ATLAS: A database linking binding affinities with structures for wild-type and mutant TCR-pMHC complexes

Borrman, Tyler; Cimons, Jennifer M.; Cosiano, Michael; Purcaro, Michael J.; Pierce, Brian G.; Baker, Brian M.; Weng, Zhiping

doi:10.1002/prot.25260

Cited by 63 publications

(63 citation statements)

References 40 publications

(55 reference statements)

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“…A main reason for this might be the lack of large amounts of experimental data for specific complexes. To increase the amount of relevant data for immune protein complexes, Sirin et al curated the AB‐Bind database for antibody complexes and Borrman et al built the ATLAS database for TCR‐pMHC complexes. The general ΔΔ G predictors, such as FoldX and mCSM, were tested on AB‐Bind data, showing low predictive performance (PCC: 0.34 and 0.35 for FoldX and mCSM, respectively) .…”

Section: Predicting Binding Affinity Changes In Ppismentioning

confidence: 99%

Finding the ΔΔG spot: Are predictors of binding affinity changes upon mutations in protein–protein interactions ready for it?

Geng

Roel‐Touris

et al. 2019

WIREs Comput Mol Sci

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Predicting the structure and thermodynamics of protein–protein interactions (PPIs) are key to a proper understanding and modulation of their function. Since experimental methods might not be able to catch up with the fast growth of genomic data, computational alternatives are therefore required. We present here a review dealing with various aspects of predicting binding affinity changes upon mutations (ΔΔG). We focus on predictors that consider three‐dimensional structure information to estimate the impact of mutations on the binding affinity of a protein–protein complex, excluding the rigorous free energy perturbation methods. Training and evaluation, ΔΔG databases, data selection, and existing ΔΔG predictors are specially emphasized. We also establish the parallel with scoring functions used in docking since those share many similar PPI features with ΔΔG predictors. The field has seen a common evolution of ΔΔG predictors and scoring functions over time, transforming from purely energetic functions to statistical energy‐based and further to machine learning‐based functions. As machine learning has come to age, limitations in terms of quantity, quality and variety of the available data become the bottlenecks for the future development of these computational methods. This can be alleviated by building infrastructures for data generation, collection and sharing. Further developments can be catalyzed by conducting community‐wide blind challenges for method assessment. This article is categorized under: Structure and Mechanism > Molecular Structures Structure and Mechanism > Computational Biochemistry and Biophysics Molecular and Statistical Mechanics > Molecular Interactions

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Section: Predicting Binding Affinity Changes In Ppismentioning

confidence: 99%

Finding the ΔΔG spot: Are predictors of binding affinity changes upon mutations in protein–protein interactions ready for it?

Geng

Roel‐Touris

et al. 2019

WIREs Comput Mol Sci

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“…They can be discerned via sequence landscapes as noted above (47), but have been more traditionally defined as regions where mutations have the greatest impact on binding (57). TCRs where hot spots have been explored through point mutations are listed in a recently developed online database (https://zlab.umassmed.edu/atlas/web/) (58). But point mutants are almost always to alanine, a rather limited exploration of chemical space (51, 59).…”

Section: Rationalizing the Specificity/cross-reactivity Duality Of Tcrsmentioning

confidence: 99%

Emerging Concepts in TCR Specificity: Rationalizing and (Maybe) Predicting Outcomes

Singh

Riley

Baker

et al. 2017

The Journal of Immunology

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T cell specificity emerges from a myriad of processes, ranging from the biological pathways that control T cell signaling to the structural and physical mechanisms that influence how TCRs bind antigen/MHC. Of these processes, the binding specificity of the TCR is a key component. However, TCR specificity is enigmatic: TCRs are at once specific but also cross-reactive. Although long-appreciated, this duality continues to puzzle immunologists and has implications for the development of TCR-based therapeutics. Here we review TCR specificity, emphasizing results that have emerged from structural and physical studies of TCR binding. We show how the TCR specificity/cross-reactivity duality can be rationalized from structural and biophysical principles. There is excellent agreement between predictions from these principles and classic predictions about the scope of TCR cross-reactivity. We demonstrate how these same principles can also explain amino acid preferences in immunogenic epitopes and highlight opportunities for structural considerations in predictive immunology.

