Evidence map›Paper›PMID 37973981›Full record

ArticleNature communications2023

Simulation-guided engineering of split GFPs with efficient β-strand photodissociation.

Yasmin Shamsudin, Alice R Walker, Chey M Jones, Todd J Martínez, Steven G Boxer

Abstract read
In one paragraph

Article in Nature communications, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. Article
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.

Yasmin ShamsudinDepartment of Chemistry, Stanford University, Stanford, CA, 94305, USA. yasmin.shamsudin@kemi.uu.se.ORCID 0000-0002-6249-9877
Alice R WalkerDepartment of Chemistry, Stanford University, Stanford, CA, 94305, USA.ORCID 0000-0002-8617-3425
Chey M JonesDepartment of Chemistry, Stanford University, Stanford, CA, 94305, USA.ORCID 0000-0002-8173-7957
Todd J MartínezDepartment of Chemistry, Stanford University, Stanford, CA, 94305, USA.ORCID 0000-0002-4798-8947
Steven G BoxerDepartment of Chemistry, Stanford University, Stanford, CA, 94305, USA. sboxer@stanford.edu.ORCID 0000-0001-9167-4286

Funding

Biophysical Studies of Macromolecules and Molecular AssembliesR35GM118044 · NIGMS · STANFORD UNIVERSITY · PI STEVEN G. BOXER · 2016 to 2026
$7.2M
NIGMS NIH HHS R35 GM118044
6 · The paper itself

Abstract

Green fluorescent proteins (GFPs) are ubiquitous for protein tagging and live-cell imaging. Split-GFPs are widely used to study protein-protein interactions by fusing proteins of interest to split GFP fragments that create a fluorophore upon typically irreversible complementation. Thus, controlled dissociation of the fragments is desirable. Although we have found that split strands can be photodissociated, the quantum efficiency of light-induced photodissociation of split GFPs is low. Traditional protein engineering approaches to increase efficiency, including extensive mutagenesis and screening, have proved difficult to implement. To reduce the search space, key states in the dissociation process are modeled by combining classical and enhanced sampling molecular dynamics with QM/MM calculations, enabling the rational design and engineering of split GFPs with up to 20-fold faster photodissociation rates using non-intuitive amino acid changes. This demonstrates the feasibility of modeling complex molecular processes using state-of-the-art computational methods, and the potential of integrating computational methods to increase the success rate in protein engineering projects.

Indexed as

Molecular Dynamics SimulationBiophysical PhenomenaGreen Fluorescent ProteinsMutagenesisProtein Conformation, beta-StrandGreen Fluorescent Proteins

Identifiers

PMID37973981
PMCPMC10654500

What OpenQuestion holds

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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.