Improved structural method for T-cell cross-reactivity prediction

Mendes, Marcus Fabiano de Almeida; Antunes, Dinler Amaral; Rigo, Maurício; Sinigaglia, Marialva; Vieira, Gustavo Fioravanti

doi:10.1016/j.molimm.2015.06.017

Cited by 22 publications

(35 citation statements)

References 42 publications

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“…The clonal TCR will bind the multimers in a hierarchy governed by the TCR-pMHC affinity, which is reflected in the hierarchy of DNA barcode reads after sequencing. [90][91][92][93][94][95][96], along with the prospects of describing and understanding some of the elementary relationships between antigen specificity and the cross-recognition potential of a TCR [97].…”

Section: Implications For Immune Therapymentioning

confidence: 99%

T-cell-receptor cross-recognition and strategies to select safe T-cell receptors for clinical translation

Bentzen

Hadrup

2019

Immuno-Oncology and Technology

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Adoptive transfer of T-cell-receptor (TCR)-transduced T cells has shown promising results for cancer treatment, but has also produced severe immunotoxicities caused by on-target as well as off-target TCR recognition. Off-target toxicities are related to the ability of a single T cell to cross-recognize and respond to several different peptide–major histocompatibility complex (pMHC) antigens; a property that is essential for providing broad antigenic coverage despite a confined number of unique TCRs in the human body. However, this degeneracy makes it incredibly difficult to account for the range of targets that any TCR might recognize, which represents a major challenge for the clinical development of therapeutic TCRs. The prospect of using affinity-optimized TCRs has been impeded due to observations that affinity enhancement might alter the specificity of a TCR, thereby increasing the risk that it will cross-recognize endogenous tissue. Strategies for selecting safe TCRs for the clinic have included functional assessment after individual incubations with tissue-derived primary cells or with peptides substituted with single amino acids. However, these strategies have not been able to predict cross-recognition sufficiently, leading to fatal cross-reactivity in clinical trials. Novel technologies have emerged that enable extensive characterization of the exact interaction points of a TCR with pMHC, which provides a foundation from which to make predictions of the cross-recognition potential of individual TCRs. This review describes current advances in strategies for dissecting the molecular interaction points of TCRs, focusing on their potential as tools for predicting cross-recognition of TCRs in clinical development.

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Section: Implications For Immune Therapymentioning

confidence: 99%

T-cell-receptor cross-recognition and strategies to select safe T-cell receptors for clinical translation

Bentzen

Hadrup

2019

Immuno-Oncology and Technology

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“…Although cross-reactivity has become a well-recognized phenomenon in recent decades, we cannot say the same about the structural basis governing heterologous T cell recognition. We performed studies which demonstrated molecular variations in the TCR contact surfaces of pMHC complexes, and their impact on the stimulation of cytotoxic responses [18,19]. These in silico approaches explained previously in in vitro and in vivo assays were not elucidated by single comparisons of epitopes amino acid sequences [54].…”

Section: Target Prediction and Cross-reactivitymentioning

confidence: 99%

“…It demands a deeper analysis, from a structural point of view. Evidence for this comes from evidence that very similar peptide sequences can generate dissimilar pMHC surfaces (that come into contact with the TCR), while nonrelated peptide sequences could present almost identical pMHC surfaces (regarding topography and charge distribution, key elements for TCR recognition) [18][19][20]. An understanding of these sequences/structure correspondences will make it possible to infer immunogenic fingerprints, as well as autoimmunity and cross-reactivity trigger identification.…”

Section: Structural Immunoinformatica In Viral Infections-the Rationamentioning

confidence: 99%

“…These structures should be compared with pMHC-I structures of previously-described immunogenic targets, which are already contained in our CrossTope Database [23]. This comparison could be performed by hierarchical clustering analysis, for example, as depicted in [18,19], or by a direct electrostatic potential data comparison through MatchTope (tool in development, personal communication). The prospective peptides from viral proteins, which are highly similar to immunogenic ones, are recovered as the most promising targets, and these peptide sequences are used to synthesize the tetramers for posterior testing (workflow summarized in Figure 1).…”

