My time in @martinsteinegger.bsky.social's group is ending, but I’m staying in Korea to build a lab at Sungkyunkwan University School of Medicine. If you or someone you know is interested in molecular machine learning and open-source bioinformatics, please reach out. I am hiring! mirdita.org

Mirdita Lab - Laboratory for Computational Biology & Molecular Machine Learning

Mirdita Lab builds scalable bioinformatics methods.

mirdita.org

Just coincidentally found GenBank Release 84.0 from 1994 in the neighboring lab. Anyone out there with an even older version?

In case you missed our ML for proteins seminar on CHEAP compression for protein embeddings back in October, here it is -- thanks @megthescientist.bsky.social for doing so much for the MLxProteins community 🫶

Meg T (she/her/hers)@megthescientist.bsky.social · 2y ago

&& superstar @amyxlu.bsky.social (and prev. Co-organizer of @ml4proteins.bsky.social) CHEAP talk online now! youtu.be/7XnROkjo5Vg?...

•introduced “zero shot prediction” as a question of guessing a bioassay’s outcome by likelihoods of pLMs •commented on biases in evolutionary signals from Tree of life used to train pLMs (a favorite paper I read in 2024: shorturl.at/fbC7g)

Thanks @workshopmlsb.bsky.social for letting us share our work! 🔗📄 bit.ly/plaid-proteins

Generating All-Atom Protein Structure from Sequence-Only Training Data

Generative models for protein design are gaining interest for their potential scientific impact. However, protein function is mediated by many modalities, and simultaneously generating multiple modali...

bit.ly

MLSB (in San Diego + Copenhagen)@workshopmlsb.bsky.social · 2y ago

We trained a model to co-generate protein sequence and structure by working in the ESMFold latent space, which encodes both. PLAID only requires sequences for training but generates all-atom structures! Really proud of @amyxlu.bsky.social 's effort leading this project end-to-end!

Amy Lu@amyxlu.bsky.social · 2y ago

1/🧬 Excited to share PLAID, our new approach for co-generating sequence and all-atom protein structures by sampling from the latent space of ESMFold. This requires only sequences during training, which unlocks more data and annotations: bit.ly/plaid-proteins 🧵

1/🧬 Excited to share PLAID, our new approach for co-generating sequence and all-atom protein structures by sampling from the latent space of ESMFold. This requires only sequences during training, which unlocks more data and annotations: bit.ly/plaid-proteins 🧵