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Record W2916070683 · doi:10.1002/pro.3595

Evaluation of a noncanonical Cys40‐Cys55 disulfide linkage for stabilization of single‐domain antibodies

2019· article· en· W2916070683 on OpenAlexafffund
Dae Young Kim, Hiba Kandalaft, Greg Hussack, Shalini Raphael, Wen Ding, John F. Kelly, Kevin A. Henry, Jamshid Tanha

Bibliographic record

VenueProtein Science · 2019
Typearticle
Languageen
FieldMedicine
TopicMonoclonal and Polyclonal Antibodies Research
Canadian institutionsUniversity of OttawaNational Research Council Canada
FundersNational Research Council Canada
KeywordsThermostabilityDisulfide LinkageChemistrySingle-domain antibodyDisulfide bondCircular dichroismCrystallographySolubilityDenaturation (fissile materials)StereochemistryAntibodyBiochemistryCysteinePhysical chemistryBiologyNuclear chemistryEnzymeGenetics

Abstract

fetched live from OpenAlex

Abstract Incorporation of noncanonical disulfide linkages into single‐domain antibodies (sdAbs) has been shown to enhance thermostability and other properties. Here, we evaluated the effects of introducing a novel disulfide linkage formed between Cys residues at IMGT positions 40 and 55 on the melting temperatures ( T m s), reversibility of thermal unfolding, solubility, and antigen‐binding affinities of three types of sdAbs (V H H, V H , and V L domains). The Cys40‐Cys55 disulfide linkage was tolerated by 9/9 V H Hs, 12/12 V H s, and 2/11 V L s tested and its formation was confirmed by mass spectrometry. Using circular dichroism, we found that the Cys40‐Cys55 disulfide linkage increased sdAb T m by an average of 10.0°C (range: 0–21.8°C). However, enhanced thermostability came at the cost of a partial loss of refolding ability upon thermal denaturation as well as, for some sdAbs, significantly decreased solubility and antigen‐binding affinity. Thus, Cys40/Cys55 can be added to the panel of known locations for introducing stabilizing noncanonical disulfide linkages into antibody variable domains, although its effects should be tested empirically for individual sdAbs.

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.

How this classification was reachedexpand

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)0.004
metaresearch head score (Gemma)0.002
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: Empirical · Consensus signal: Empirical
Teacher disagreement score0.033
Threshold uncertainty score0.268

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.071
GPT teacher head0.383
Teacher spread0.312 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations6
Published2019
Admission routes2
Has abstractyes

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