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Record W3081466185 · doi:10.1042/bcj20200260

Sunitinib inhibits RNase L by destabilizing its active dimer conformation

2020· article· en· W3081466185 on OpenAlexaff
Jinle Tang, Yingjie Wang, Huan Zhou, Yuxin Ye, Manisha Talukdar, Ziyang Fu, Zhihong Liu, Jihui Li, Dante Neculai, Jiali Gao, Hao Huang

Bibliographic record

VenueBiochemical Journal · 2020
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicVirus-based gene therapy research
Canadian institutionsLunenfeld-Tanenbaum Research InstituteUniversity of TorontoMount Sinai Hospital
Fundersnot available
KeywordsSunitinibRibonucleaseRNase PRNase HDimerBinding siteChemistryRNase PHBiochemistryBiologyRNACancerGeneGenetics

Abstract

fetched live from OpenAlex

The pseudokinase (PK) RNase L is a functional ribonuclease and plays important roles in human innate immunity. The ribonuclease activity of RNase L can be regulated by the kinase inhibitor sunitinib. The combined use of oncolytic virus and sunitinib has been shown to exert synergistic effects in anticancer therapy. In this study, we aimed to uncover the mechanism of action through which sunitinib inhibits RNase L. We solved the crystal structures of RNase L in complex with sunitinib and its analogs toceranib and SU11652. Our results showed that sunitinib bound to the ATP-binding pocket of RNase L. Unexpectedly, the αA helix linking the ankyrin repeat-domain and the PK domain affected the binding mode of sunitinib and resulted in an unusual flipped orientation relative to other structures in PDB. Molecular dynamics simulations and dynamic light scattering results support that the binding of sunitinib in the PK domain destabilized the dimer conformation of RNase L and allosterically inhibited its ribonuclease activity. Our study suggested that dimer destabilization could be an effective strategy for the discovery of RNase L inhibitors and that targeting the ATP-binding pocket in the PK domain of RNase L was an efficient approach for modulating its ribonuclease activity.

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 score0.010
Threshold uncertainty score0.614

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 head0.031
GPT teacher head0.299
Teacher spread0.268 · 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

Citations13
Published2020
Admission routes1
Has abstractyes

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