Biology subjects

Postovskaya, A.

Publications and source records attributed to Postovskaya, A..

3 recordsLinked to original sources

bioRxiv2022

Benchmarking solutions to the T-cell receptor epitope prediction problem: IMMREP22 workshop report

Many different solutions to predicting the cognate epitope target of a T-cell receptor (TCR) have been proposed. However several questions on the advantages and disadvantages of these different approaches remain unresolved, as most methods have only been evaluated within the context of their initial publications and data sets. Here, we report the findings of the first public TCR-epitope prediction benchmark performed on 23 prediction models in the context of the ImmRep 2022 TCR-epitope specificity workshop. This benchmark revealed that the use of paired-chain alpha-beta, as well as CDR1/2 or V/J information, when available, improves classification obtained with CDR3 data, independent of the underlying approach. In addition, we found that straight-forward distance-based approaches can achieve a respectable performance when compared to more complex machine-learning models. Finally, we highlight the need for a truly independent follow-up benchmark and provide recommendations for the design of such a next benchmark.

bioinformatics↗

bioRxiv2020

Tracking SARS-CoV-2 T cells with epitope T-cell receptor recognition models

Despite the general agreement on the importance of T cells during SARS-CoV-2 infection, the clinical impact of specific and cross-reactive T-cell responses remains uncertain, while this knowledge may indicate how to adjust vaccines and maintain robust long-term protection against continuously emerging variants. To characterize CD8+ T-cell response to epitopes unique to SARS-CoV-2 (SC-unique) or shared with other coronaviruses (CoV-common), we trained a large number of TCR-epitope recognition models for MHC-I-presented SARS-CoV-2 epitopes from publicly available data. Applying those models to longitudinal COVID-19 TCR repertoires of critical and non-critical COVID-19 patients, we discovered that notwithstanding comparable CD8+ T-cell depletion and the sizes of putative CoV-common CD8+ TCR repertoires in all symptomatic patients at the initial stage of the disease, the temporal dynamics of putative SC2-unique TCRs differed depending on the disease severity. Only non-critical patients had developed large and diverse SC2-unique CD8+ T-cell response by the second week of the disease. Additionally, only this patient group demonstrated redundancy in CD8+ TCRs putatively recognizing unique and common SARS-CoV-2 epitopes. Our findings thus emphasize the role of the de novo CD8+ T-cell response and support the argument against the clinical benefit of pre-existing cross-reactive CD8+ T cells. Now, the analytical framework of this study can not only be employed to track specific and cross-reactive SARS-CoV-2 CD8+ T cells in any TCR repertoire but also be generalized to more epitopes and be employed for adaptive immune response assessment and monitoring to inform public health decisions.

bioinformatics↗

bioRxiv2019

Treating biomolecular interaction as an image classification problem - a case study on T-cell receptor-epitope recognition prediction

The prediction of epitope recognition by T-cell receptors (TCRs) has seen many advancements in recent years, with several methods now available that can predict recognition for a specific set of epitopes. However, the generic case of evaluating all possible TCR-epitope pairs remains challenging, mainly due to the high diversity of the interacting sequences and the limited amount of currently available training data. In this work, we provide an overview of the current state of this unsolved problem. First, we examine appropriate validation strategies to accurately assess the generalization performance of generic TCR-epitope recognition models when applied to both known and novel epitopes. In addition, we present a novel feature representation approach which we call ImRex (interaction map recognition). This approach is based on the pairwise combination of physicochemical properties of the individual amino acids in the CDR3 and epitope sequences, which provides a convolutional neural network with the combined representation of both sequences. Lastly, we highlight various challenges that are particular to TCR-epitope data and that can adversely affect model performance. These include the issue of selecting negative data, the imbalanced epitope distribution of curated TCR-epitope datasets, and the potential exchangeability of TCR alpha and beta chains. Our results indicate that while extrapolation to novel epitopes remains a difficult challenge, ImRex makes this feasible for a subset of epitopes that are not too dissimilar from the training data. We show that appropriate feature engineering methods and rigorous benchmark standards are required to create and validate TCR-epitope predictive models.

bioinformatics↗