MHC-II dynamics are maintained in HLA-DR allotypes to ensure catalyzed peptide exchange

Abualrous, Esam T.; Stolzenberg, Sebastian; Sticht, Jana; Wieczorek, Marek; Roske, Yvette; Günther, Matthias; Dähn, Steffen; Boesen, Benedikt B; Calvo, Marcos Martínez; Biese, Charlotte; Kuppler, Frank; Medina-García, Álvaro; Álvaro-Benito, Miguel; Höfer, Thomas; Noé, Frank; Freund, Christian

doi:10.1038/s41589-023-01316-3

Cited by 15 publications

(7 citation statements)

References 60 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Our method demonstrates the potential of integrating experimental and in silico approaches to access and understand immunogenicity. We conclude that the impact of allotype-specific parameters such as DM-susceptibility 46 , antigen-related features (e.g. SASA or protein foldedness) 44,45 and experimentally definable processing constraints (e.g.…”

Section: Discussionmentioning

confidence: 92%

Effective assessment of CD4⁺T cell Immunodominance patterns: impact of antigen processing and HLA restriction

Álvaro-Benito,

Abualrous,

Lingel

et al. 2024

Preprint

Self Cite

View full text Add to dashboard Cite

Identifying T cell epitopes is essential for studying and potentially tuning immune responses to pathogens. The polymorphic nature of major histocompatibility complex of class II (MHCII)-genes, and the complexity of the antigen processing mechanisms hinders the effective prediction of immunodominant patterns in humans, specially at the population level. Here, we combined the output of a reconstituted antigen processing system and ofin silicoprediction tools for SARS-CoV-2 antigens considering a broad-population coverage DRB1* panel to gain insights on immunodominance patterns. The two methods complement each other, and the resulting model improves upon single positive predictive values (PPV) from each of them to explain known epitopes. This model was used to design a minimalistic peptide pool (59 peptides) matching the performance reported for large overlapping peptide pools (> 500 peptides). Furthermore, almost 70 % of the candidates (23 peptides) selected for a frequent HLA background (DRB1*03:01/*07:01) feature immunodominant responsesex vivo, validating our platform for accessing T cell epitopes at the population level. The analysis of the impact of processing constraints reveals distinct impact of proteolysis and solvent accessible surface area on epitope selection depending on the antigen. Thus, considering these properties for antigens in question should improve available epitope prediction tools.

show abstract

Section: Discussionmentioning

confidence: 92%

Effective assessment of CD4⁺T cell Immunodominance patterns: impact of antigen processing and HLA restriction

Álvaro-Benito,

Abualrous,

Lingel

et al. 2024

Preprint

Self Cite

View full text Add to dashboard Cite

show abstract

“…Since the publication of AlphaFold in July 2021, one new HLA‐DRB1 allele has been crystallized and stored in the PDB ( DRB1*07:01 , https://doi.org/10.2210/pdb7Z0Q/pdb)22). 22 The pairwise root mean square difference in dihedral angles (RMSDA) between five AlphaFold‐predicted DRB1*07:01 structures and the de novo crystallization is compared to the pairwise RMSDA of eight DRB1*01:01 experimental structures.…”

Section: Methodsmentioning

confidence: 99%

Repeated local ellipsoid protrusion supplements HLA surface characterization

Niemann,

Matern,

Spierings

2023

HLA

View full text Add to dashboard Cite

Allorecognition of donor HLA is a major risk factor for long‐term kidney graft survival. Although several molecular matching algorithms have been proposed that compare physiochemical and structural features of the donors' and recipients' HLA proteins in order to predict their compatibility, the exact underlying mechanisms are still not fully understood. We hypothesized that the ElliPro approach of single ellipsoid fitting and protrusion ranking lacks sensitivity for the characteristic shape of HLA molecules and developed a prediction pipeline named Snowball that is fitting smaller ellipsoids iteratively to substructures. Aggregated protrusion ranks of locally fitted ellipsoids were calculated for 712 publicly available HLA structures and 78 predicted structures using AlphaFold 2. Amino‐acid sequence and protrusion ranks were used to train deep neural network predictors to infer protrusion ranks for all known HLA sequences. Snowball protrusion ranks appear to be more sensitive than ElliPro scores in fine parts of the HLA such as the helix structures forming the HLA binding groove in particular when the ellipsoids are fitted to substructures considering atoms within a 15 Å radius. A cloud‐based web service was implemented based on amino‐acid matching considering both protein‐ and position‐specific surface area and protrusion ranks extending the previously presented Snowflake prediction pipeline.

