Literature DB >> 26556651

Finding the Subcellular Location of Barley, Wheat, Rice and Maize Proteins: The Compendium of Crop Proteins with Annotated Locations (cropPAL).

Cornelia M Hooper1, Ian R Castleden2, Nader Aryamanesh2, Richard P Jacoby2, A Harvey Millar2.   

Abstract

Barley, wheat, rice and maize provide the bulk of human nutrition and have extensive industrial use as agricultural products. The genomes of these crops each contains >40,000 genes encoding proteins; however, the major genome databases for these species lack annotation information of protein subcellular location for >80% of these gene products. We address this gap, by constructing the compendium of crop protein subcellular locations called crop Proteins with Annotated Locations (cropPAL). Subcellular location is most commonly determined by fluorescent protein tagging of live cells or mass spectrometry detection in subcellular purifications, but can also be predicted from amino acid sequence or protein expression patterns. The cropPAL database collates 556 published studies, from >300 research institutes in >30 countries that have been previously published, as well as compiling eight pre-computed subcellular predictions for all Hordeum vulgare, Triticum aestivum, Oryza sativa and Zea mays protein sequences. The data collection including metadata for proteins and published studies can be accessed through a search portal http://crop-PAL.org. The subcellular localization information housed in cropPAL helps to depict plant cells as compartmentalized protein networks that can be investigated for improving crop yield and quality, and developing new biotechnological solutions to agricultural challenges.
© The Author 2015. Published by Oxford University Press on behalf of Japanese Society of Plant Physiologists. All rights reserved. For permissions, please email: journals.permissions@oup.com.

Entities:  

Keywords:  Cell Biology; Compartments; Crop; Database; Proteomes; Subcellular localizations

Mesh:

Substances:

Year:  2015        PMID: 26556651     DOI: 10.1093/pcp/pcv170

Source DB:  PubMed          Journal:  Plant Cell Physiol        ISSN: 0032-0781            Impact factor:   4.927


  18 in total

1.  Roles of membrane transporters: connecting the dots from sequence to phenotype.

Authors:  Rakesh David; Caitlin S Byrt; Stephen D Tyerman; Matthew Gilliham; Stefanie Wege
Journal:  Ann Bot       Date:  2019-09-24       Impact factor: 4.357

Review 2.  Plant Reactome and PubChem: The Plant Pathway and (Bio)Chemical Entity Knowledgebases.

Authors:  Parul Gupta; Sushma Naithani; Justin Preece; Sunghwan Kim; Tiejun Cheng; Peter D'Eustachio; Justin Elser; Evan E Bolton; Pankaj Jaiswal
Journal:  Methods Mol Biol       Date:  2022

3.  Nitrate-responsive transcriptome analysis reveals additional genes/processes and associated traits viz. height, tillering, heading date, stomatal density and yield in japonica rice.

Authors:  Vikas Kumar Mandal; Annie Prasanna Jangam; Navjyoti Chakraborty; Nandula Raghuram
Journal:  Planta       Date:  2022-01-17       Impact factor: 4.116

4.  Subcellular Proteomics as a Unified Approach of Experimental Localizations and Computed Prediction Data for Arabidopsis and Crop Plants.

Authors:  Cornelia M Hooper; Ian R Castleden; Sandra K Tanz; Sally V Grasso; A Harvey Millar
Journal:  Adv Exp Med Biol       Date:  2021       Impact factor: 2.622

5.  SUBA4: the interactive data analysis centre for Arabidopsis subcellular protein locations.

Authors:  Cornelia M Hooper; Ian R Castleden; Sandra K Tanz; Nader Aryamanesh; A Harvey Millar
Journal:  Nucleic Acids Res       Date:  2016-11-28       Impact factor: 16.971

6.  LOCALIZER: subcellular localization prediction of both plant and effector proteins in the plant cell.

Authors:  Jana Sperschneider; Ann-Maree Catanzariti; Kathleen DeBoer; Benjamin Petre; Donald M Gardiner; Karam B Singh; Peter N Dodds; Jennifer M Taylor
Journal:  Sci Rep       Date:  2017-03-16       Impact factor: 4.379

7.  MU-LOC: A Machine-Learning Method for Predicting Mitochondrially Localized Proteins in Plants.

Authors:  Ning Zhang; R S P Rao; Fernanda Salvato; Jesper F Havelund; Ian M Møller; Jay J Thelen; Dong Xu
Journal:  Front Plant Sci       Date:  2018-05-23       Impact factor: 5.753

8.  Genome-Scale Characterization of Predicted Plastid-Targeted Proteomes in Higher Plants.

Authors:  Ryan W Christian; Seanna L Hewitt; Eric H Roalson; Amit Dhingra
Journal:  Sci Rep       Date:  2020-05-19       Impact factor: 4.379

9.  Multiple marker abundance profiling: combining selected reaction monitoring and data-dependent acquisition for rapid estimation of organelle abundance in subcellular samples.

Authors:  Cornelia M Hooper; Tim J Stevens; Anna Saukkonen; Ian R Castleden; Pragya Singh; Gregory W Mann; Bertrand Fabre; Jun Ito; Michael J Deery; Kathryn S Lilley; Christopher J Petzold; A Harvey Millar; Joshua L Heazlewood; Harriet T Parsons
Journal:  Plant J       Date:  2017-11-20       Impact factor: 6.417

Review 10.  A Proteomic View on the Role of Legume Symbiotic Interactions.

Authors:  Estíbaliz Larrainzar; Stefanie Wienkoop
Journal:  Front Plant Sci       Date:  2017-07-18       Impact factor: 5.753

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