Johannes Rebelein

Nitrogenase, Biotic and Artificial Metalloenzymes, CO2 Fixation, Emmy Noether Research Group Leader

[Johannes Rebelein @RebeleinLab]

Our latest work on the nitrogenase-like methylthio-alkane reductase is now out
@NatureCatalysis
https://t.co/NxFGaYg7Xb We biochemically and structurally characterized the methylthio-alkane reductase, a metalloenzyme that can produce ethylene from organic sulfur compounds.

[Antonio J. Pierik @AJPierik]

We are hiring a Biophysics professor. Have a look at https://t.co/TQzJf77NzW

[Johannes Rebelein @RebeleinLab]

@HuRibbe @angew_chem @UCIBioSci @UCIChemistry Congrats!

[Johannes Rebelein @RebeleinLab]

@LennartSchada @ChariteBerlin @UniLeidenNews Congrats! That's awesome!

[Johannes Rebelein @RebeleinLab]

Great work by the Pierik and Netz group in identifying a new Fe/S protein.

[Antonio J. Pierik @AJPierik]

We proudly present the birth of an in vivo and in vitro certified new Fe/S protein in eukaryotes: The function of Mak16 in ribosome biogenesis depends on its [4Fe-4S] cluster
https://t.co/Fnaep96Y3Q

[René Inckemann @rene_inckemann]

Very excited to finally share my PhD paper, about advancing #chloroplast #synbio through high-throughput plastome engineering of #Chlamydomonas.
Huge thanks to the whole team!
https://t.co/uqT72tIS2i

[VAAM - Vereinigung f. Allg. u. Ang. Mikrobiologie @VAAM_Microbes]

📣Final week to submit your abstract for the VAAM Annual Conference #VAAM2026 in Berlin
Get your submission in before time runs out:
Abstract Deadline 📆7 November – no extension!
🎓 VAAM student members: Travel grant application also until 7 Nov
📩submit: https://t.co/LHI9Ehe3pI

[Johannes Rebelein @RebeleinLab]

@AJPierik @NatureCatalysis I totally agree! Thank you for your support!

[Johannes Rebelein @RebeleinLab]

Our latest work on the nitrogenase-like methylthio-alkane reductase is now out
@NatureCatalysis
https://t.co/NxFGaYg7Xb We biochemically and structurally characterized the methylthio-alkane reductase, a metalloenzyme that can produce ethylene from organic sulfur compounds.

[Johannes Rebelein @RebeleinLab]

@juanmangas @NatureCatalysis Thank you Juan!

[Johannes Rebelein @RebeleinLab]

@jessicacsfla @Trematode12 @charliebuchanan @TristanTiboReif @frederikschmi @KaHochberg and @AJPierik. This work was done
@mpi_marburg.

[Johannes Rebelein @RebeleinLab]

This fantastic work was mainly done by Ana Lago Maciel as well as our collaboration partners @jessicacsfla, @Trematode12, @charliebuchanan, @TristanTiboReif, @frederikschmi, Stefano Lometto, Nicole Paczia, Jan M. Schuller, @DFlammingHansen, Gabriella T. Heller, Simone Prinz,

[Johannes Rebelein @RebeleinLab]

An open position at my alma mater UCI in structural biology...

[Ribbe Hu Labs @HuRibbe]

Our home department of Molecular Biology & Biochemistry at UC Irvine is looking to hire a new tenure-track assistant professor in the broad area of structural biology. Come be our colleague! https://t.co/SRSQEqHGiP

Please apply and/or share this post!

@UCIBioSci; @UCIrvine

[Ribbe Hu Labs @HuRibbe]

Our home department of Molecular Biology & Biochemistry at UC Irvine is looking to hire a new tenure-track assistant professor in the broad area of structural biology. Come be our colleague! https://t.co/SRSQEqHGiP

Please apply and/or share this post!

@UCIBioSci; @UCIrvine

[Johannes Rebelein @RebeleinLab]

Sounds like a great tool!

[Biology+AI Daily @BiologyAIDaily]

PymolFold: A PyMOL Plugin for API-driven Structure Prediction and Quality Assessment

1. PymolFold is a novel PyMOL plugin that integrates state-of-the-art protein structure prediction models like ESM-3 and Boltz2 into the PyMOL visualization environment, creating a unified workflow for prediction, visualization, and analysis. This integration significantly lowers the technical barriers for experimental scientists who lack specialized hardware or computational expertise.

2. The plugin offers both graphical and command-line interfaces, providing flexibility for users with different preferences. It incorporates PXMeter for immediate quantitative benchmarking against reference structures, enabling researchers to validate their structural hypotheses directly within PyMOL. This streamlined workflow accelerates the pace of discovery in structural biology and drug design.

