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Nat Commun July 2023 PMID 37443362

Discovering functionally important sites in proteins.

Matteo Cagiada, Sandro Bottaro, Søren Lindemose, Signe M Schenstrøm, Amelie Stein, Rasmus Hartmann-Petersen, Kresten Lindorff-Larsen

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Mentions

109
Peak: 64 in Jul 2023
across 3 sources since Jul 2022

Extended reach

321,476
potential audience

Total likes

518

Video views

38

Mentions per month

Jul 2022 – Oct 2023

Where it's discussed

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AI synopsis

What people are saying

The discussions center on the research's relevance and methodology, with phrases like 'machine-learning model' and reference to '2D-multiplexed data' used to illustrate its sophisticated approach. The tone reflects excitement and pride in the work, particularly in celebrating Matteo Cagiada’s PhD defense, which adds a personal touch to the scientific achievements being highlighted.

AgreementModerate agreement

Most discussions indicate support for the publication's findings and the work presented by Matteo Cagiada.

InterestHigh interest

The discussions showcase a strong curiosity about the research, particularly regarding its implications for predicting important protein sites.

EngagementModerate engagement

While many posts are brief, there are comments that delve into the methodologies used, suggesting a moderate depth of engagement.

ImpactHigh impact

Participants perceive the research as having significant implications for the field of bioinformatics, highlighting its potential applications.

Abstract in brief

  • Proteins are crucial in various biological fields, and missense variants can lead to diseases, but identifying important protein sites is challenging due to limited data.
  • A new machine learning approach combines statistical models of protein sequences with biophysical stability models to predict functional sites effectively.
  • This method was validated using experimental data on variant effects, illustrating its application in identifying active, regulatory, and binding sites, including a case study on HPRT1 related to Lesch-Nyhan syndrome.
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Kresten Lindorff-Larsen
@LindorffLarsen · X

I'm happy to present you the final version of Matteo's work on predicting functionally important sites in proteins using a machine-learning model that leverages structure and sequences and is trained using 2D-multiplexed data on abundance and activity https://t.co/YoIjLpRVIi https://t.co/IbIHFoBi7O

Jul 13, 2023 · 290 likes · 57 shares · 14,523 followers View post →
BioDecoded
@BioDecoded · X

Discovering functionally important sites in proteins | Nature Communications https://t.co/H15v2dQfOZ #Bioinformatics https://t.co/yIWLSUYuAT

Aug 12, 2023 · 126 likes · 33 shares · 8,899 followers View post →
bioRxiv Biophysics
@biorxiv_biophys · X

Discovering functionally important sites in proteins https://t.co/F0OM2LnjXI #biorxiv_biophys

Jul 16, 2022 · 38 likes · 4 shares · 5,378 followers View post →
bioRxiv
@biorxivpreprint · X

Discovering functionally important sites in proteins https://t.co/OSFV2hIOer #bioRxiv

Jul 16, 2022 · 23 likes · 4 shares · 136,946 followers View post →
ShorterLab
@ShorterLab · X

Discovering functionally important sites in proteins: https://t.co/CX7fJjqvFA

Jul 14, 2023 · 20 likes · 7 shares · 12,044 followers View post →
Kresten Lindorff-Larsen
@lindorfflarsen.bsky.social · Bluesky

Huge congratulations to Matteo Cagiada from the group who today defended his PhD

Oct 3, 2023 · 12 likes · 1 share View post →

All mentions

Kresten Lindorff-Larsen
@lindorfflarsen.bsky.social · Bluesky

Huge congratulations to Matteo Cagiada from the group who today defended his PhD

Oct 3, 2023 · 12 likes · 1 share View post →
Lakshitha Perera, Ph.D.
@lakshithacomp · X

RT @LindorffLarsen: I'm happy to present you the final version of Matteo's work on predicting functionally important sites in proteins usin…

Sep 12, 2023 · 57 shares · 169 followers View post →
M. Kamran Azim
@KamranAzim2 · X

RT @BioDecoded: Discovering functionally important sites in proteins | Nature Communications https://t.co/H15v2dQfOZ #Bioinformatics https…

Aug 17, 2023 · 33 shares · 116 followers View post →
Naphak Modhiran
@cusycon · X

RT @BioDecoded: Discovering functionally important sites in proteins | Nature Communications https://t.co/H15v2dQfOZ #Bioinformatics https…

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@arkajit_de · X

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@drzohaibh · X

RT @BioDecoded: Discovering functionally important sites in proteins | Nature Communications https://t.co/H15v2dQfOZ #Bioinformatics https…

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Dr. Nayab Hussain (Ph.D.)
@me_Nayab · X

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しんしあ@バイオテクコミュニティ「BioSpace」モデレーター
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Lisandro H. Otero
@LisandroHOtero1 · X

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Hiroshi Fujisaki
@hiro2fuji · X

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Adrian Mulholland
@AdrianMulholla1 · X

RT @BioDecoded: Discovering functionally important sites in proteins | Nature Communications https://t.co/H15v2dQfOZ #Bioinformatics https…

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Chrystian Camilo Sosa Arango
@SosaChrystian · X

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Mitchell Parker, MD, PhD
@Mitch_P · X

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BioDecoded
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Discovering functionally important sites in proteins | Nature Communications https://t.co/H15v2dQfOZ #Bioinformatics https://t.co/yIWLSUYuAT

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Cell Biology J-Club
@cell_club · X

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Scientek
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Kresten Lindorff-Larsen
@LindorffLarsen · X

I'm happy to present you the final version of Matteo's work on predicting functionally important sites in proteins using a machine-learning model that leverages structure and sequences and is trained using 2D-multiplexed data on abundance and activity https://t.co/YoIjLpRVIi https://t.co/IbIHFoBi7O

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RT @biorxiv_biophys: Discovering functionally important sites in proteins https://t.co/F0OM2LnjXI #biorxiv_biophys

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The Colombo Lab
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RT @biorxiv_biophys: Discovering functionally important sites in proteins https://t.co/F0OM2LnjXI #biorxiv_biophys

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Biorxiv Reddit
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Discovering functionally important sites in proteins https://t.co/XRXWFgkXb1

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Naoya KOBAYASHI
@kobnaoya · X

RT @biorxivpreprint: Discovering functionally important sites in proteins https://t.co/OSFV2hIOer #bioRxiv

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bioRxiv Biophysics
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Discovering functionally important sites in proteins https://t.co/F0OM2LnjXI #biorxiv_biophys

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bioRxiv
@biorxivpreprint · X

Discovering functionally important sites in proteins https://t.co/OSFV2hIOer #bioRxiv

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