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Record W4296077873 · doi:10.1038/s41467-022-33229-5

A small molecule antagonist of SMN disrupts the interaction between SMN and RNAP II

2022· article· en· W4296077873 on OpenAlexafffund
Yanli Liu, Aman Iqbal, Weiguo Li, Zuyao Ni, Yalong Wang, Jurupula Ramprasad, Karan Joshua Abraham, Mengmeng Zhang, Dorothy Yanling Zhao, Qin Su, P. Loppnau, Honglv Jiang, Xinghua Guo, Peter J. Brown, Xuechu Zhen, Guoqiang Xu, Karim Mekhail, Xingyue Ji, Mark T. Bedford, Jack Greenblatt, Jinrong Min

Bibliographic record

VenueNature Communications · 2022
Typearticle
Languageen
FieldMedicine
TopicNeurogenetic and Muscular Disorders Research
Canadian institutionsStructural Genomics ConsortiumUniversity of Toronto
FundersBiological and Environmental ResearchArgonne National LaboratoryNational Institute of General Medical SciencesOntario Genomics InstituteWestern Economic Diversification CanadaNational Research Council CanadaCanadian Institutes of Health ResearchCancer Prevention and Research Institute of TexasGenentechPriority Academic Program Development of Jiangsu Higher Education InstitutionsU.S. Department of EnergyNational Natural Science Foundation of ChinaGovernment of CanadaEuropean Federation of Pharmaceutical Industries and AssociationsNational Institutes of HealthOntario GenomicsMerck KGaAGenome CanadaNational Cancer InstituteBayerMcGill UniversityUniversity of ChicagoNatural Sciences and Engineering Research Council of CanadaPfizerUniversity of SaskatchewanCanadian Light SourceBristol-Myers Squibb
KeywordsSpinal muscular atrophyCas9Cell biologyBiologyTranscription (linguistics)CRISPRSmall moleculeMutagenesisChemistryComputational biologyGeneticsMutationGene

Abstract

fetched live from OpenAlex

Survival of motor neuron (SMN) functions in diverse biological pathways via recognition of symmetric dimethylarginine (Rme2s) on proteins by its Tudor domain, and deficiency of SMN leads to spinal muscular atrophy. Here we report a potent and selective antagonist with a 4-iminopyridine scaffold targeting the Tudor domain of SMN. Our structural and mutagenesis studies indicate that both the aromatic ring and imino groups of compound 1 contribute to its selective binding to SMN. Various on-target engagement assays support that compound 1 specifically recognizes SMN in a cellular context and prevents the interaction of SMN with the R1810me2s of RNA polymerase II subunit POLR2A, resulting in transcription termination and R-loop accumulation mimicking SMN depletion. Thus, in addition to the antisense, RNAi and CRISPR/Cas9 techniques, potent SMN antagonists could be used as an efficient tool to understand the biological functions of SMN.

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: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.758
Threshold uncertainty score0.502

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 studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.001
Research integrity0.0000.001
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 head0.050
GPT teacher head0.364
Teacher spread0.314 · 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 designNot applicable
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

Citations20
Published2022
Admission routes2
Has abstractyes

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Same venueNature CommunicationsSame topicNeurogenetic and Muscular Disorders ResearchFrench-language works237,207