Evidence map›Paper›PMID 28045983›Full record

ArticlePloS one2017

PPero, a Computational Model for Plant PTS1 Type Peroxisomal Protein Prediction.

Jue Wang, Yejun Wang, Caiji Gao, Liwen Jiang, Dianjing Guo

Open access · goldAbstract read
In one paragraph

Article in PloS one, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.

0numbers the graph read from it
0cells of the map it votes in
14citing papers in PubMed
1.9field-weighted citation impact, top 14% of its field
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

14 citing papers in PubMed, 31 citations in OpenAlex.

  1. Article
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  7. Peroxisomal Proteome Mining of Sweet Pepper (Frontiers in plant science · 2022
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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

5 authors at 2 institutions in 1 country.

Jue WangSchool of Life Sciences and State Key Lab of Agrobiotechnology, The Chinese University of Hong Kong, Hong Kong.
Yejun WangDepartment of Medical Genetics, Shenzhen University Health Science Center, Shenzhen, China.
Caiji GaoSchool of Life Sciences and State Key Lab of Agrobiotechnology, The Chinese University of Hong Kong, Hong Kong.
Liwen JiangSchool of Life Sciences and State Key Lab of Agrobiotechnology, The Chinese University of Hong Kong, Hong Kong.
Dianjing GuoSchool of Life Sciences and State Key Lab of Agrobiotechnology, The Chinese University of Hong Kong, Hong Kong.
Chinese University of Hong Kong · CNShenzhen University Health Science Center · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Well-defined motifs often make it easy to investigate protein function and localization. In plants, peroxisomal proteins are guided to peroxisomes mainly by a conserved type 1 (PTS1) or type 2 (PTS2) targeting signal, and the PTS1 motif is commonly used for peroxisome targeting protein prediction. Currently computational prediction of peroxisome targeted PTS1-type proteins are mostly based on the 3 amino acids PTS1 motif and the adjacent sequence which is less than 14 amino acid residue in length. The potential contribution of the adjacent sequences beyond this short region has never been well investigated in plants. In this work, we develop a bi-profile Bayesian SVM method to extract and learn position-based amino acid features for both PTS1 motifs and their extended adjacent sequences in plants. Our proposed model outperformed other implementations with similar applications and achieved the highest accuracy of 93.6% and 92.6% for Arabidosis and other plant species respectively. A large scale analysis for Arabidopsis, Rice, Maize, Potato, Wheat, and Soybean proteome was conducted using the proposed model and a batch of candidate PTS1 proteins were predicted. The DNA segments corresponding to the C-terminal sequences of 9 selected candidates were cloned and transformed into Arabidopsis for experimental validation, and 5 of them demonstrated peroxisome targeting.

Indexed as

Computer SimulationAlgorithmsAmino Acid MotifsAmino AcidsArabidopsisArabidopsis ProteinsBayes TheoremComputational BiologyGenome, PlantGlycine maxMicroscopy, ConfocalOryzaPeroxisomesPeroxisome-Targeting Signal 1 ReceptorPlant ProteinsProbabilityAmino AcidsArabidopsis ProteinsPeroxisome-Targeting Signal 1 ReceptorPlant ProteinsProtein Sorting SignalsProteomeReceptors, Cytoplasmic and Nuclear

Identifiers

PMID28045983
PMCPMC5207514
OpenAlexW2571036315

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

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.