Source 1primary paper2025MED
Toolkit/bioinformatic refinement
bioinformatic refinement
Taxonomy: Technique Branch / Method. Workflows sit above the mechanism and technique branches rather than replacing them.
Summary
We also incorporate strategies for iterative biopanning and bioinformatic refinement to improve sensitivity and accuracy.
Usefulness & Problems
No literature-backed usefulness or problem-fit explainer has been materialized for this record yet.
Published Workflows
Comprehensive self-antigen screening to assess cross-reactivity in promiscuous T-cell receptors.
2025Objective: Assess cross-reactivity and off-target toxicity risk of an engineered candidate therapeutic TCR by functionally screening comprehensive self-antigen peptide libraries in a live-cell context.
Why it works: The workflow is presented as combining comprehensive peptide library coverage with functional live-cell TCR profiling, then adding iterative enrichment and computational refinement to improve sensitivity and accuracy.
TCR triggering by genome-encoded peptidesfunctional detection of cross-reactive epitopescomprehensive self-antigen cell library constructionTope-seq screeningiterative biopanningbioinformatic refinement
Stages
- 1.comprehensive self-antigen cell library construction(library_build)
This stage creates the comprehensive peptide-coding library substrate needed for broad functional screening of TCR cross-reactivity.
Selection: construct comprehensive self-antigen libraries covering genome-encoded peptides for downstream functional screening
- 2.first-pass bulk Tope-seq screening(broad_screen)
This stage performs the initial high-throughput functional scan across large peptide libraries to identify candidate cross-reactive epitopes.
Selection: screen epitope libraries against a model therapeutic candidate TCR to detect cross-reactive epitopes
- 3.iterative biopanning enrichment(selection)
This stage is added to improve sensitivity and accuracy beyond first-pass bulk screening.
Selection: iterative biopanning to improve sensitivity and accuracy after first-pass screening
- 4.bioinformatic refinement(secondary_characterization)
This stage computationally refines screening results to improve performance after experimental screening and enrichment.
Selection: refine screening outputs computationally to improve sensitivity and accuracy
Steps
- 1.construct comprehensive self-antigen cell libraries
Generate broad self-antigen peptide-coding libraries for downstream functional TCR cross-reactivity screening.
Library construction must occur before functional screening because the screen requires comprehensive peptide-coding inputs.
- 2.screen epitope libraries against the model therapeutic TCR using Tope-seqassay method
Identify peptides that functionally trigger the candidate therapeutic TCR and detect known cross-reactive epitopes.
This is the first-pass high-throughput functional readout applied after library generation to broadly identify candidate cross-reactive peptides.
- 3.apply iterative biopanning to improve screening sensitivity and accuracyenrichment method
Enrich or recover relevant candidates beyond first-pass screening.
It follows first-pass screening because it is explicitly incorporated to improve sensitivity and accuracy after the initial broad screen.
- 4.refine screening outputs bioinformaticallycomputational refinement method
Improve sensitivity and accuracy of candidate cross-reactive epitope calls.
This analysis step follows experimental screening and enrichment because it refines the resulting candidate set.
Taxonomy & Function
Primary hierarchy
Technique Branch
Method: A concrete computational method used to design, rank, or analyze an engineered system.
Mechanisms
selection enrichmentTarget processes
recombinationselectionValidation
Cell-freeBacteriaMammalianMouseHumanTherapeuticIndep. Replication
Supporting Sources
Ranked Claims
Iterative biopanning and bioinformatic refinement were incorporated to improve sensitivity and accuracy of the screening strategy.
We also incorporate strategies for iterative biopanning and bioinformatic refinement to improve sensitivity and accuracy
Tope-seq detected known cross-reactive epitopes from libraries of more than 5 × 10^5 unique peptide-coding sequences in first-pass bulk screening at a significance threshold of p < 0.01.
show that this strategy can be used to detect known cross-reactive epitopes from libraries of >5 × 105 unique peptide-coding sequences at a significance threshold of p < 0.01 in first-pass bulk screening
library size 500000 unique peptide-coding sequencessignificance threshold 0.01
The study demonstrates proof-of-principle functional TCR screening on a library of more than 2 × 10^7 peptide-coding DNA fragments.
demonstrate here the first proof-of-principle for functional TCR screening on a library of >2 × 107 peptide-coding DNA fragments
library size 20000000 peptide-coding DNA fragments
Approval Evidence
1 source1 linked approval claimfirst-pass slug bioinformatic-refinement
We also incorporate strategies for iterative biopanning and bioinformatic refinement to improve sensitivity and accuracy.
Source:
optimizationsupports
Iterative biopanning and bioinformatic refinement were incorporated to improve sensitivity and accuracy of the screening strategy.
We also incorporate strategies for iterative biopanning and bioinformatic refinement to improve sensitivity and accuracy
Source:
Comparisons
No literature-backed comparison notes have been materialized for this record yet.
Ranked Citations
- 1.