Biology subjects

Krog, J.

Publications and source records attributed to Krog, J..

4 recordsLinked to original sources

bioRxiv2025

Deep mapping of the TCR-antigen interface using pMHC-pseudotyped viruses and yeast display

T cell receptor (TCR) specificity is central to the efficacy of T cell therapies, yet scalable methods to map how TCR sequences shape antigen recognition remain limited. To address this, we introduce VelociRAPTR, a library-on-library approach that combines yeast-displayed TCR libraries with pMHC-displaying virus-like particles (pMHC-VLPs) to rapidly screen millions of TCR-antigen interactions. We show that pMHC-VLPs efficiently bind TCRs on yeast and generate equivalent data to recombinantly produced pMHC protein. We then apply VelociRAPTR to screen 47 million variants of the A6 and 868 TCRs against 92 pMHCs simultaneously, mutating both the CDR3 loops and cognate peptides. The resulting CDR3-pMHC maps reveal biased recognition patterns, where mutations to CDR3 loops can selectively constrain or broaden specificity to peptide analogs. These insights provide a foundation for engineering TCRs with defined pMHC binding profiles and improving models that predict TCR-antigen interactions, including the prediction of off-target recognition. By coupling the scale of yeast display with the modularity of VLPs, VelociRAPTR offers a generalizable strategy for generating deep, high-throughput protein- protein interaction data.

immunology↗

bioRxiv2023

Machine learning model interpretations explain T cell receptor binding

T cells mediate immune responses against pathogens and cancer through T cell receptors (TCRs) that recognize foreign peptides presented on the cell surface by Major Histocompatibility Complex (MHC) proteins. TCRs carry enormous diversity and differ across individuals, and mechanisms that determine TCR-pMHC binding are poorly understood. The ability to predict TCR-pMHC interactions would accelerate development of cellular therapeutics to design TCRs that specifically bind to a target of interest. We designed a randomized library of 196 TCR CDR3{beta} sequences and experimentally evaluated their affinities for the Tax-A02 peptide-MHC target. We trained ML models that predict TCR binding to Tax-A02 from TCR sequence and used model interpretation to identify TCR sequence features associated with binding. We found these features accurately mirror the true sequence features in our experimental data.

immunology↗

bioRxiv2022

High-throughput suppressor screens unveil functional convergence of single-gene lysis proteins

In contrast to dsDNA phages where multiple proteins are involved in programmed host lysis, lysis in ssRNA Fiersviridae and ssDNA Microviridae phages requires only a single gene (sgl for single gene lysis) to meet the size constraints of some of the smallest genomes in the biosphere. To achieve lysis, Sgl proteins exploit evolutionary "weak spots" in bacterial cell wall biogenesis. In several cases, this is done by inhibiting specific steps in Lipid II synthesis. Recently metatranscriptomics has revealed thousands of novel ssRNA phage genomes, each of which must carry at least one sgl gene. Determining the targets of these Sgl proteins could reveal novel vulnerabilities in bacterial envelope biogenesis and may lead to new antibiotics. Here, we employ a high-throughput genetic screen to uncover genome-wide host suppressors of Sgl activity and apply it to a set of diverse Sgls with unknown molecular targets. In addition to validating known molecular mechanisms, we determined that the Sgl of PP7, an ssRNA phage of P. aeruginosa, targets MurJ, the flippase responsible for Lipid II export which was previously shown to be the target of the Sgl of coliphage M. These two Sgls, which are unrelated and predicted to have opposite membrane topology, thus represent a case of convergent evolution. Another set of Sgls which are thought to cause lysis without inhibiting cell wall synthesis elicit a common set of multicopy suppressors, suggesting these Sgls act by the same or similar mechanism.

microbiology↗

bioRxiv2019

Optimality of the spontaneous prophage induction rate

Lysogens are bacterial cells that have survived after being infected by bacterial viruses called bacteriophages. Instead of being killed by the virus, the infected cell survives by integrating the viral DNA into its own genome. This is only possible with "temperate" bacteriophages which do not always lyse their host to reproduce, but sometimes replicate passively using the lysogenic pathway. After an infection resulting in lysogeny, the lysogen continues to grow and divide normally, seemingly unaffected by the integrated viral genome which is now referred to as a prophage. However, the prophage can have an impact on the hosts phenotype and overall fitness in certain environments. This makes competition between the lysogen and its nonlysogen counterpart possible because both cells have different genomes and potentially different growth rates. Additionally, the prophages within the lysogens are capable of spontaneously reverting back to the lytic pathway via spontaneous prophage induction (SPI), causing death of the lysogen and the release of new progeny phages. These new phages can then lyse or lysogenize other susceptible nonlysogens, thereby impacting the competition between lysogens and nonlysogens. In a scenario with differing growth rates, it is not clear whether SPI would be beneficial or detrimental to the lysogens since it directly causes cell death but also attacks nonlysogenic competitiors, either lysing or lysogenizing them. In this work we study the evolutionary dynamics of a mixture of lysogens and nonlysogens and derive general conditions on the rate of SPI resulting in lysogens displacing nonlysogens. We show that there exists an optimal SPI rate, and apply the model to bacteriophage{lambda} . We find that the model can explain why the experimentally measured SPI rate for phage{lambda} is so low. We also investigate the impact of stochasticity and conclude that even at low copy numbers the SPI rate can still be fairly low while still providing an advantage to the lysogens.

evolutionary biology↗