Section: Structural Immunoinformatica In Viral Infections-the Rationamentioning

confidence: 99%

“…We must understand how T-cell receptors differentiate self and nonself pMHC complexes [9,15,[49][50][51]. It seems that the molecular aspects contained in these complexes, such as particularities in topography and charge distribution, are involved in immunogenicity discrimination [18][19][20]. They probably influence the TCR/pMHC interaction and stability that can culminate in the death of the virus-infected or tumoral cell.…”

Section: Target Prediction and Cross-reactivitymentioning

confidence: 99%

See 2 more Smart Citations

pMHC Structural Comparisons as a Pivotal Element to Detect and Validate T-Cell Targets for Vaccine Development and Immunotherapy—A New Methodological Proposal

Vianna

Mendes

Bragatte

et al. 2019

Cells

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The search for epitopes that will effectively trigger an immune response remains the “El Dorado” for immunologists. The development of promising immunotherapeutic approaches requires the appropriate targets to elicit a proper immune response. Considering the high degree of HLA/TCR diversity, as well as the heterogeneity of viral and tumor proteins, this number will invariably be higher than ideal to test. It is known that the recognition of a peptide-MHC (pMHC) by the T-cell receptor is performed entirely in a structural fashion, where the atomic interactions of both structures, pMHC and TCR, dictate the fate of the process. However, epitopes with a similar composition of amino acids can produce dissimilar surfaces. Conversely, sequences with no conspicuous similarities can exhibit similar TCR interaction surfaces. In the last decade, our group developed a database and in silico structural methods to extract molecular fingerprints that trigger T-cell immune responses, mainly referring to physicochemical similarities, which could explain the immunogenic differences presented by different pMHC-I complexes. Here, we propose an immunoinformatic approach that considers a structural level of information, combined with an experimental technology that simulates the presentation of epitopes for a T cell, to improve vaccine production and immunotherapy efficacy.

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A new in silico approach to investigate molecular aspects of factor IX missense causative mutations and their impact on the hemophilia B severity

et al. 2019

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Factor IX (encoded by F9) is a protein in the coagulation process, where its lack or deficiency leads to hemophilia B. This condition has been much less studied than hemophilia A, especially in Latin America. We analyzed the structural and functional impact of 54 missense mutations (18 reported by us previously, and 36 other mutations from the Factor IX database) through molecular modeling approaches. To accomplish this task, we examine the electrostatic patterns, hydrophobicity/hydrophilicity, disulfide, and H‐bond differences of the Factor IX structures harboring the missense mutations found, correlating them with their clinical effects. The 54 mutated sequences were modeled and their physicochemical features were determined and used as input in clusterization tools. The electrostatic pattern seems to influence in disease severity, especially for mutations investigated in epidermal growth factors 1 and 2 (EGF1/2) domains. The combined use of all physicochemical information improved the clustering of structures associated to similar phenotypes, especially for mutations from GLA and EGF1–2 domains. The effect of mutations in the disease phenotype severity seems to be a complex interplay of molecular features, each one contributing to different impacts. This highlights that previous studies and tools analyzing individually single features for single mutations are missing elements that fulfill the whole picture.

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Improved structural method for T-cell cross-reactivity prediction

Cited by 22 publications

References 42 publications

T-cell-receptor cross-recognition and strategies to select safe T-cell receptors for clinical translation

T-cell-receptor cross-recognition and strategies to select safe T-cell receptors for clinical translation

pMHC Structural Comparisons as a Pivotal Element to Detect and Validate T-Cell Targets for Vaccine Development and Immunotherapy—A New Methodological Proposal

A new in silico approach to investigate molecular aspects of factor IX missense causative mutations and their impact on the hemophilia B severity

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