3. PymolFold supports monomer and multimer predictions through API access, eliminating the need for local deployment of complex models. Case studies across proteins of varying lengths and complexities demonstrate that Boltz2 without MSA offers the fastest predictions for most typical proteins, while ESM-3 is more efficient for very large or multi-domain proteins. These insights help users choose the optimal workflow based on their specific needs.

4. The plugin’s architecture ensures that PyMOL remains responsive during background tasks such as API calls. It uses a multi-processed, non-blocking design, allowing users to continue working while predictions are being processed. This responsiveness is crucial for maintaining an efficient workflow in a dynamic research environment.

5. PymolFold also excels in complex system prediction and validation. For example, it successfully predicted the structure of the immune checkpoint protein PD-1 bound to a therapeutic Fab antibody fragment, achieving highly accurate interface metrics. The entire process, from sequence input to quantitative analysis, was completed in just a few minutes, highlighting the practical utility of PymolFold for complex systems.

6. The plugin is freely available for academic use on GitHub, along with detailed installation instructions and documentation. This accessibility ensures that a wide range of researchers can benefit from the advanced structural modeling capabilities offered by PymolFold.

📜Paper: https://t.co/OaBLtaBTcA
#PymolFold #ProteinStructurePrediction #StructuralBiology #DrugDesign #PyMOL #API #ComputationalBiology

[Biology+AI Daily @BiologyAIDaily]

PymolFold: A PyMOL Plugin for API-driven Structure Prediction and Quality Assessment

1. PymolFold is a novel PyMOL plugin that integrates state-of-the-art protein structure prediction models like ESM-3 and Boltz2 into the PyMOL visualization environment, creating a unified workflow for prediction, visualization, and analysis. This integration significantly lowers the technical barriers for experimental scientists who lack specialized hardware or computational expertise.

2. The plugin offers both graphical and command-line interfaces, providing flexibility for users with different preferences. It incorporates PXMeter for immediate quantitative benchmarking against reference structures, enabling researchers to validate their structural hypotheses directly within PyMOL. This streamlined workflow accelerates the pace of discovery in structural biology and drug design.

3. PymolFold supports monomer and multimer predictions through API access, eliminating the need for local deployment of complex models. Case studies across proteins of varying lengths and complexities demonstrate that Boltz2 without MSA offers the fastest predictions for most typical proteins, while ESM-3 is more efficient for very large or multi-domain proteins. These insights help users choose the optimal workflow based on their specific needs.

4. The plugin’s architecture ensures that PyMOL remains responsive during background tasks such as API calls. It uses a multi-processed, non-blocking design, allowing users to continue working while predictions are being processed. This responsiveness is crucial for maintaining an efficient workflow in a dynamic research environment.

5. PymolFold also excels in complex system prediction and validation. For example, it successfully predicted the structure of the immune checkpoint protein PD-1 bound to a therapeutic Fab antibody fragment, achieving highly accurate interface metrics. The entire process, from sequence input to quantitative analysis, was completed in just a few minutes, highlighting the practical utility of PymolFold for complex systems.

6. The plugin is freely available for academic use on GitHub, along with detailed installation instructions and documentation. This accessibility ensures that a wide range of researchers can benefit from the advanced structural modeling capabilities offered by PymolFold.

📜Paper: https://t.co/OaBLtaBTcA
#PymolFold #ProteinStructurePrediction #StructuralBiology #DrugDesign #PyMOL #API #ComputationalBiology

[Dr. Catharine Young @DrCatharineY]

I fear the public will never truly realize the immense damage done to our scientific research system and its consequences. A loss of ~$4.5 billion in frozen or cancelled grants with at least 148 impacted clinical trials = 138,000 patients due to be enrolled or already enrolled.

[Johannes Rebelein @RebeleinLab]

Fantastic work by the Narayan Lab connecting enzyme sequence space and substrate diversity!

[Narayan Lab @ UMich @NarayanLab]

CATNIP for the win! Read our newest work with the Gomes group- https://t.co/lILWdTxbVR

@Apatoneh @gabepgomes @daniil_boiko @AlisonNarayanUM

[Oliver Wenger @WengerOliver]

New molecular design absorbs 2 photons to store 2 positive and 2 negative charges

100 ns lifetime, 3 eV energy storage, and 37% quantum yield

A step toward multi-electron photochemistry

Mathis Brändlin and @BjornPfund in @NatureChemistry

https://t.co/CwtRDeoRY3

[Johannes Rebelein @RebeleinLab]

@AJPierik Are there other companies producing it?