Evidence map›Paper›PMID 33566620›Full record

ArticleThe journal of physical chemistry. B2021

Computational Investigation of Structural and Spectroscopic Properties of LOV-Based Proteins with Improved Fluorescence.

Felipe Cardoso Ramos, Lorenzo Cupellini, Benedetta Mennucci

Abstract read
In one paragraph

Article in The journal of physical chemistry. B, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.

0numbers the graph read from it
0cells of the map it votes in
7citing 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

7 citing papers in PubMed.

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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.

Felipe Cardoso RamosDipartimento di Chimica e Chimica Industriale, University of Pisa, Via G. Moruzzi 13, Pisa, I-56124, Italy.
Lorenzo CupelliniDipartimento di Chimica e Chimica Industriale, University of Pisa, Via G. Moruzzi 13, Pisa, I-56124, Italy.ORCID 0000-0003-0848-2908
Benedetta MennucciDipartimento di Chimica e Chimica Industriale, University of Pisa, Via G. Moruzzi 13, Pisa, I-56124, Italy.ORCID 0000-0002-4394-0129

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Flavin-based fluorescent proteins are a class of fluorescent reporters derived from light, oxygen, and voltage (LOV) sensing proteins. Through mutagenesis, natural LOV proteins have been engineered to obtain improved fluorescence properties. In this study, we combined extended classical Molecular Dynamics simulations and multiscale Quantum Mechanics/Molecular Mechanics methods to clarify the relationship between structural and dynamic changes induced by specific mutations and the spectroscopic response. To reach this goal we compared two LOV variants, one obtained by the single mutation needed to photochemically inactivate the natural system, and the other (iLOV) obtained through additional mutations and characterized by a significantly improved fluorescence. Our simulations confirmed the "flipping and crowding" effect induced in iLOV by the additional mutations and revealed its mechanism of action. We also showed that these mutations, and the resulting differences in the composition and flexibility of the binding pockets, are not reflected in significant shifts of the excitation and emission energies, in agreement with the similarity of the spectra measured for the two systems. However, a small but consistent reduction was found in the Stokes shift of iLOV, suggesting a reduction of the intermolecular reorganization experienced by the chromophore after excitation, which could slow down its internal conversion to the ground state and improve the fluorescence.

Indexed as

OxygenProteinsFlavinsLightMolecular Dynamics SimulationSpectrum AnalysisFlavinsOxygenProteins

Identifiers

PMID33566620
PMCPMC7917436

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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.