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Record W2413636846 · doi:10.1101/056325

Sequence Entropy and the Absolute Rate of Amino Acid Substitutions

2016· preprint· en· W2413636846 on OpenAlexaff
Richard A. Goldstein, David D. Pollock

Bibliographic record

VenuebioRxiv (Cold Spring Harbor Laboratory) · 2016
Typepreprint
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicProtein Structure and Dynamics
Canadian institutionsInstitute of Infection and Immunity
Fundersnot available
KeywordsAmino acidNeutral theory of molecular evolutionProtein evolutionEpistasisMolecular evolutionPopulationChemistryEntropy (arrow of time)Amino acid substitutionThermodynamicsStatistical physicsPhysicsMutationBiochemistryPhylogenetics

Abstract

fetched live from OpenAlex

The evolution of model proteins under selection for thermodynamic stability suggests parallels between evolutionary behavior and chemical reaction kinetics. We developed a statistical mechanics theory of protein evolution by dividing amino acid interactions into site-specific and ‘bath’ components, and show that substitutions between two amino acids occur when their site-specific contributions to stability are nearly identical. Fluctuating epistatic interactions drive stabilities into and out of these regions of near neutrality, with the time spent in the neutral region and thus the rate of substitution governed by physicochemical similarities between the amino acids. We derive a theoretical framework for how site-specific stabilities are determined, and demonstrate that substitution rates and the magnitude of the evolutionary Stokes shift can be predicted from biophysics and the effect of sequence entropy alone. Population genetics underlays our analysis, but population size does not determine the absolute rate of amino acid substitutions.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.008
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.010

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.008
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.001
Scholarly communication0.0010.002
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.215
Teacher spread0.206 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
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

Citations6
Published2016
Admission routes1
Has abstractyes

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Same venuebioRxiv (Cold Spring Harbor Laboratory)Same topicProtein Structure and DynamicsFrench-language works237,207