Biology subjects

Scheper, W.

Publications and source records attributed to Scheper, W..

4 recordsLinked to original sources

bioRxiv2025

A functionally validated TCR-pMHC database for TCR specificity model development

Accurate prediction of TCR specificity forms a holy grail in immunology and large language models and computational structure predictions provide a path to achieve this. Importantly, current TCR-pMHC prediction models have been trained and evaluated using historical data of unknown quality. Here, we develop and utilize a high-throughput synthetic platform for TCR assembly and evaluation to assess a large fraction of VDJdb-deposited TCR-pMHC entries using a standardized readout of TCR function. Strikingly, this analysis demonstrates that claimed TCR reactivity is only confirmed for 50% of evaluated entries. Intriguingly, the use of TCRbridge to analyze AlphaFold3 confidence metrics reveals a substantial performance in distinguishing functionally validating and non-validating TCRs even though AlphaFold3 was not trained on this task, demonstrating the utility of the validated VDJdb (TCRvdb) database that we generated. We provide TCRvdb as a resource to the community to support training and evaluation of improved predictive TCR specificity models.

immunology↗

bioRxiv2025

Casein kinase 1δ-regulated formation of GVBs induces resilience to tau pathology-mediated protein synthesis impairment

In Alzheimers disease, many surviving neurons with tau pathology contain granulovacuolar degeneration bodies (GVBs), neuron-specific lysosomal structures induced by pathological tau assemblies. This could indicate a neuroprotective role for GVBs, however, the mechanism of GVB formation and its functional implications are elusive. Here, we demonstrate that CK1{delta} activity is required for GVB formation. CK1{delta} is sequestered in the GVB during this process in an autophagy-dependent manner. We show that neurons with GVBs (GVB+) are resilient to tau-induced impairment of global protein synthesis and are protected against tau-mediated neurodegeneration. GVB+ neurons do not exhibit differential activation of proteostatic stress responses, but have increased ribosomal content. Importantly, unlike neurons without GVBs, GVB+ neurons fully retain the capacity to induce long-term potentiation-induced protein synthesis in the presence of tau pathology. Our results have identified CK1{delta} as a key regulator of GVB formation that confers a protective neuron-specific proteostatic stress response to tau pathology. These findings provide novel opportunities for targeting neuronal resilience in tauopathies. HighlightsO_LIGVB+ neurons are protected against tau-induced neurodegeneration C_LIO_LICK1{delta} activity is essential for the formation of GVBs C_LIO_LIGVB+ neurons are resilient to tau-induced impairment of protein synthesis C_LIO_LIGVB+ neurons retain the capacity for LTP-regulated protein synthesis C_LI Graphical abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=154 SRC="FIGDIR/small/645932v2_ufig1.gif" ALT="Figure 1"> View larger version (32K): org.highwire.dtl.DTLVardef@cda628org.highwire.dtl.DTLVardef@157bcb8org.highwire.dtl.DTLVardef@126dbe6org.highwire.dtl.DTLVardef@2efe8f_HPS_FORMAT_FIGEXP M_FIG C_FIG

neuroscience↗

bioRxiv2023

Neuronal lipofuscinosis caused by Kufs disease/CLN4 DNAJC5 mutations but not by a CSPα/DNAJC5 deficiency

Kufs disease/CLN4 is an autosomal dominant neurodegenerative disorder that affects young adults, caused by mutations in the DNAJC5 gene that encodes the synaptic vesicle co-chaperone Cysteine String Protein (CSP/DNAJC5). The Leu115Arg and Leu116{Delta} mutations in humans are known to independently cause the disease, although the underlying mechanisms are unknown. To investigate the disease mechanisms in vivo, we generated three independent mouse lines overexpressing different versions of CSP/DNAJC5 under the neuron-specific Thy1 promoter: wild-type (WT), Leu115Arg, and Leu116{Delta}. Mice expressing mutant CSP/DNAJC5 are viable and do not show any significant increase in morbidity or mortality. However, we observed the presence of pathological lipofuscinosis in the mutants, indicated by autofluorescent punctate structures labeled with antibodies against ATP synthase subunit C, which were absent in the WT transgenic line. Additionally, transmission electron microscopy revealed intracellular structures resembling granular osmiophilic deposits (GRODs), observed in Kufs disease patients, in the mutants but not in non-transgenic controls or the WT transgenic mice. Notably, conventional, or conditional knockout mice lacking CSP/DNAJC5 did not exhibit any signs of increased lipofuscinosis or GRODs. Our novel mouse models thus provide a valuable tool to investigate the molecular mechanisms underlying Kufs disease/CLN4. We conclude that DNAJC5 mutations cause neuronal lipofuscinosis through a cell-autonomous gain of a novel but pathological function of CSP/DNAJC5.

neuroscience↗

bioRxiv2023

STAPLER: Efficient learning of TCR-peptide specificity prediction from full-length TCR-peptide data

The prediction of peptide-MHC (pMHC) recognition by {beta} T-cell receptors (TCRs) remains a major biomedical challenge. Here, we develop STAPLER (Shared TCR And Peptide Language bidirectional Encoder Representations from transformers), a transformer language model that uses a joint TCR{beta}- peptide input to allow the learning of patterns within and between TCR{beta} and peptide sequences that encode recognition. First, we demonstrate how data leakage during negative data generation can confound performance estimates of neural network-based models in predicting TCR - pMHC specificity. We then demonstrate that, because of its pre-training and fine-tuning masked language modeling tasks, STAPLER outperforms both neural network-based and distance-based ML models in predicting the recognition of known antigens in an independent dataset, in particular for antigens for which little related data is available. Based on this ability to efficiently learn from limited labeled TCR- peptide data, STAPLER is well-suited to utilize growing TCR - pMHC datasets to achieve accurate prediction of TCR - pMHC specificity.

bioinformatics↗