Evidence map›Paper›PMID 39005363›Full record

ArticlebioRxiv : the preprint server for biology2024

Deep mutational scanning reveals sequence to function constraints for SWEET family transporters.

Krishna K Narayanan, Austin T Weigle, Lingyun Xu, Xuenan Mi, Chen Zhang, Li-Qing Chen, Erik Procko, Diwakar Shukla

Abstract readPreprint
In one paragraph

Article in bioRxiv : the preprint server for biology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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0cells of the map it votes in
0citing 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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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.

Krishna K NarayananDepartment of Biochemistry, University of Illinois Urbana-Champaign, Urbana, Illinois 61801, United States.
Austin T WeigleDepartment of Chemistry, University of Illinois Urbana-Champaign, Urbana, Illinois 61801, United States.
Lingyun XuDepartment of Bioengineering, University of Illinois Urbana-Champaign, Urbana, Illinois 61801, United States.
Xuenan MiCenter for Biophysics and Quantitative Biology, University of Illinois Urbana-Champaign, Urbana, Illinois 61801, United States.
Chen ZhangDepartment of Plant Biology, University of Illinois Urbana-Champaign, Urbana, Illinois 61801, United States.
Li-Qing ChenDepartment of Plant Biology, University of Illinois Urbana-Champaign, Urbana, Illinois 61801, United States.
Erik ProckoDepartment of Biochemistry, University of Illinois Urbana-Champaign, Urbana, Illinois 61801, United States.
Diwakar ShuklaDepartment of Chemical & Biomolecular Engineering; Department of Plant Biology; Department of Bioengineering; Department of Chemistry, Center for Biophysics and Quantitative Biology, University of Illinois Urbana-Champaign, Urbana, Illinois 61801, United States.ORCID 0000-0003-4079-5381

Funding

Elucidating sequence, structural and dynamic basis of the functional regulation of membrane proteinsR35GM142745 · NIGMS · UNIVERSITY OF ILLINOIS AT URBANA-CHAMPAIGN · PI SHUKLA, DIWAKAR · 2021 to 2025
$1.8M
NIGMS NIH HHS R35 GM142745
6 · The paper itself

Abstract

Protein science is entering a transformative phase enabled by deep mutational scans that provide an unbiased view of the residue level interactions that mediate function. However, it has yet to be extensively used to characterize the mutational and evolutionary landscapes of plant proteins. Here, we apply the method to explore sequence-function relationships within the sugar transporter AtSWEET13. DMS results describe how mutational interrogation throughout different regions of the protein affects AtSWEET13 abundance and transport function. Our results identify novel transport-enhancing mutations that are validated using the FRET sensor assays. Extending DMS results to phylogenetic analyses reveal the role of transmembrane helix 4 (TM4) which makes the SWEET family transporters distinct from prokaryotic SemiSWEETs. We show that transmembrane helix 4 is intolerant to motif swapping with other clade-specific SWEET TM4 compositions, despite accommodating single point-mutations towards aromatic and charged polar amino acids. We further show that the transfer learning approaches based on physics and ML based

Identifiers

PMID39005363
PMCPMC11244857

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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.