show abstract

“…Understanding the mechanism of peptide-MHC (pMHC) binding raises an intriguing research question regarding how MHC molecules effectively bind to a wide range of peptides while maintaining strong binding and specificity. Previous research focusing on the structural aspects of pMHC complexes has provided valuable insights into our understanding of antigen presentation specificity (3) and peptide binding dynamics (4,5). Allele-specific residues at anchor positions and complementary pockets in the MHC molecule play a significant role in determining the promiscuity and specificity of peptide recognition by MHC molecules (6,7).…”

Section: Introductionmentioning

confidence: 99%

PANDORA v2.0: Benchmarking peptide-MHC II models and software improvements

Parizi¹,

Marzella²,

Ramakrishnan³

et al. 2023

Preprint

View full text Add to dashboard Cite

T-cell specificity to differentiate between self and non-self relies on T-cell receptor (TCR) recognition of peptides presented by the Major Histocompatibility Complex (MHC). Investigations into the three-dimensional (3D) structures of peptide:MHC (pMHC) complexes have provided valuable insights of MHC functions. Given the limited availability of experimental pMHC structures and considerable diversity of peptides and MHC alleles, it calls for the development of efficient and reliable computational approaches for modeling pMHC structures. Here we present an update of PANDORA and the systematic evaluation of its performance in modelling 3D structures of pMHC class II complexes (pMHC-II), which play a key role in the cancer immune response. PANDORA is a modelling software that can build low-energy models in a few minutes by restraining peptide residues inside the MHC-II binding groove. We benchmarked PANDORA on 136 experimentally determined pMHC-II structures covering 44 unique alpha;beta chain pairs. Our pipeline achieves a median backbone Ligand-Root Mean Squared Deviation (L-RMSD) of 0.42 Angstrom on the binding core and 0.88 Angstrom on the whole peptide for the benchmark dataset. We incorporated software improvements to make PANDORA a pan-allele framework and improved the user interface and software quality. Its computational efficiency allows enriching the wealth of pMHC binding affinity and mass spectrometry data with 3D models. These models can be used as a starting point for molecular dynamics simulations or structure-boosted deep learning algorithms to identify MHC-binding peptides. PANDORA is available as a Python package through Conda or as a source installation at https://github.com/X-lab-3D/PANDORA .

show abstract

MHC-II dynamics are maintained in HLA-DR allotypes to ensure catalyzed peptide exchange

Cited by 15 publications

References 60 publications

Effective assessment of CD4⁺T cell Immunodominance patterns: impact of antigen processing and HLA restriction

Effective assessment of CD4⁺T cell Immunodominance patterns: impact of antigen processing and HLA restriction

Repeated local ellipsoid protrusion supplements HLA surface characterization

PANDORA v2.0: Benchmarking peptide-MHC II models and software improvements

Contact Info

Product

Resources

About

MHC-II dynamics are maintained in HLA-DR allotypes to ensure catalyzed peptide exchange

Cited by 15 publications

References 60 publications

Effective assessment of CD4+T cell Immunodominance patterns: impact of antigen processing and HLA restriction

Effective assessment of CD4+T cell Immunodominance patterns: impact of antigen processing and HLA restriction

Repeated local ellipsoid protrusion supplements HLA surface characterization

PANDORA v2.0: Benchmarking peptide-MHC II models and software improvements

Contact Info

Product

Resources

About

Effective assessment of CD4⁺T cell Immunodominance patterns: impact of antigen processing and HLA restriction

Effective assessment of CD4⁺T cell Immunodominance patterns: impact of antigen processing and HLA restriction