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Record W4312466774 · doi:10.1016/j.mcpro.2022.100308

Integrative Structural Biology to Understand Biological Complexity

2022· article· en· W4312466774 on OpenAlexaff
Wah Chiu, Geoffrey G. Hesketh, Claire Martin, J. W. Knight, Christopher D. Go, Anne‐Claude Gingras

Bibliographic record

VenueMolecular & Cellular Proteomics · 2022
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicGenetics, Bioinformatics, and Biomedical Research
Canadian institutionsUniversity of TorontoLunenfeld-Tanenbaum Research Institute
Fundersnot available
KeywordsComputational biologyBiologyEvolutionary biologyData scienceComputer science

Abstract

fetched live from OpenAlex

Cryogenic electron microscopy (cryoEM) has been advanced to resolve atomic structures of biochemically purified macromolecules with details equivalent to X-ray crystal structures. A unique aspect of cryoEM is to use image processing methods to sort out images of particles with heterogeneous compositions and conformations. These capabilities have been demonstrated across a spectrum of macromolecules including viruses, membrane channels, protein folding machines with and without substrates, and RNA. The reliability of these structures can be assured by using rigorous criteria that the atomic model obeys expected stereochemistry of the molecules and simultaneously matches well with the experimentally observed density maps. The resolvability of the cryoEM structures have been shown in many cases to be sufficiently good to resolve carbohydrates, lipids, ligands, ions and water molecules that are critical to understand the chemical basis of their tertiary structures and their functions involving conformational variations. We will present examples of integrating cryoEM, mass spectrometry and biological assays to guide interpretation of cryoEM structures in terms of their contents and functions.

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 score0.072
Threshold uncertainty score1.000

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 head0.039
GPT teacher head0.295
Teacher spread0.256 · 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

Citations0
Published2022
Admission routes1
Has abstractyes

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