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Pablo Arantes @pabloarantes.bsky.social · 1d

Your protein moves, but when exactly? ⏱️
Meet eRMSF, our new Python tool that tracks fluctuations over time!

Traditional RMSF shows “how much.”
eRMSF shows “when.” 🔥

Preprint 🔗 doi.org/10.26434/che...
GitHub github.com/pablo-arante...
Try it on Colab → colab.research.google.com/github/pablo...

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Reposted by Pablo Arantes

Martin Pacesa @martinpacesa.bsky.social · Aug 27

Exciting to see our protein binder design pipeline BindCraft published in its final form in @Nature ! This has been an amazing collaborative effort with Lennart, Christian, @sokrypton.org, Bruno and many other amazing lab members and collaborators.

www.nature.com/articles/s41...

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Pablo Arantes @pabloarantes.bsky.social · Aug 21

Thank you, @olexandr.bsky.social! Yes, AIMNet2 is included in the notebook. Users can choose between several models: TorchANI, AIMNet2, and MACE-OFF. 😉

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Pablo Arantes @pabloarantes.bsky.social · Aug 20

Thank you my friend! 🥰

Reposted by Pablo Arantes

Marcelo D. Polêto @mdpoleto.bsky.social · Aug 20

When Pablo told me about this idea, I could see an immediate impact on drug discovery endeavors.

People can easily refine parameters of multiple dihedral torsion for hundreds of molecules and employ those in FEP simulations.

10/10 recommend 👍

Pablo Arantes @pabloarantes.bsky.social · Aug 20

Exciting news for molecular research! Introducing ParametrizANI: a fast, accurate, & free tool for small molecule parametrization! We're democratizing research, enabling teams of all sizes to perform dihedral parametrization with DFT-level accuracy. 1/8
doi.org/10.26434/che...

ParametrizANI: Fast, Accurate, and Free Parametrization for Small Molecules

In molecular studies, the accurate parametrization of small molecules stands as an essential yet growing demand. Addressing this, we introduce ParametrizANI, a tool crafted explicitly for establishing...

doi.org

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Reposted by Pablo Arantes

Giulia Palermo @giuliapalermo.bsky.social · Aug 20

New from our lab!
🚀 ParametrizANI - a #NeuralNetworks tool for molecular parametrization.
Predicts potential energy surfaces with near-DFT/CC accuracy, at a fraction of the computational cost!
#AI #QuantumChemistry #CompChem #ML 🤖
chemrxiv.org/engage/chemr...
Try it:
github.com/palermolab/P...

GitHub - palermolab/ParametrizANI: ParametrizANI - Fast, Accurate and Free Dihedral Parametrization in the Cloud with TorchANI

ParametrizANI - Fast, Accurate and Free Dihedral Parametrization in the Cloud with TorchANI - palermolab/ParametrizANI

github.com

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Pablo Arantes @pabloarantes.bsky.social · Aug 20

Huge thanks to our co-authors Souvik Sinha & @giuliapalermo.bsky.social ! And deep gratitude to the @openmm.org, TorchANI team, Rotational Profiler developers, the "Making it Rain" team (@conradopedebos.bsky.social , @mdpoleto.bsky.social and Rodrigo Ligabue Braun) for their inspiration! 8/8

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media.tenor.com

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Pablo Arantes @pabloarantes.bsky.social · Aug 20

Ready to try it? ✨ All our Colab notebooks are freely & publicly available on @github.com! Dive in to enhance your molecular studies. We're committed to fostering deeper insights & improved methodologies in the scientific community. Find ParametrizANI here: 7/8 github.com/palermolab/P...

GitHub - palermolab/ParametrizANI: ParametrizANI - Fast, Accurate and Free Dihedral Parametrization in the Cloud with TorchANI

ParametrizANI - Fast, Accurate and Free Dihedral Parametrization in the Cloud with TorchANI - palermolab/ParametrizANI

github.com

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Pablo Arantes @pabloarantes.bsky.social · Aug 20

A game-changer! ParametrizANI is perfect for drug discovery, helping evaluate candidates with high accuracy & speed. It's also an excellent resource for education, offering hands-on experience without complex setups, & allows professional customization. #DrugDiscovery 6/8

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Pablo Arantes @pabloarantes.bsky.social · Aug 20

Our user-friendly Jupyter notebooks provide a complete workflow for dihedral parametrization, from SMILES strings to optimized force field parameters. We support both GAFF & @openforcefield.org force fields, ensuring compatibility for your simulations. #ForceFields 5/8

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Pablo Arantes @pabloarantes.bsky.social · Aug 20

Accessibility is key! ParametrizANI runs on
@googlecolab.bsky.social, a "click-and-go" experience with free access to CPUs. No heavy parallel processing needed! We've parametrized molecules in less than 5 minutes on CPU. A big step for accurate, efficient small molecule parametrization! 4/8

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Pablo Arantes @pabloarantes.bsky.social · Aug 20

A core component: our Python version of the Rotational Profiler code. This analytical algorithm efficiently computes classical torsional dihedral parameters by fitting empirical energy profiles to a reference curve. #ComputationalChemistry #Algorithms #ForceFields 3/8

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Pablo Arantes @pabloarantes.bsky.social · Aug 20

How? ParametrizANI uses the robust PyTorch-based TorchANI program & ANI deep learning models (ANI-1x, ANI-2x). This predicts potential energy surfaces with near-DFT or coupled-cluster accuracy, at a fraction of the computational cost! #DeepLearning #TorchANI #AIforScience 2/8

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Pablo Arantes @pabloarantes.bsky.social · Aug 20

Exciting news for molecular research! Introducing ParametrizANI: a fast, accurate, & free tool for small molecule parametrization! We're democratizing research, enabling teams of all sizes to perform dihedral parametrization with DFT-level accuracy. 1/8
doi.org/10.26434/che...

