Class I molecules with similar peptide-binding specificities are the result of both common ancestry and convergent evolution

2003 ◽  
Vol 54 (12) ◽  
pp. 830-841 ◽  
Author(s):  
Alessandro Sette ◽  
John Sidney ◽  
Brian D. Livingston ◽  
John L. Dzuris ◽  
Claire Crimi ◽  
...  
2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Piotr Minias ◽  
Ke He ◽  
Peter O. Dunn

Abstract Background The Major Histocompatibility Complex (MHC) codes for the key vertebrate immune receptors responsible for pathogen recognition. Foreign antigens are recognized via their compatibility to hyper-variable region of the peptide-binding groove (PBR), which consists of two separate protein domains. Specifically, the PBR of the MHC class I receptors, which recognize intra-cellular pathogens, has two α domains encoded by exon 2 (α1) and exon 3 (α2) of the same gene. Most research on avian MHC class I polymorphism has traditionally focused exclusively on exon 3 and comparisons of selection between the two domains have been hampered by the scarcity of molecular data for exon 2. Thus, it is not clear whether the two domains vary in their specificity towards different antigens and whether they are subject to different selective pressure. Results Here, we took advantage of rapidly accumulating genomic resources to test for the differences in selection patterns between both MHC class I domains of the peptide-binding groove in birds. For this purpose, we compiled a dataset of MHC class I exon 2 and 3 sequences for 120 avian species from 46 families. Our phylogenetically-robust approach provided strong evidence for highly consistent levels of selection on the α1 and α2 domains. There were strong correlations in all selection measures (number of positively/negatively selected residues and dN/dS ratios) between both PBR exons. Similar positive associations were found for the level of amino acid polymorphism across the two domains. Conclusions We conclude that the strength of selection and the level of polymorphism are highly consistent between both peptide-binding domains (α1 and α2) of the avian MHC class I.


1992 ◽  
Vol 22 (6) ◽  
pp. 1405-1412 ◽  
Author(s):  
Ton N. M. Schumacher ◽  
Grada M. Van Bleek ◽  
Marie-ThéRèSe Heemels ◽  
Karl Deres ◽  
Ka Wan Li ◽  
...  

2017 ◽  
Vol 199 (10) ◽  
pp. 3679-3690 ◽  
Author(s):  
Natasja G. de Groot ◽  
Corrine M. C. Heijmans ◽  
Arnoud H. de Ru ◽  
George M. C. Janssen ◽  
Jan W. Drijfhout ◽  
...  

2020 ◽  
Vol 21 (4) ◽  
pp. 1119-1135 ◽  
Author(s):  
Shutao Mei ◽  
Fuyi Li ◽  
André Leier ◽  
Tatiana T Marquez-Lago ◽  
Kailin Giam ◽  
...  

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.


2011 ◽  
Vol 63 (12) ◽  
pp. 821-834 ◽  
Author(s):  
Lasse Eggers Pedersen ◽  
Mikkel Harndahl ◽  
Michael Rasmussen ◽  
Kasper Lamberth ◽  
William T. Golde ◽  
...  

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