Andrea Pasquadibisceglie

The official home of the Python Programming Language

TLDR; The PSF has made the decision to put our community and our shared diversity, equity, and inclusion values ahead of seeking $1.5M in new revenue. Please read and share. pyfound.blogspot.com/2025/10/NSF-...
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Join our lab dibernardo.tigem.it for the #ERC-funded project DIMERCIRCUITS at @tigem.bsky.social. We are building synthetic gene circuits to power next-generation gene and cell therapies. We are looking for candidates with a PhD in #SyntheticBiology or related. Apply by email: dibernardo@tigem.it

🚀 Launch your career in advanced gene and cell therapies! 🚀

RAREFIND #MSCA-COFUND #PhD Programme opens its call for applications!

👉Know more and apply now: www.rarefind-cofund.eu

#RAREFINDPhD

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...

We are hiring a Bioinformatician expert in NGS for our Bioinformatics Core @tigem.bsky.social please share with anyone that may be interested.

Very nice presentation of @eriklindahl.bsky.social about his work to integrate AI, MD and experiments. 🤩 #EBSA2025

Are you curious about ligand selectivity and modulation mechanisms of the voltage-gated potassium channel Kv7 family? Stop by Poster #39!

#EBSA2025 #IonChannels #StructuralBiology #ComputationalBiophysics

Excited to be at #EBSA2025!
Today I'll present my postdoc project carried out under the supervision of @delemottelab.bsky.social and with the collaboration of Sara Liin and her amazing team!

🌍 Attending #ASGCT2025 in #NewOrleans?
Don’t miss this Scientific Symposium hosted by the Coalition of International Gene Therapy Societies:

📌 Clinical Trials Around the Globe
🗓️ [Today, May 16th]
🕗 8:00 AM – 9:45 AM
📍 Room 293–296

#GeneTherapy #ASGCT #AAV #Retina #LiverGeneTherapy #RareDisease

Gene therapy in children with AIPL1-associated severe retinal dystrophy: an open-label, first-in-human interventional study Our findings indicate that young children with AIPL1-related retinal dystrophy benefited substantially from subretinal administration of rAAV8.hRKp.AIPL1, with improved visual acuity and functional vi...

A new paper hitting the press: Gene therapy for AIPL-1 retinal dystrophy. Almost all kids with this can only perceive light, at best. Getting early will help with vision also neurodevelopment and psychosocial aspects. 🧬 ⭐ 👁

www.thelancet.com/journals/lan...

Structures of protein folding intermediates on the ribosome The ribosome biases the conformations sampled by nascent polypeptide chains along folding pathways towards biologically active states. A hallmark of the co-translational folding (coTF) of many protein...

Preprint! All-atom structures of 2 folding intermediates on the ribosome, along parallel pathways & conserved across Ig domains, by 19F NMR & MD

Co-led by @julianstreit.bsky.social, & thanks twlodarski.bsky.social, Alki, Lisa & John Christodoulou!

#nmrchat #compbio
www.biorxiv.org/content/10.1...

BoltzDesign1: Inverting All-Atom Structure Prediction Model for Generalized Biomolecular Binder Design by @yehlincho.bsky.social @martinpacesa.bsky.social @sokrypton.org 🧶🧬

www.biorxiv.org/content/10.1...

Fig 3d Scatterplots compare xT-Fold predictions (x axis) to other models (y axis), color coded by perplexity (green for high, purple for low).

Yet another PLM, this one with 100B params, that fails to outperform AF2 (93M params) when MSAs are provided www.nature.com/articles/s41...

We are recruiting a colleague to our division at Chalmers in Data-Driven Life Science (broadly defined), a competitive starting package is offered and you get to be part of a support, yet young and ambitious research environment. Apply here: www.chalmers.se/en/about-cha...

Honored to be part of @tigem.bsky.social and Fondazione Telethon

Unsupervised Learning of Progress Coordinates during Weighted Ensemble Simulations: Application to NTL9 Protein Folding A major challenge for many rare-event sampling strategies is the identification of progress coordinates that capture the slowest relevant motions. Machine-learning methods that can identify progress coordinates in an unsupervised manner have therefore been of great interest to the simulation community. Here, we developed a general method for identifying progress coordinates “on-the-fly” during weighted ensemble (WE) rare-event sampling via deep learning (DL) of outliers among sampled conformations. Our method identifies outliers in a latent space model of the system’s sampled conformations that is periodically trained using a convolutional variational autoencoder. As a proof of principle, we applied our DL-enhanced WE method to simulate the NTL9 protein folding process. To enable rapid tests, our simulations propagated discrete-state synthetic molecular dynamics trajectories using a generative, fine-grained Markov state model. Results revealed that our on-the-fly DL of outliers enhanced the efficiency of WE by >3-fold in estimating the folding rate constant. Our efforts are a significant step forward in the unsupervised learning of slow coordinates during rare event sampling.

