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Record W2991041010 · doi:10.1002/pro.3794

Engineering the serpin α<sub>1</sub>‐antitrypsin: A diversity of goals and techniques

2019· review· en· W2991041010 on OpenAlex
Benjamin M. Scott, William P. Sheffield

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueProtein Science · 2019
Typereview
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicProtease and Inhibitor Mechanisms
Canadian institutionsMcMaster UniversityCanadian Blood Services
FundersHealth CanadaNational Institute of Standards and TechnologyCanadian Blood ServicesHeart and Stroke Foundation of CanadaAustralian GovernmentGovernment of Canada
KeywordsSerpinProtein engineeringProtein designMutagenesisDirected evolutionComputational biologyBiologyFunction (biology)Phage displayProtein structureMutantGeneticsBiochemistryEnzymeGene

Abstract

fetched live from OpenAlex

Abstract α 1 ‐Antitrypsin (α 1 ‐AT) serves as an archetypal example for the serine proteinase inhibitor (serpin) protein family and has been used as a scaffold for protein engineering for &gt;35 years. Techniques used to engineer α 1 ‐AT include targeted mutagenesis, protein fusions, phage display, glycoengineering, and consensus protein design. The goals of engineering have also been diverse, ranging from understanding serpin structure–function relationships, to the design of more potent or more specific proteinase inhibitors with potential therapeutic relevance. Here we summarize the history of these protein engineering efforts, describing the techniques applied to engineer α 1 ‐AT, specific mutants of interest, and providing an appended catalog of the &gt;200 α 1 ‐AT mutants published to date.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.168
Threshold uncertainty score0.525

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.022
GPT teacher head0.267
Teacher spread0.245 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it