ParametrizANI: Fast, Accurate, and Free Parametrization for Small Molecules

In molecular studies, the accurate parametrization of small molecules stands as an essential yet growing demand. Addressing this, we introduce ParametrizANI, a tool crafted explicitly for establishing...

doi.org

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Reposted by Pablo Arantes

Martin Pacesa @martinpacesa.bsky.social · Jun 30

We have written up a tutorial on how to run BindCraft, how to prepare your input PDB, how to select hotspots, and various other tips and tricks to get the most out of binder design!

github.com/martinpacesa...

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Reposted by Pablo Arantes

Stand Up for Science! @standupforscience.bsky.social · Jun 9

🚨BREAKING: 300+ NIH employees call out the harm of censorship & politicized science in scathing email to Bhattacharya, demanding an end to political interference, a lift on funding freezes, & rehiring of fired staff whose work saves lives.

This is historic - insiders are blowing the whistle.
🧵(1/5)

Front-page-style graphic titled “BREAKING NEWS” with photos of RFK Jr. and Dr. Bhattacharya in front of a government hearing chamber. Text reads: “NIH Scientists Sound the Alarm as Health Research Faces Historic Threat” and “NIH Employees Send Trump Cronies Scathing Wake-Up Call.”

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Pablo Arantes @pabloarantes.bsky.social · Jun 9

Tem vaga ainda?

Reposted by Pablo Arantes

Yehlin Cho @yehlincho.bsky.social · Jun 3

🚀 Excited to release BoltzDesign1!

✨ Now with LogMD-based trajectory visualization.
🔗 Demo: rcsb.ai/ff9c2b1ee8
Feedback & collabs welcome! 🙌

🔗: GitHub: github.com/yehlincho/Bo...
🔗: Colab: colab.research.google.com/github/yehli...
@sokrypton.org @martinpacesa.bsky.social

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Pablo Arantes @pabloarantes.bsky.social · Apr 8

This is the kind of message that makes it all worth it.

When someone takes a moment to say thank you, it reminds me why we keep pushing forward—sharing tools, writing posts, and trying to make science more open and accessible. Grateful for the kind words!

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Pablo Arantes @pabloarantes.bsky.social · Apr 2

Our latest paper just dropped in PNAS! 🎉

Turns out, CRISPR-associated transposons don’t just jump—they dance their way through DNA! 🕺🔬

Exciting times for genome engineering!
🧬 Read more in PNAS: www.pnas.org/doi/10.1073/...

#CRISPR #GeneEditing #PNAS #MDsimulations #CompChem

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Pablo Arantes @pabloarantes.bsky.social · Apr 2

I have included side-chain reconstruction using HPacker in the BioEmu Notebook. Thank you to @martinsteinegger.bsky.social whose notebook provided inspiration for incorporating the cell to add side-chain reconstruction.

🔗 Try it on Google Colab: colab.research.google.com/github/pablo...

Pablo Arantes @pabloarantes.bsky.social · Mar 20

I’ve just updated the BioEmu notebook to include the powerful LogMD. Now, you can generate equilibrium ensembles and explore the full ensemble directly in the notebook.

A huge thanks to Alexander Mathiasen for the support! 🙌

🔗 Try it on Google Colab: lnkd.in/gcuqd-fT

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Pablo Arantes @pabloarantes.bsky.social · Mar 24

And what do you think would be faster to run it on a local machine with RTX 4090 or keep on the colab with the A100?

I don't have access to RTX 4090 and I'm only using A100 on Google Colab, so I suggested to check the original paper: doi.org/10.1101/2024...

Scalable emulation of protein equilibrium ensembles with generative deep learning

Following the sequence and structure revolutions, predicting the dynamical mechanisms of proteins that implement biological function remains an outstanding scientific challenge. Several experimental t...

doi.org

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Pablo Arantes @pabloarantes.bsky.social · Mar 24

However, do you think it would be better to do 5000 sample compared to 1000 sample?

I ran some tests and compared an ensemble of 1000 samples with each containing 5000 frames. I did not observe any differences between them except for the number of frames.

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Pablo Arantes @pabloarantes.bsky.social · Mar 24

Regarding your ideas, I loved the idea to compare an ensemble of WT and mutated protein using BioEmu, I'm working on a notebook to do that.

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Pablo Arantes @pabloarantes.bsky.social · Mar 24

Hi Tareq, Thank you for all information regarding BioEmu. As I clearly described on my firt post, I did not develop the BioEmu, I just performed the implementation using Google Colab.

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