Next, we’ll expand TCRvdb to include thousands of TCRs annotated to the 8 most densely annotated epitopes in the database using our "sequence-synthesis-screen" workflow.
We’ll also use TCRvdb to develop improved TCR-pMHC reactivity models.
📍 TCRvdb is hosted here:
🔗 github.com/schumacherla...
13/ In contrast, TCRbridge could not reliably distinguish whether non-validating (mislabeled) TCRs were reactive to YLQ or GLC.
This demonstrates how TCRvdb helps reveal that models like AlphaFold3 may perform better than previously assumed—if tested on validated data.
12/ We also show that TCRbridge can distinguish whether validating TCRs are reactive to YLQ or GLC.
11/ We introduce TCRbridge, a model that combines AlphaFold3’s per-residue confidence metrics at TCR–peptide interfaces.
TCRbridge revealed strong ability to distinguish validating from non-validating GLC-annotated TCRs, despite AlphaFold3 not being trained for this task.
10/ Can more complex models like AlphaFold3 distinguish validating from non-validating GLC-annotated TCRs? Interestingly, AlphaFold3 assigned lower confidence to non-validating TCR structures.
9/ tcrdist3 groups TCRs by sequence similarity. tcrdist3 distinguishes truly YLQ-reactive TCRs from non-validating ones, but its performance for GLC-reactive TCRs is modest, likely due to validating and non-validating GLC- annotated TCRs being similar in sequence space.
8/ We introduce TCRvdb, a functionally validated TCR-pMHC database to support TCR specificity model development, along with a standardized platform for rapid expansion. Using TCRvdb, we evaluated the performance of tcrdist3 and AlphaFold-based models.
7/ Why do some TCRs validate and others don’t? One key predictor: studies with more donors who had recent or ongoing COVID-19 showed a higher fraction of truly YLQ-reactive TCRs.
6/ Strikingly, this functional analysis demonstrates that claimed TCR reactivity is only confirmed for 50% of evaluated entries.
5/ To generate a high-confidence dataset for TCR specificity modeling, we validated TCRs annotated as reactive to YLQ (SARS-CoV-2) and GLC (EBV) epitopes using pooled functional genetic screening.
4/ Nanopore QC analysis of 5 TCR libraries showed 96.9% successful assembly, with an average of 74.2% sequence-perfect unique TCRs. Libraries were highly uniform—only 5.6-fold difference between the 5th and 95th percentiles.
3/ To create a Nanopore TCR QC pipeline with single base error detection capacity, we designed a UMI-based sequence error correction method to create highly accurate TCR UMI consensus sequences.
2/ Comprehensive QC of TCR libraries requires full-length TCR sequencing to resolve correct Vα–CDR3α–Jα and Vβ–CDR3–Jβ pairings. While Nanopore can sequence full-length TCRs, its raw base-call accuracy isn’t sufficient to detect mispairings, mutations, or indels.
1/ To evaluate a large fraction of TCR-pMHC entries in the database without bias from T cell phenotype or TCR abundance, we developed and utilized a high-throughput synthetic platform for TCR assembly.
It's not the models, it's the data!
We show that a substantial proportion of a widely used TCR-pMHC database does not functionally validate, causing underestimation of performance of TCR-pMHC prediction models.
www.biorxiv.org/content/10.1...
🌟Keystone Cancer Immunotherapy conference 2025 🌟 Speaker Highlight #2 at #KSCancerImmune25: Dr. Ton N. Schumacher @nkinl.bsky.social will discuss how T cells detect tumors in “Dissecting T Cell Recognition of Cancer.” Don’t miss his cutting-edge immunology insights!
🏆 Congrats to Ton Schumacher of the NKI and Oncode Institute for receiving the ESMO Award for Immuno-Oncology 2024! His work on T-cells and immunotherapy is revolutionizing cancer treatment ➡️ t.co/WHlqDB7fjF
