Adaptive immune system uses T cell receptors (TCRs) to recognize pathogens and to consequently initiate immune responses. TCRs can be sequenced from individuals and methods analyzing the specificity of the TCRs can help us better understand individuals’ immune status in different disorders. For this task, we have developed TCRGP, a novel Gaussian process method that predicts if TCRs recognize specified epitopes. TCRGP can utilize the amino acid sequences of the complementarity determining regions (CDRs) from TCRα and TCRβ chains and learn which CDRs are important in recognizing different epitopes. Our comprehensive evaluation with epitope-specific TCR sequencing data shows that TCRGP achieves on average higher prediction accuracy in terms of AUROC score than existing state-of-the-art methods in epitope-specificity predictions. We also propose a novel analysis approach for combined single-cell RNA and TCRαβ (scRNA+TCRαβ) sequencing data by quantifying epitope-specific TCRs with TCRGP and identify HBV-epitope specific T cells and their transcriptomic states in hepatocellular carcinoma patients.

T cell receptors (TCRs) can recognize various pathogens and consequently start immune responses. TCRs can be sequenced from individuals and methods that can analyze the specificity of the TCRs can help us better understand the individual's immune status in different diseases. We have developed TCRGP, a novel Gaussian process (GP) method that can predict if TCRs recognize certain epitopes. This method can utilize different CDR sequences from both TCRα and TCRβ chains from single-cell data and learn which CDRs are important in recognizing the different epitopes. We have experimented with one previously presented and one new data set and show that TCRGP outperforms other state-of-the-art methods in predicting the epitope specificity of TCRs on both data sets. The software implementation and data sets are available at https://github.com/emmijokinen/TCRGP.

Background: Relatlimab+nivolumab (anti-LAG3+anti-PD1) has been approved by FDA as a 1 st -line therapy in stage III/IV melanoma, but its detailed effect on the immune system is unknown. Methods:We evaluated blood samples from 40 immunotherapy-naïve or prior immunotherapy-refractory patients with metastatic melanoma treated with anti-LAG3+anti-PD1 in a phase I trial (NCT01968109) using single-cell RNA and T cell receptor (TCR) sequencing (scRNA+TCRαβ-seq) combined with other multiomics profiling. Results:The highest LAG3 expression was noted in NK cells, regulatory T cells (Tregs), and CD8+ T cells, and these cell populations underwent the most significant changes during the treatment. Adaptive NK cells were enriched in responders and underwent profound transcriptomic changes during the therapy resulting in an active phenotype. LAG3+ Tregs expanded but based on the transcriptome profile became metabolically silent during the treatment. Lastly, higher baseline TCR clonality was observed in responding patients, and their expanding CD8+ T cell clones gained more cytotoxic and NK-like phenotype. Conclusion:Anti-LAG3+anti-PD1 therapy has profound effects on NK cells and Tregs in addition to CD8+ T cells.

In this work, deoxyribose-5-phosphate aldolase (Ec DERA, EC 4.1.2.4) from Escherichia coli was chosen as the protein engineering target for improving the substrate preference towards smaller, non-phosphorylated aldehyde donor substrates, in particular towards acetaldehyde. The initial broad set of mutations was directed to 24 amino acid positions in the active site or in the close vicinity, based on the 3D complex structure of the E. coli DERA wild-type aldolase. The specific activity of the DERA variants containing one to three amino acid mutations was characterised using three different substrates. A novel machine learning (ML) model utilising Gaussian processes and feature learning was applied for the 3rd mutagenesis round to predict new beneficial mutant combinations. This led to the most clear-cut (two- to threefold) improvement in acetaldehyde (C2) addition capability with the concomitant abolishment of the activity towards the natural donor molecule glyceraldehyde-3-phosphate (C3P) as well as the non-phosphorylated equivalent (C3). The Ec DERA variants were also tested on aldol reaction utilising formaldehyde (C1) as the donor. Ec DERA wild-type was shown to be able to carry out this reaction, and furthermore, some of the improved variants on acetaldehyde addition reaction turned out to have also improved activity on formaldehyde. Key points • DERA aldolases are promiscuous enzymes. • Synthetic utility of DERA aldolase was improved by protein engineering approaches. • Machine learning methods aid the protein engineering of DERA.

Motivation T cells use T cell receptors (TCRs) to recognize small parts of antigens, called epitopes, presented by major histocompatibility complexes. Once an epitope is recognized, an immune response is initiated and T cell activation and proliferation by clonal expansion begin. Clonal populations of T cells with identical TCRs can remain in the body for years, thus forming immunological memory and potentially mappable immunological signatures, which could have implications in clinical applications including infectious diseases, autoimmunity and tumor immunology. Results We introduce TCRconv, a deep learning model for predicting recognition between T cell receptors and epitopes. TCRconv uses a deep protein language model and convolutions to extract contextualized motifs and provides state-of-the-art TCR-epitope prediction accuracy. Using TCR repertoires from COVID-19 patients, we demonstrate that TCRconv can provide insight into T cell dynamics and phenotypes during the disease. Availability TCRconv is available at http://github.com/emmijokinen/tcrconv Supplementary information Supplementary data are available at Bioinformatics online.