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Record W2754391532 · doi:10.1021/acs.jpclett.7b02021

A Universal Pattern in the Percolation and Dissipation of Protein Structural Perturbations

2017· article· en· W2754391532 on OpenAlexaff
Nandakumar Rajasekaran, Ashok Sekhar, Athi N. Naganathan

Bibliographic record

VenueThe Journal of Physical Chemistry Letters · 2017
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicProtein Structure and Dynamics
Canadian institutionsUniversity of Toronto
FundersIndian Institute of Technology MadrasDepartment of Biotechnology, Ministry of Science and Technology, IndiaWellcome TrustWellcome
KeywordsAllosteric regulationMolecular dynamicsProtein structureBiological systemPercolation (cognitive psychology)Protein dynamicsComputational biologyChemistryBiophysicsPhysicsStatistical physicsBiologyComputational chemistryBiochemistryNeuroscienceReceptor

Abstract

fetched live from OpenAlex

Understanding the extent to which information is transmitted through the intramolecular interaction network of proteins upon a perturbation, that is, an allosteric effect, has long remained an unsolved problem. Through an analysis of high-resolution NMR data from the literature on 28 different proteins and 49 structural perturbations, we show that the extent of induced structural changes through mutations and molecular events including protein-protein, protein-peptide, protein-ligand binding, and post-translational modifications exhibit a near-universal exponential functional form. The extent of percolation into the protein structures can be up to 20-25 Å despite no apparent change in the 3D structures. These observations are also consistent with theoretical expectations, elementary graph theoretic analysis of protein structures, detailed molecular dynamics simulations, and experimental double-mutant cycles. Our analysis highlights that most molecular events would contribute to allosteric effects independent of protein structure, topology, or identity and provides a simple avenue to test and potentially model their effects.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.234
Threshold uncertainty score0.140

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 head0.005
GPT teacher head0.236
Teacher spread0.231 · 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 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

Citations56
Published2017
Admission routes1
Has abstractyes

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