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Record W2951362051 · doi:10.1101/665745

A Bacterial Surface Layer Protein Exploits Multi-step Crystallization for Rapid Self-assembly

2019· preprint· en· W2951362051 on OpenAlexaff
Jonathan Herrmann, Po-Nan Li, Fatemeh Jabbarpour, Anson Chan, Ivan Rajković, Tsutomu Matsui, Lucy Shapiro, John Smit, Thomas Weiß, M.E.P. Murphy, Soichi Wakatsuki

Bibliographic record

VenuebioRxiv (Cold Spring Harbor Laboratory) · 2019
Typepreprint
Languageen
FieldMaterials Science
TopicEnzyme Structure and Function
Canadian institutionsUniversity of British Columbia
FundersNational Institute of General Medical SciencesU.S. Department of Energy
KeywordsCrystallizationNucleationProtein crystallizationCrystallographyS-layerMaterials scienceBiophysicsNanomaterialsMacromoleculeSelf-assemblyNanotechnologyChemistryBiochemistryBiology

Abstract

fetched live from OpenAlex

Abstract Surface layers (S-layers) are crystalline protein coats surrounding microbial cells. S-layer proteins (SLPs) regulate their extracellular self-assembly by crystallizing when exposed to an environmental trigger. However, molecular mechanisms governing rapid protein crystallization in vivo or in vitro are largely unknown. Here, we demonstrate that the C. crescentus SLP readily crystallizes into sheets in vitro via a calcium-triggered multi-step assembly pathway. This pathway involves two domains serving distinct functions in assembly. The C-terminal crystallization domain forms the physiological 2D crystal lattice, but full-length protein crystallizes multiple orders of magnitude faster due to the N-terminal nucleation domain. Observing crystallization using time-resolved electron cryo-microscopy (Cryo-EM) reveals a crystalline intermediate wherein N-terminal nucleation domains exhibit motional dynamics with respect to rigid lattice-forming crystallization domains. Dynamic flexibility between the two domains rationalizes efficient S-layer crystal nucleation on the curved cellular surface. Rate enhancement of protein crystallization by a discrete nucleation domain may enable engineering of kinetically controllable self-assembling 2D macromolecular nanomaterials. Significance Statement Many microbes assemble a crystalline protein layer on their outer surface as an additional barrier and communication platform between the cell and its environment. Surface layer proteins efficiently crystallize to continuously coat the cell and this trait has been utilized to design functional macromolecular nanomaterials. Here, we report that rapid crystallization of a bacterial surface layer protein occurs through a multi-step pathway involving a crystalline intermediate. Upon calcium-binding, sequential changes occur in the structure and arrangement of the protein, which are captured by time-resolved small angle x-ray scattering and transmission electron cryo-microscopy. We demonstrate that a specific domain is responsible for enhancing the rate of self-assembly, unveiling possible evolutionary mechanisms to enhance the kinetics of 2D protein crystallization in vivo .

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)0.001
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 score0.022
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0010.000
Research integrity0.0010.000
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 head0.021
GPT teacher head0.229
Teacher spread0.208 · 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

Citations3
Published2019
Admission routes1
Has abstractyes

Explore more

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