An integrated approach to the characterization of immune repertoires using AIMS: An Automated Immune Molecule Separator

Boughter, Christopher T.; Meier-Schellersheim, Martin

doi:10.1371/journal.pcbi.1011577

Cited by 4 publications

(2 citation statements)

References 66 publications

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“…Using AIMS, a recently developed software ( Boughter et al, 2020 ; Boughter and Meier-Schellersheim, 2023 ) for encoding and analyzing amino acid sequences and their biophysical properties in the context of molecular interactions, we found that across all tested TRAV, TRBV, and HLA alleles, there is no evidence of strongly conserved germline-encoded interactions that exist for all possible combinations of TRAV/TRBV genes and HLA polymorphisms. The sequence-level diversity in the germline-encoded TCR CDR loops alone makes such interactions highly unlikely.…”

Section: Discussionmentioning

confidence: 97%

“…In the work reported here, we explore how much insight about TCR-MHC interactions can be gained using a simplified, pseudo-structural representation of the biophysics of protein interactions that scores amino acid interactions as either productive, neutral, or counter-productive with regard to complex formation. Using the Automated Immune Molecule Separator (AIMS) software ( Boughter et al, 2020 ; Boughter and Meier-Schellersheim, 2023 ) we generated the first systematic characterization of every germline-encoded TRAV-MHC and TRBV-MHC interaction through such a pseudo-structural approach. The AIMS architecture allows us to analyze the CDR1 and CDR2 contributions to interactions with MHC molecules independent of CDR3, providing a not previously explored antigen-independent perspective on the TCR-MHC complex.…”

Section: Introductionmentioning

confidence: 99%

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Conserved biophysical compatibility among the highly variable germline-encoded regions shapes TCR-MHC interactions

Boughter,

Meier-Schellersheim

2023

eLife

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T cells are critically important components of the adaptive immune system primarily responsible for identifying and responding to pathogenic challenges. This recognition of pathogens is driven by the interaction between membrane-bound T cell receptors (TCRs) and antigenic peptides presented on major histocompatibility complex (MHC) molecules. The formation of the TCR-peptide-MHC complex (TCR-pMHC) involves interactions among germline-encoded and hypervariable amino acids. Germline-encoded and hypervariable regions can form contacts critical for complex formation, but only interactions between germline-encoded contacts are likely to be shared across many of all the possible productive TCR-pMHC complexes. Despite this, experimental investigation of these interactions have focused on only a small fraction of the possible interaction space. To address this, we analyzed every possible germline-encoded TCR-MHC contact in humans, thereby generating the first comprehensive characterization of these largely antigen-independent interactions. Our computational analysis suggests that germline-encoded TCR-MHC interactions that are conserved at the sequence level are rare due to the high amino acid diversity of the TCR CDR1 and CDR2 loops, and that such conservation is unlikely to dominate the dynamic protein-protein binding interface. Instead, we propose that binding properties such as the docking orientation are defined by regions of biophysical compatibility between these loops and the MHC surface.

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Section: Discussionmentioning

confidence: 97%

Section: Introductionmentioning

confidence: 99%

Conserved biophysical compatibility among the highly variable germline-encoded regions shapes TCR-MHC interactions

Boughter,

Meier-Schellersheim

2023

eLife

Self Cite

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Integrating Dynamic Network Analysis with AI for Enhanced Epitope Prediction in PD-L1:Affibody Interactions

Gomes,

Yang,

Vanella

et al. 2024

J. Am. Chem. Soc.

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Understanding binding epitopes involved in protein–protein interactions and accurately determining their structure are long-standing goals with broad applicability in industry and biomedicine. Although various experimental methods for binding epitope determination exist, these approaches are typically low throughput and cost-intensive. Computational methods have potential to accelerate epitope predictions; however, recently developed artificial intelligence (AI)-based methods frequently fail to predict epitopes of synthetic binding domains with few natural homologues. Here we have developed an integrated method employing generalized-correlation-based dynamic network analysis on multiple molecular dynamics (MD) trajectories, initiated from AlphaFold2Multimer structures, to unravel the structure and binding epitope of the therapeutic PD-L1:Affibody complex. Both AlphaFold2 and conventional molecular dynamics trajectory analysis were ineffective in distinguishing between two proposed binding models, parallel and perpendicular. However, our integrated approach, utilizing dynamic network analysis, demonstrated that the perpendicular mode was significantly more stable. These predictions were validated using a suite of experimental epitope mapping protocols, including cross-linking mass spectrometry and next-generation sequencing-based deep mutational scanning. Conversely, AlphaFold3 failed to predict a structure bound in the perpendicular pose, highlighting the necessity for exploratory research in the search for binding epitopes and challenging the notion that AI-generated protein structures can be accepted without scrutiny. Our research underscores the potential of employing dynamic network analysis to enhance AI-based structure predictions for more accurate identification of protein–protein interaction interfaces.

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Limits on inferring T cell specificity from partial information

Henderson,

Nagano,

Milighetti

et al. 2024

Proc. Natl. Acad. Sci. U.S.A.

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A key challenge in molecular biology is to decipher the mapping of protein sequence to function. To perform this mapping requires the identification of sequence features most informative about function. Here, we quantify the amount of information (in bits) that T cell receptor (TCR) sequence features provide about antigen specificity. We identify informative features by their degree of conservation among antigen-specific receptors relative to null expectations. We find that TCR specificity synergistically depends on the hypervariable regions of both receptor chains, with a degree of synergy that strongly depends on the ligand. Using a coincidence-based approach to measuring information enables us to directly bound the accuracy with which TCR specificity can be predicted from partial matches to reference sequences. We anticipate that our statistical framework will be of use for developing machine learning models for TCR specificity prediction and for optimizing TCRs for cell therapies. The proposed coincidence-based information measures might find further applications in bounding the performance of pairwise classifiers in other fields.

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An integrated approach to the characterization of immune repertoires using AIMS: An Automated Immune Molecule Separator

Cited by 4 publications

References 66 publications

Conserved biophysical compatibility among the highly variable germline-encoded regions shapes TCR-MHC interactions

Conserved biophysical compatibility among the highly variable germline-encoded regions shapes TCR-MHC interactions

Integrating Dynamic Network Analysis with AI for Enhanced Epitope Prediction in PD-L1:Affibody Interactions

Limits on inferring T cell specificity from partial information

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