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“…Given that αβ TCRs generally employ a diagonal binding mode to engage pMHC-I antigens where the CDR3α and CDR3β TCR loops form direct contacts with key peptide residues (39, 40), knowledge of the surface features for different epitopes adds an extra layer of information to interpret sequence variability between different viral strains. For other important antigens with known structures in the PDB, such features can be derived from an annotated database connecting pMHC-I/TCR co-crystal structures with biophysical binding data (41), and were recently employed in an artificial neural network approach to predict the immunogenicity of different HLA-A*02:01 bound peptides in the context of tumor neoantigen display (42). A separate study has shown that the electrostatic compatibility between self vs foreign HLA surfaces can be used to determine antibody alloimmune responses (43).…”

Section: Resultsmentioning

confidence: 99%

Structure-based modeling of SARS-CoV-2 peptide/HLA-A02 antigens

Nerli

Sgourakis

2020

Preprint

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11As a first step toward the development of diagnostic and therapeutic tools to fight the Coronavirus 12 disease , it is important to characterize CD8+ T cell epitopes in the SARS-CoV-2 13 peptidome that can trigger adaptive immune responses. Here, we use RosettaMHC, a comparative 14 modeling approach which leverages existing high-resolution X-ray structures from peptide/MHC 15 complexes available in the Protein Data Bank, to derive physically realistic 3D models for high- 16affinity SARS-CoV-2 epitopes. We outline an application of our method to model 439 9mer and 17 279 10mer predicted epitopes displayed by the common allele HLA-A*02:01, and we make our 18 models publicly available through an online database (https://rosettamhc.chemistry.ucsc.edu). As 19 more detailed studies on antigen-specific T cell recognition become available, RosettaMHC 20 models of antigens from different strains and HLA alleles can be used as a basis to understand the 21 link between peptide/HLA complex structure and surface chemistry with immunogenicity, in the 22 context of SARS-CoV-2 infection. 24An ongoing pandemic caused by the novel SARS coronavirus (SARS-CoV-2) has become the 25 focus of extensive efforts to develop vaccines and antiviral therapies (1). Immune modulatory 26 interferons, which promote a widespread antiviral reaction in infected cells, and inhibition of pro-27 inflammatory cytokine function through anti-IL-6/IL-6R antibodies, have been proposed as 28 possible COVID-19 therapies (2, 3). However, stimulating a targeted T cell response against 29 specific viral antigens is hampered by a lack of detailed knowledge of the immunodominant 30 epitopes displayed by common Human Leukocyte Antigen (HLA) alleles across individuals 31 (public epitopes). The molecules of the class I major histocompatibility complex (MHC-I, or HLA 32 in humans) display on the cell surface a diverse pool of 8 to 15 amino acid peptides derived from 33 the endogenous processing of proteins expressed inside the cell (4). This MHC-I restriction of 34 peptide antigens provides jawed vertebrates with an essential mechanism for adaptive immunity: 35 surveillance of the displayed peptide/MHC-I (pMHC-I) molecules by CD8+ cytotoxic T-36 lymphocytes allows detection of aberrant protein expression patterns, which signify viral infection 37 and can trigger an adaptive immune response (5). A recent study has shown important changes in 38 T cell compartments during the acute phase of SARS-CoV-2 infection (6), suggesting that the 39 ability to quantify antigen-specific T cells would provide new avenues for understanding the 40 (which was not certified by peer review) : bioRxiv preprint 2 expansion and contraction of the TCR repertoire in different disease cohorts and clinical settings. 41Given the reduction in breadth and functionality of the naïve T cell repertoire during aging (7), 42 identifying a minimal set of viral antigens that can elicit a protective response will enable the 43 design of diagnostic tools to monitor critical gaps in the T cell repertoir...

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ATLAS: A database linking binding affinities with structures for wild-type and mutant TCR-pMHC complexes

Cited by 63 publications

References 40 publications

Finding the ΔΔG spot: Are predictors of binding affinity changes upon mutations in protein–protein interactions ready for it?

Finding the ΔΔG spot: Are predictors of binding affinity changes upon mutations in protein–protein interactions ready for it?

Emerging Concepts in TCR Specificity: Rationalizing and (Maybe) Predicting Outcomes

Structure-based modeling of SARS-CoV-2 peptide/HLA-A02 antigens

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