Evidence map›Paper›PMID 34078670›Full record

ArticleProceedings of the National Academy of Sciences of the United States of America2021

High-throughput developability assays enable library-scale identification of producible protein scaffold variants.

Alexander W Golinski, Katelynn M Mischler, Sidharth Laxminarayan, Nicole L Neurock, Matthew Fossing, Hannah Pichman, Stefano Martiniani, Benjamin J Hackel

Abstract readEvaluation Study
In one paragraph

Article in Proceedings of the National Academy of Sciences of the United States of America, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers.

0numbers the graph read from it
0cells of the map it votes in
18citing 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

18 citing papers in PubMed.

  1. Engineered B7-H3 Binding in Modular Gp2 Miniproteins.Biotechnology and bioengineering · 2026
    Article
  2. Employing deep mutational scanning in theProceedings of the National Academy of Sciences of the United States of America · 2025
    Article
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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

8 authors.

Alexander W GolinskiDepartment of Chemical Engineering and Materials Science, University of Minnesota, Minneapolis, MN 55455.ORCID 0000-0003-0771-0710
Katelynn M MischlerDepartment of Chemical Engineering and Materials Science, University of Minnesota, Minneapolis, MN 55455.
Sidharth LaxminarayanDepartment of Chemical Engineering and Materials Science, University of Minnesota, Minneapolis, MN 55455.ORCID 0000-0002-5269-5612
Nicole L NeurockDepartment of Chemical Engineering and Materials Science, University of Minnesota, Minneapolis, MN 55455.
Matthew FossingDepartment of Chemical Engineering and Materials Science, University of Minnesota, Minneapolis, MN 55455.
Hannah PichmanDepartment of Chemical Engineering and Materials Science, University of Minnesota, Minneapolis, MN 55455.
Stefano MartinianiDepartment of Chemical Engineering and Materials Science, University of Minnesota, Minneapolis, MN 55455.ORCID 0000-0003-2028-2175
Benjamin J HackelDepartment of Chemical Engineering and Materials Science, University of Minnesota, Minneapolis, MN 55455 hackel@umn.edu.ORCID 0000-0003-3561-9463

Funding

Engineering Gp2 as a small ligand scaffoldR01EB023339 · NIBIB · UNIVERSITY OF MINNESOTA · PI HACKEL, BENJAMIN · 2017 to 2020
$1.3M
NIBIB NIH HHS R01 EB023339
6 · The paper itself

Abstract

Proteins require high developability-quantified by expression, solubility, and stability-for robust utility as therapeutics, diagnostics, and in other biotechnological applications. Measuring traditional developability metrics is low throughput in nature, often slowing the developmental pipeline. We evaluated the ability of 10 variations of three high-throughput developability assays to predict the bacterial recombinant expression of paratope variants of the protein scaffold Gp2. Enabled by a phenotype/genotype linkage, assay performance for 10

Indexed as

Databases, Nucleic AcidGene LibraryHigh-Throughput Screening AssaysMachine LearningProteinsProteinsdevelopabilitypredictive modelingprotein engineering

Identifiers

PMID34078670
PMCPMC8201827

What OpenQuestion holds

Textmetadata
Read underepoch 390

Registered trials

None linked

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.