Evidence map›Paper›PMID 34795380›Full record

ArticleScientific reports2021

Robust unsupervised deconvolution of linear motifs characterizes 68 protein modifications at proteome scale.

Theodore G Smith, Anuli C Uzozie, Siyuan Chen, Philipp F Lange

Abstract read
In one paragraph

Article in Scientific reports, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

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Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Review
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

4 authors.

Theodore G SmithDepartment of Pathology, University of British Columbia, Vancouver, BC, Canada.
Anuli C UzozieDepartment of Pathology, University of British Columbia, Vancouver, BC, Canada.
Siyuan ChenDepartment of Pathology, University of British Columbia, Vancouver, BC, Canada.
Philipp F LangeDepartment of Pathology, University of British Columbia, Vancouver, BC, Canada. philipp.lange@ubc.ca.

Funding

Natural Sciences and Engineering Research Council of Canada RGPIN-2018-05645
6 · The paper itself

Abstract

The local sequence context is the most fundamental feature determining the post-translational modification (PTM) of proteins. Recent technological improvements allow for the detection of new and less prevalent modifications. We found that established state-of-the-art algorithms for the detection of PTM motifs in complex datasets failed to keep up with this technological development and are no longer robust. To overcome this limitation, we developed RoLiM, a new linear motif deconvolution algorithm and webserver, that enables robust and unbiased identification of local amino acid sequence determinants in complex biological systems demonstrated here by the analysis of 68 modifications found across 30 tissues in the human draft proteome map. Furthermore, RoLiM analysis of a large-scale phosphorylation dataset comprising 30 kinase inhibitors of 10 protein kinases in the EGF signalling pathway identified prospective substrate motifs for PI3K and EGFR.

Indexed as

ProteomeAlgorithmsAmino Acid MotifsCluster AnalysisComputer SimulationErbB ReceptorsHumansModels, StatisticalPhosphatidylinositol 3-KinasesPhosphorylationProspective StudiesProtein Processing, Post-TranslationalProteomicsSystems BiologyErbB ReceptorsPhosphatidylinositol 3-KinasesProteome

Identifiers

PMID34795380
PMCPMC8602328

What OpenQuestion holds

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.