Johannes Rebelein

Antonio J. Pierik @AJPierik · Nov 13, 2025

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
pnas.org/doi/10.1073/pn…

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Ribbe Hu Labs Verified account @HuRibbe · Oct 17, 2025

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! recruit.ap.uci.edu/JPF09887

Please apply and/or share this post!

@UCIBioSci; @UCIrvine

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Biology+AI Daily @BiologyAIDaily · Oct 10, 2025

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: biorxiv.org/content/10.110…
#PymolFold #ProteinStructurePrediction #StructuralBiology #DrugDesign #PyMOL #API #ComputationalBiology

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Narayan Lab @ UMich @NarayanLab · Oct 1, 2025

CATNIP for the win! Read our newest work with the Gomes group- doi.org/10.1038/s41586…

@Apatoneh @gabepgomes @daniil_boiko @AlisonNarayanUM

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