Philip Romero

Biophysics-based protein language models for protein engineering - Nature Methods Mutational effect transfer learning (METL) is a protein language model framework that unites machine learning and biophysical modeling. Transformer-based neural networks are pretrained on biophysical simulation data to capture fundamental relationships between protein sequence, structure and energetics.

AI + physics for protein engineering 🚀
Our collaboration with @anthonygitter.bsky.social is out in Nature Methods! We use synthetic data from molecular modeling to pretrain protein language models. Congrats to Sam Gelman and the team!
🔗 www.nature.com/articles/s41...

K2 Therapeutics

Our new company, K2 Therapeutics, is off to the races to develop new antibody drugs that target multipass membrane proteins. We are hiring so check us out! www.k2-tx.com/home#careers

Multiobjective learning and design of bacteriophage specificity To better understand and design proteins, it is crucial to consider the multifunctional landscapes on which all proteins exist. Proteins are often optimized for single functions during design and engi...

🎉 Congrats to Nate for his awesome preprint! We used deep learning to design phages with complex infectivity and specificity profiles. Big shifts in host targeting come from just a few mutations! Training on multifunctional data enables precise control over protein properties 🧬
tinyurl.com/yc4wtn8h

Congrats to Nathaniel and Sri for their exciting work teaching protein language models to generate beyond what evolution has explored. They introduce Reinforcement Learning from eXperimental Feedback (RLXF) to steer generation toward enhanced and non-natural functions
www.biorxiv.org/content/10.1...

GitHub - RomeroLab/omega: OMEGA code described in "Scalable and cost-efficient custom gene library assembly from oligopools." OMEGA code described in "Scalable and cost-efficient custom gene library assembly from oligopools." - RomeroLab/omega

Thank you for your interest in OMEGA! We’ve just posted the GitHub repository here: github.com/RomeroLab/om... The repo includes a Colab notebook to help you get started quickly. We welcome your feedback--if you run into issues or have suggestions to improve usability, please let us know!

Scalable and cost-efficient custom gene library assembly from oligopools Advances in metagenomics, deep learning, and generative protein design have enabled broad in silico exploration of sequence space, but experimental characterization is still constrained by the cost an...

🎉Congrats to Chase on her new preprint! She developed OMEGA--a simple method for assembling custom gene panels for as little as $1.50 per gene. Big step forward protein engineering and design!🧬
www.biorxiv.org/content/10.1...

Finally joining social media after all these years!!