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Protein Language Models: Is Scaling Necessary? - This paper challenges the prevailing belief that scaling up protein language models (pLMs) is essential for better performance, proposing that careful data curation can achieve comparable results at a fraction of the cost. - The

Designing of thermostable proteins with a desired melting temperature 1. Breakthrough in designing thermostable proteins with a targeted melting temperature, boosting applications in industrial and pharmaceutical fields. 2. Developed a regression model using 17,312

A Computationally Designed Panel of Diverse and Selective Peroxygenases for Terpene Oxyfunctionalization 1. This study presents a computational approach to design 50 diverse and selective unspecific peroxygenase (UPO) variants for terpene oxyfunctionalization, all of which were

Using residue interaction networks to understand protein function and evolution and to engineer new proteins - This paper explores the application of residue interaction networks (RINs), which model proteins as graphs, with residues as nodes and interactions as edges. RINs

Have you ever wanted to design protein binders with ease? Today we present 𝑩𝒊𝒏𝒅𝑪𝒓𝒂𝒇𝒕, a user-friendly and open-source pipeline that allows to anyone to create protein binders de novo with high experimental success rates. Bruno Correia Sergey Ovchinnikov biorxiv.org/content/10.110…

Fantastic to see the chemistry Nobel be awarded for this essential work - especially when we use it in our group on a daily basis! #chemnobel #NobelPrizeChemistry

Key numbers in biology. 1/2

Computational Stabilization of a Non-heme Iron Enzyme Enables Efficient Evolution of New Function 🚀 New paper from David Baker and Jesse Zalatan!🚀 1/ This study demonstrates the use of the deep learning-based tool, ProteinMPNN, to computationally redesign Fe(II)/αKG

Improved protein binder design using beta-pairing targeted RFdiffusion 🚀 New preprint from David Baker!🚀 • A breakthrough in protein binder design: this study uses RFdiffusion conditioned to generate binders that form precise beta-strand pairings with polar protein targets,

We're building the first-of-a-kind Dynamic PDB to capture generalized protein motions (GPCRs, kinases, proteases, TNF, etc.) at scale for targets involved in immuno-oncology, and neuro-degeneration. Key highlights: 1. Predict functionally relevant conformational states for

Machine learning-guided directed evolution strategies exceeded or at least matched DE performance with the advantages becoming more pronounced as landscapes had fewer active variants and more local optima. Francesca-Zhoufan Li @ ICLR'25 Yisong Yue Jason Yang Kadina Johnston Frances Arnold

Come listen to my boss yap.

Experimental kcat and KM measurements for hundreds of naturally variants of adenylate kinase. - thermophilic enzymes are not slower than mesophilic! - general kcat/KM predictors are bad and easily beaten by models trained on this specific dataset!

My team at Deepmind (protein design) is hiring an experimentalist with enzyme expertise. Please RT and/or apply! I'm happy to answer any questions as well. boards.greenhouse.io/deepmind/jobs/…

Computational Design of Metallohydrolases 🚀 New preprint from David Baker!🚀 1. This study introduces RFam, a generative AI-based flow-matching model, which enables atomic-level scaffolding of enzyme active sites without predefined sequence positions or rotamer states,

We compared the calibration of various machine learning uncertainty estimation methods for protein engineering. No method excels across all scenarios, and uncertainty-based strategies for optimization often did not outperform methods without uncertainty.

This rate of validation suggests that antibodies should be considered non-specific until proven otherwise: 357 validated out of 1,124 tested. .

Self-supervised machine learning methods trained on natural sequences and structures are good at separating functional and nonfunctional variants but do not identify the best variants.

science.org/doi/full/10.11…

Wow, I think this is the start of mechanism-first protein design. RFdiffusion2 redesigns enzyme creation from the atom up—directly scaffolding active sites from transition state geometries, no rotamers or residue indices needed.