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Record W4401215074 · doi:10.26434/chemrxiv-2024-w1g7v

pH-sulfate synergy regulates processing and mechanics of mussel byssus protein condensates

2024· preprint· en· W4401215074 on OpenAlexaff
Hamideh R. Alanagh, Magda G. Sánchez-Sánchez, Michael R. Wozny, Yeganeh Habibi, Candace Jarade, Tara Sprules, Mike Strauss, Anthony Mittermaier, Adam G. Hendricks, Matthew J. Harrington

Bibliographic record

VenueChemRxiv · 2024
Typepreprint
Languageen
FieldMaterials Science
TopicSilk-based biomaterials and applications
Canadian institutionsMcGill University
Fundersnot available
KeywordsByssusChemistryChemical engineeringViscoelasticityRheologyMusselBiophysicsNanotechnologyMytilusMaterials scienceComposite materialGeologyOceanography

Abstract

fetched live from OpenAlex

Fluid protein condensates are used as precursor phases for fabricating extracellular protein-based materials including elastin, spider silk, and mussel byssus. The byssus, utilized by mussels for anchoring in marine environments, consists of tough, self-healing adhesive fibers. Byssus formation involves the secretion of protein condensate droplets under acidic conditions that subsequently solidify under basic seawater conditions. We currently have a poor understanding of the physicochemical triggers and molecular-level interactions at play, in particular the role of pH and sulfate anions previously identified during native fabrication. Here, we investigated the pH and sulfate-dependent structural and mechanical response of condensates made from a recombinant byssus protein (mfp-1) using optical tweezers microrheology, FRAP, confocal Raman spectroscopy, NMR, and cryo-EM. We found that the protein concentration in condensates increased, and the viscoelastic response became more rigid under basic conditions in the presence of sulfate ions compared with chloride ions, consistent with spectroscopic analysis indicating different molecular interactions under these different chemical conditions. These studies highlight the crucial interplay between sulfate anions and pH in tuning condensate viscoelasticity via control of intermolecular interactions, providing insights into the natural byssus formation process with relevance for bio-inspired materials processing of sustainable plastics and materials for tissue engineering.

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 categoriesMeta-epidemiology (narrow)
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 score0.044
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.001
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 head0.018
GPT teacher head0.255
Teacher spread0.236 · 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.

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

Citations2
Published2024
Admission routes1
Has abstractyes

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