Leung et al. used deep autoencoders with outlier detection to guide weighted ensemble simulations of NTL9 folding, achieving up to threefold better efficiency than standard methods. pubs.acs.org/doi/full/10....

The Protein Design Archive (PDA): insights from 40 years of protein design - Nature Biotechnology Nature Biotechnology - The Protein Design Archive (PDA): insights from 40 years of protein design

Chronowska et al. introduce the Protein Design Archive, a curated database of over 1,500 de novo designs, revealing rapid growth from rational to deep learning–based methods. Their website offers key metrics and filtering tools for guiding future designs. www.nature.com/articles/s41...

In Brief: A potential first-in-class treatment using @boehringerglobal.bsky.social's lentivirus-based CFTR gene addition therapy for cystic fibrosis begins clinical trials www.nature.com/articles/s41...
rdcu.be/eecLO

🚨 The latest study from #NatureCommunications, led by Pasquale Piccolo, systematically evaluated AAV-mediated liver transduction across different fibrosis models.

🚀 PhD Opportunity – NC-mTORC1 & Genetic Diseases!

Join @tigem.bsky.social & Univ. of Naples to explore Non-Canonical mTORC1 signaling in mTORopathies like BHD & TSC!

🚀 Our mission 🧬

Our mission is to push scientific boundaries through cutting-edge research, innovative therapies, and training the next generation of biomedical experts.
Learn more: www.tigem.it
✨ #Genetics #RareDiseases #MedicalResearch

Figure 2: Network layout of the rMD informed autoencoder. The basic encoder compresses the flattened Cartesian coordinates of protein structures from MD simulations into a low-dimensional latent space (magenta dot) via fully connected network layers gradually shrinking in size. The decoder then reconstructs the original Cartesian representation from the LS using expanding layers. The blue CRBN structures represent inputs from simulations, while the red structures are their network-generated outputs. The autoencoder is trained using the ”predLoss” layer, minimizing the Loss2 function, which calculates the average RMSD between inputs and outputs. The 5 different point clouds show 5 different LS point distributions (orange-training set, blue-validation set) after training the basic network, initializing it with different random seeds. The informed autoencoder introduces an additional loss layer, ”latentLoss” and a loss function Loss1. The input for Loss1 are the LS coordinates and the target are the CV coordinates (purple ”colVars” box). By simultaneously optimizing Loss1 and Loss2, the trained network will compress the original trajectory frames into a unique latent space where LS coordinates have an approximate one-to-one correspondence with the CV coordinates as shown in Fig. 3.

Collective variables from MD simulations can be linked to the learned latent dimensions of an autoencoder for generative modeling of conformational interconversion trajectories www.biorxiv.org/content/10.1...

The BioEmu-1 model and inference code are now public under MIT license!!!

Please go ahead, play with it and let us know if there are issues.

github.com/microsoft/bi...

This year's version of GROMACS is out!

If you want to hear about the new features and performance improvements join our #webinar on 18 February 2025

Registration ➡️ bit.ly/40Ebbme

#moleculardynamics

Predicting absolute protein folding stability using generative models

@mcagiada.bsky.social @sokrypton.org & I used ESM-IF to predict ∆G for folding & conformational change

Paper, code and colab
📜 dx.doi.org/10.1002/pro....
💾 github.com/KULL-Centre/...
👩‍💻 colab.research.google.com/github/KULL-...

I will miss you all! (a good napolitan pizza is waiting for you in Pozzuoli now 😉)

Thanks!

Welcome to TIGEM Telethon Institute of Genetics and Medicine

At TIGEM, we are looking for a motivated student/young researcher to start a new project on hyperactive enzyme design using deep-learning and physics-based methods! If you are interested, please get in touch with us tigem.it/newsroom/car...! Thank you for sharing it! #compchem #compbio

I’m thrilled to announce that, in February next year, I’ll be starting a new role as a Staff Scientist at TIGEM in Italy! I’m truly grateful to my fantastic supervisor @delemottelab.bsky.social and the MBS group for an incredible two years in Stockholm! Tack så mycket!

David Baker chats with Rosetta folks

Nobel Laureates in Chemistry David Baker, Demis Hassabis, and John Jumper

David Baker visits all the Rosetta folks after his Nobel lecture. And photo of all three Chemistry laureates - David Baker, Demis Hassabis, and John Jumper. An honor to be here in Stockholm to see this.