MétaCan
Menu
Back to cohort

Molecular coevolution of coagulation factor VIII and von Willebrand factor

2021· article· en· W3129105568 on OpenAlexafffund
Philip M. Zakas, Christopher W. Coyle, Anja Brehm, Marion Bayer, Barbara Solecka-Witulska, Caelan E. Radford, Christine Brown, Kate Nesbitt, Courtney Dwyer, Christoph Kannicht, H. Trent Spencer, Eric A. Gaucher, Christopher B. Doering, David Lillicrap

Bibliographic record

VenueBlood Advances · 2021
Typearticle
Languageen
FieldMedicine
TopicPlatelet Disorders and Treatments
Canadian institutionsQueen's University
FundersNational Institute of Arthritis and Musculoskeletal and Skin DiseasesNational Heart, Lung, and Blood InstituteCanadian Institutes of Health Research
KeywordsCoevolutionVon Willebrand factorBiologyIn silicoCoagulationComputational biologyRecombinant DNASequence (biology)GeneMolecular evolutionGeneticsEvolutionary biologyPhylogeneticsImmunologyMedicine

Abstract

fetched live from OpenAlex

Ancestral sequence reconstruction provides a unique platform for investigating the molecular evolution of single gene products and recently has shown success in engineering advanced biological therapeutics. To date, the coevolution of proteins within complexes and protein-protein interactions is mostly investigated in silico via proteomics and/or within single-celled systems. Herein, ancestral sequence reconstruction is used to investigate the molecular evolution of 2 proteins linked not only by stabilizing association in circulation but also by their independent roles within the primary and secondary hemostatic systems of mammals. Using sequence analysis and biochemical characterization of recombinant ancestral von Willebrand factor (VWF) and coagulation factor VIII (FVIII), we investigated the evolution of the essential macromolecular FVIII/VWF complex. Our data support the hypothesis that these coagulation proteins coevolved throughout mammalian diversification, maintaining strong binding affinities while modulating independent and distinct hemostatic activities in diverse lineages.

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)0.000
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.066
Threshold uncertainty score0.279

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 science0.0000.000
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 head0.009
GPT teacher head0.259
Teacher spread0.250 · 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

Citations7
Published2021
Admission routes2
Has abstractyes

Explore more

Same venueBlood AdvancesSame topicPlatelet Disorders and TreatmentsFrench-language works237,207