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Record W2980848095 · doi:10.1042/bcj20190517

Lessons from LIMK1 enzymology and their impact on inhibitor design

2019· article· en· W2980848095 on OpenAlexfundno aff
E. Salah, Deep Chatterjee, Alessandra Beltrami, Anthony Tumber, Franziska Preuß, Peter Canning, A. Chaikuad, Petra Knaus, Stefan Knapp, Alex N. Bullock, Sebastian Mathea

Bibliographic record

VenueBiochemical Journal · 2019
Typearticle
Languageen
FieldMedicine
TopicNeurogenetic and Muscular Disorders Research
Canadian institutionsnot available
FundersDeutsche ForschungsgemeinschaftOntario Ministry of Economic Development and InnovationNovartis PharmaWellcome TrustEuropean Federation of Pharmaceutical Industries and AssociationsMerck KGaAMinistero dello Sviluppo EconomicoFundação de Amparo à Pesquisa do Estado de São PauloDiamond Light SourceGenome CanadaPfizer
KeywordsStaurosporineRegulatorChemistryCell biologyKinasePhosphorylationBiochemistryBiophysicsBiologyComputational biologyProtein kinase A

Abstract

fetched live from OpenAlex

LIM domain kinase 1 (LIMK1) is a key regulator of actin dynamics. It is thereby a potential therapeutic target for the prevention of fragile X syndrome and amyotrophic lateral sclerosis. Herein, we use X-ray crystallography and activity assays to describe how LIMK1 accomplishes substrate specificity, to suggest a unique 'rock-and-poke' mechanism of catalysis and to explore the regulation of the kinase by activation loop phosphorylation. Based on these findings, a differential scanning fluorimetry assay and a RapidFire mass spectrometry activity assay were established, leading to the discovery and confirmation of a set of small-molecule LIMK1 inhibitors. Interestingly, several of the inhibitors were inactive towards the closely related isoform LIMK2. Finally, crystal structures of the LIMK1 kinase domain in complex with inhibitors (PF-477736 and staurosporine, respectively) are presented, providing insights into LIMK1 plasticity upon inhibitor binding.

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 score0.025
Threshold uncertainty score0.451

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 head0.034
GPT teacher head0.333
Teacher spread0.299 · 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

Citations29
Published2019
Admission routes1
Has abstractyes

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