captures the chemical and physical properties, and a novel method incorporating these properties is proposed to represent peptide flanking regions (PFR). Furthermore, MHC2NNZ improves the prediction accuracy by combining with amino acid preference at more specific positions of the peptides binding core. By evaluating on 3549 peptides binding to six most frequent HLA DQ molecules, MHC2NNZ is demonstrated to outperform other state-of-the-art MHC-II prediction methods.

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