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Record W2766991812 · doi:10.1101/201251

Evolutionary variance analysis of the Glycoside Hydrolase Family 13: Structural evidence in classification and evolution

2017· preprint· en· W2766991812 on OpenAlexafffund
José Sergio Hleap, Christian Blouin

Bibliographic record

VenuebioRxiv (Cold Spring Harbor Laboratory) · 2017
Typepreprint
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicEnzyme Production and Characterization
Canadian institutionsMcGill UniversityMcGill University and Génome Québec Innovation CentreMcGill Genome CentreDalhousie University
FundersNatural Sciences and Engineering Research Council of CanadaDepartamento Administrativo de Ciencia, Tecnología e Innovación (COLCIENCIAS)
KeywordsGlycoside hydrolasePhylogeneticsPhylogenetic treeBiologyEvolutionary biologyIdentification (biology)Computational biologyHydrolaseGeneticsEnzymeBiochemistryEcologyGene

Abstract

fetched live from OpenAlex

Abstract Glycoside Hydrolase Family 13 (GH13) structures are responsible for the hydrolysis of starch into smaller carbohydrates. They important in industrial applications and evolutionary studies. This family has been thoroughly documented in the the Carbohydrate-Active enZYmes Database (CAZY), and divided into subfamilies based mainly in sequence information. Here we give structural evidence into GH13 classification and evolution using structural information. Here we proposed a novel method that is sensitive enough to identify miss-classifications, or to provide evidence for further partition that can be of interests to bio-engineers and evolutionary biologists. We also introduced a method to explore the relative importance of residues with respect to the overall deformation that it causes to the overall structure in an evolutionary time scale. We found that the GH13 family can be classified into three main structural groups. There is a hierarchical structure within these clusters that can be use to inform other classification schemes. We also found that by using structural information, subtle structural shifts can be identified and that can be missed in sequence/phylogeny-only based classifications. When each structural group is explored, we found that identifying the most structurally variable sites can lead to identification of functionally (both catalytically and structurally) important residues.

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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.715
Threshold uncertainty score0.880

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 head0.020
GPT teacher head0.242
Teacher spread0.222 · 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 designObservational
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
Published2017
Admission routes2
Has abstractyes

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