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Type: Journal article
Title: Roles of membrane transporters: connecting the dots from sequence to phenotype
Author: David, R.
Byrt, C.S.
Tyerman, S.D.
Gilliham, M.
Wege, S.
Citation: Annals of Botany, 2019; 124(2):201-208
Publisher: Oxford University Press
Issue Date: 2019
ISSN: 0305-7364
Statement of
Rakesh David, Caitlin S. Byrt, Stephen D. Tyerman, Matthew Gilliham, and Stefanie Wege
Abstract: Background: Plant membrane transporters are involved in diverse cellular processes underpinning plant physiology, such as nutrient acquisition, hormone movement, resource allocation, exclusion or sequestration of various solutes from cells and tissues, and environmental and developmental signalling. A comprehensive characterization of transporter function is therefore key to understanding and improving plant performance. Scope and conclusions: In this review, we focus on the complexities involved in characterizing transporter function and the impact that this has on current genomic annotations. Specific examples are provided that demonstrate why sequence homology alone cannot be relied upon to annotate and classify transporter function, and to show how even single amino acid residue variations can influence transporter activity and specificity. Misleading nomenclature of transporters is often a source of confusion in transporter characterization, especially for people new to or outside the field. Here, to aid researchers dealing with interpretation of large data sets that include transporter proteins, we provide examples of transporters that have been assigned names that misrepresent their cellular functions. Finally, we discuss the challenges in connecting transporter function at the molecular level with physiological data, and propose a solution through the creation of new databases. Further fundamental in-depth research on specific transport (and other) proteins is still required; without it, significant deficiencies in large-scale data sets and systems biology approaches will persist. Reliable characterization of transporter function requires integration of data at multiple levels, from amino acid residue sequence annotation to more in-depth biochemical, structural and physiological studies.
Keywords: Transport proteins; large data sets; protein classification; machine learning
Rights: © The Author(s) 2019. Published by Oxford University Press on behalf of the Annals of Botany Company. All rights reserved. For permissions, please e-mail:
DOI: 10.1093/aob/mcz066
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Appears in Collections:Agriculture, Food and Wine publications
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