Evidence map›Paper›PMID 30016513›Full record

ArticleBioinformatics (Oxford, England)2019

Kinome-wide identification of phosphorylation networks in eukaryotic proteomes.

Luca Parca, Bruno Ariano, Andrea Cabibbo, Marco Paoletti, Annalaura Tamburrini, Antonio Palmeri, Gabriele Ausiello, Manuela Helmer-Citterich

Abstract read
In one paragraph

Article in Bioinformatics (Oxford, England), 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 42 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
42citing papers in PubMed, 1 pooled it
–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

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

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

42 citing papers in PubMed, 1 synthesis or guideline pooled it.

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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

8 authors.

Luca ParcaDepartment of Biology, Centro di Bioinformatica Molecolare, University of Rome "Tor Vergata", Rome, Italy.
Bruno ArianoDepartment of Biology, Centro di Bioinformatica Molecolare, University of Rome "Tor Vergata", Rome, Italy.
Andrea CabibboDepartment of Biology, Centro di Bioinformatica Molecolare, University of Rome "Tor Vergata", Rome, Italy.
Marco PaolettiDepartment of Biology, Centro di Bioinformatica Molecolare, University of Rome "Tor Vergata", Rome, Italy.
Annalaura TamburriniDepartment of Biology, Centro di Bioinformatica Molecolare, University of Rome "Tor Vergata", Rome, Italy.
Antonio PalmeriDepartment of Biology, Centro di Bioinformatica Molecolare, University of Rome "Tor Vergata", Rome, Italy.
Gabriele AusielloDepartment of Biology, Centro di Bioinformatica Molecolare, University of Rome "Tor Vergata", Rome, Italy.
Manuela Helmer-CitterichDepartment of Biology, Centro di Bioinformatica Molecolare, University of Rome "Tor Vergata", Rome, Italy.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Motivation: Signaling and metabolic pathways are finely regulated by a network of protein phosphorylation events. Unraveling the nature of this intricate network, composed of kinases, target proteins and their interactions, is therefore of crucial importance. Although thousands of kinase-specific phosphorylations (KsP) have been annotated in model organisms their kinase-target network is far from being complete, with less studied organisms lagging behind. Results: In this work, we achieved an automated and accurate identification of kinase domains, inferring the residues that most likely contribute to peptide specificity. We integrated this information with the target peptides of known human KsP to predict kinase-specific interactions in other eukaryotes through a deep neural network, outperforming similar methods. We analyzed the differential conservation of kinase specificity among eukaryotes revealing the high conservation of the specificity of tyrosine kinases. With this approach we discovered 1590 novel KsP of potential clinical relevance in the human proteome. Availability and implementation: http://akid.bio.uniroma2.it. Supplementary information: Supplementary data are available at Bioinformatics online.

Indexed as

ProteomeSignal TransductionEukaryotaHumansPhosphorylationPhosphotransferasesPhosphotransferasesProteome

Identifiers

PMID30016513
PMCPMC6361239

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.