Evidence map›Paper›PMID 41607291›Full record

ArticleBioTechniques

Benchmarking antibody discovery fidelity and reproducibility with an assay-locked residue fidelity index.

Michael P Weiner

Abstract read
In one paragraph

Article in BioTechniques. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

1 author.

Michael P WeinerPrecision BioTools, Branford, CT, USA.

Funding

A platform for T-cell receptor mimic antibody generation modeled using pHLA-A*11:01 KRAS G12DR44GM149009 · NIGMS · PRECISION BIOTOOLS INC · PI Sara T Passos · 2025 to 2026
$2.4M
Method for the validation by Western analysis of affinity reagents against post-translationally modified proteins.R44GM146473 · NIGMS · ABBRATECH, INC. · PI WEINER, MICHAEL P · 2023 to 2024
$2.2M
Platform for the High Throughput Generation and Validation of Affinity ReagentsR44GM148998 · NIGMS · ABBRATECH, INC. · PI WEINER, MICHAEL P · 2023 to 2024
$2.2M
DIRECTED DISCOVERY AND VALIDATION OF ANTI-SPLICE-JUNCTION SITE AbsR44GM156476 · NIGMS · ABBRATECH, INC. · PI MICHAEL P WEINER · 2024 to 2026
$1.1M
Method for the validation by Western analysis of affinity reagents against post-translationally modified proteins. I. survey of existing antibodies, and II. development of method improvementsR43GM146473 · NIGMS · ABBRATECH, INC. · PI WEINER, MICHAEL P · 2022 to 2022
$339k
T-cell receptor mimic affinity reagent generation using an in vivo novel immunogen strategyR43GM149009 · NIGMS · ABBRATECH, INC. · PI WEINER, MICHAEL P · 2023 to 2023
$295k
NIGMS NIH HHS R43 GM146473NIGMS NIH HHS R43 GM149009NIGMS NIH HHS R44 GM146473NIGMS NIH HHS R44 GM148998NIGMS NIH HHS R44 GM149009NIGMS NIH HHS R44 GM156476
6 · The paper itself

Abstract

Reproducibility in antibody discovery is undermined by dropout, paratope degeneracy, and immunogen imprinting. Although guidelines exist, the field lacks a shared, assay-locked metric to benchmark these liabilities across discovery pipelines. We propose the Residue Fidelity Index (RFI), a discovery-stage, within-assay comparative framework that consolidates fidelity liabilities into a single score. Assay-locked means RFI is comparable only within a fully specified assay configuration, uses fixed normalization and weights, and requires co-reporting of component metrics to expose drivers of the composite. RFI is not intended as a universal or optimal standard, but as one implementable example meeting basic benchmarking requirements: defined components, fixed assay context, pre-specified weights, and primary-data disclosure. RFI is reported alongside its components (D, G, I) to visualize reproducibility in parallel with affinity, humanization, and yield. To demonstrate feasibility, we applied RFI in a simulated Epivolve testbed derived from multiple studies, embedding MILKSHAKE (context retention) and Sundae (residue discrimination) validation modules into discovery. Across three campaigns (n = 25 clones), antibodies meeting provisional thresholds (dropout ≤10%, degeneracy ≤0.10, no imprinting) yielded RFI values from 0.610 to 0.982 (mean ± SD 0.922 ± 0.103; I = 1 for clones C04 and C20). These results show how fidelity can be consolidated to prioritize candidates before application-specific cellular validation. Despite limited scope and proprietary immunogens, this work provides a proof of concept for residue-level benchmarking, with per-clone primary values and RFI reported in Supplementary Table S1 for auditability.

Indexed as

AntibodiesBenchmarkingAnimalsAntibodies, MonoclonalHumansReproducibility of ResultsAntibodiesAntibodies, MonoclonalAntibody reproducibilitybenchmarking metricsdegeneracydropoutepivolveMILKSHAKEresidue fidelity indexsundae

Identifiers

PMID41607291
PMCPMC12994182

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LicenceCC BY-NC
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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.