HLA Class I Supertype Classification Based on Structural Similarity

Shen, Yue; Parks, Jerry M.; Smith, Jeremy C.

doi:10.4049/jimmunol.2200685

Cited by 10 publications

(11 citation statements)

References 102 publications

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“…HLA-Clus provides an SD metric for predicting the similarity of peptide binding specificity between HLA class I alleles and two clustering methods: hierarchical clustering and nearest-neighbor clustering. The performance of the SD metric and hierarchical clustering method was demonstrated in our previous article [ 15 ].…”

Section: Resultsmentioning

confidence: 99%

“…The details of implemented methods have been described in detail previously [ 15 ] but are briefly summarized here.…”

Section: Methodsmentioning

confidence: 99%

“…The HLA-Clus package accepts 3D structures of HLA class I α chains as input. In the previous study we modeled 449 populated HLA class I alleles [ 15 ]. The structures are available at [ 25 ] for download and further investigation.…”

Section: Methodsmentioning

confidence: 99%

“…Among the 102 alleles that have a prediction model, four are invalid according to the IPD-IMGT/HLA database (version 3.50), including three LOMO alleles, and were therefore excluded from further analysis. Structural models of the 98 valid alleles were generated using ColabFold as described previously [ 15 ]. Next, each of the remaining 17 alleles was clustered using the nearest-neighbor method together with the remaining 97 alleles, and the closest model was selected according to the clustering result.…”

Section: Methodsmentioning

confidence: 99%

“…We recently presented an HLA class I structure-based supertype and subtype classification method that combines multiple targeted solutions [ 15 ]. We briefly summarize the approach here.…”

Section: Introductionmentioning

confidence: 99%

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HLA-Clus: HLA class I clustering based on 3D structure

2023

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Background In a previous paper, we classified populated HLA class I alleles into supertypes and subtypes based on the similarity of 3D landscape of peptide binding grooves, using newly defined structure distance metric and hierarchical clustering approach. Compared to other approaches, our method achieves higher correlation with peptide binding specificity, intra-cluster similarity (cohesion), and robustness. Here we introduce HLA-Clus, a Python package for clustering HLA Class I alleles using the method we developed recently and describe additional features including a new nearest neighbor clustering method that facilitates clustering based on user-defined criteria. Results The HLA-Clus pipeline includes three stages: First, HLA Class I structural models are coarse grained and transformed into clouds of labeled points. Second, similarities between alleles are determined using a newly defined structure distance metric that accounts for spatial and physicochemical similarities. Finally, alleles are clustered via hierarchical or nearest-neighbor approaches. We also interfaced HLA-Clus with the peptide:HLA affinity predictor MHCnuggets. By using the nearest neighbor clustering method to select optimal allele-specific deep learning models in MHCnuggets, the average accuracy of peptide binding prediction of rare alleles was improved. Conclusions The HLA-Clus package offers a solution for characterizing the peptide binding specificities of a large number of HLA alleles. This method can be applied in HLA functional studies, such as the development of peptide affinity predictors, disease association studies, and HLA matching for grafting. HLA-Clus is freely available at our GitHub repository (https://github.com/yshen25/HLA-Clus).

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Section: Resultsmentioning

confidence: 99%

“…The details of implemented methods have been described in detail previously [ 15 ] but are briefly summarized here.…”

Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

“…We recently presented an HLA class I structure-based supertype and subtype classification method that combines multiple targeted solutions [ 15 ]. We briefly summarize the approach here.…”

Section: Introductionmentioning

confidence: 99%

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HLA-Clus: HLA class I clustering based on 3D structure

2023

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Continuing Discoveries in Immunogenetics and Computational Immunology: An Update

Russo,

Crispino,

Lafuente

et al. 2024

Reference Module in Life Sciences

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Broadening alloselectivity of T cell receptors by structure guided engineering

Karuppiah,

Sangani,

Whaley

et al. 2024

Sci Rep

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Specificity of a T cell receptor (TCR) is determined by the combination of its interactions to the peptide and human leukocyte antigen (HLA). TCR-based therapeutic molecules have to date targeted a single peptide in the context of a single HLA allele. Some peptides are presented on multiple HLA alleles, and by engineering TCRs for specific recognition of more than one allele, there is potential to expand the targetable patient population. Here, as a proof of concept, we studied two TCRs, S2 and S8, binding to the PRAME peptide antigen (ELFSYLIEK) presented by HLA alleles HLA-A*03:01 and HLA-A*11:01. By structure-guided affinity maturation targeting a specific residue on the HLA surface, we show that the affinity of the TCR can be modulated for different alleles. Using a combination of affinity maturation and functional T cell assay, we demonstrate that an engineered TCR can target the same peptide on two different HLA alleles with similar affinity and potency. This work highlights the importance of engineering alloselectivity for designing TCR based therapeutics suitable for differing global populations. Supplementary Information The online version contains supplementary material available at 10.1038/s41598-024-75140-7.

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HLA Class I Supertype Classification Based on Structural Similarity

Cited by 10 publications

References 102 publications

HLA-Clus: HLA class I clustering based on 3D structure

HLA-Clus: HLA class I clustering based on 3D structure

Continuing Discoveries in Immunogenetics and Computational Immunology: An Update

Broadening alloselectivity of T cell receptors by structure guided engineering

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