Evidence map›Paper›PMID 42055456›Full record

ReviewThe Plant journal : for cell and molecular biology2026

Advances and opportunities for computational interrogation of plant proteins.

Sarah M Bohling, Shaila Musharoff, Laura H Gunn

Abstract readReview
In one paragraph

Review in The Plant journal : for cell and molecular biology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
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

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

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

3 authors.

Sarah M BohlingPlant Biology Section, Cornell University, Ithaca, New York, USA.ORCID https://orcid.org/0009-0007-3071-6551
Shaila MusharoffDepartment of Computational Biology, Cornell University, Ithaca, New York, USA.
Laura H GunnPlant Biology Section, Cornell University, Ithaca, New York, USA.

Funding

U.S. Department of Energy DE-SC0024175
6 · The paper itself

Abstract

Plants exhibit remarkable biochemical and physiological diversity, and are capable of adapting to a wide range of environmental conditions and stresses. This complexity makes them essential systems for understanding how life responds to a changing climate. Plant proteins are the molecular engines that carry out the reactions, signalling and regulation underlying these adaptive processes. However, studying plant proteins remains constrained by limited experimental throughput and the challenges of genetic manipulation, which vary widely across species. While synthetic biology and heterologous expression systems have expanded opportunities to investigate plant proteins, in planta studies are still limited by the availability and efficiency of genetic transformation methods. Computational approaches offer a powerful complement to experimental research by generating high-throughput, testable hypotheses that can accelerate discovery of plant protein function. In recent years, the power, versatility and ease of use of computational tools for protein research have expanded dramatically. These methods now enable detailed predictions of protein structure, dynamics and interactions, as well as insights into their evolutionary history and mechanistic function. In this review, we highlight the expanding computational toolkit for plant protein analysis, emphasising both established and emerging approaches. We summarise recent successes where computational methods have provided key biological insights into plant protein function and highlight the potential of such methods for scientific discovery in plant research. By integrating computation with experimentation, plant biology can overcome current limitations to studying plant proteins and move more rapidly toward a mechanistic understanding of plant processes, enabling advances in agriculture, ecology and climate resilience.

Indexed as

Computational BiologyPlant ProteinsPlantsPlant Proteinsancestral sequence reconstructioncomputational plant biologyfunctional annotationmachine learning and protein modellingmolecular dynamics simulationspost‐translational modificationsprotein–protein interactionsprotein stability and flexibilityprotein structure prediction

Identifiers

PMID42055456
PMCPMC13128292

What OpenQuestion holds

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LicenceCC BY
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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.