Directed evolution is a powerful approach for enzyme engineering, but conventional workflows use only a fraction of the information generated during experimental screening. Typically, only top-perform...

Biological AI learns primarily from existing observations, but many questions cannot be answered from available data alone. Here we show that AI can instead acquire knowledge by acting directly on biological systems and learning from the consequences. We developed a closed-loop framework in which autonomous agents design protein variants, construct and characterize them in a robotic laboratory, learn from the resulting experimental feedback, and decide what experiments to perform next. We then allowed the system to operate continuously and without human intervention for approximately one month, during which multiple agents independently explored protein sequence space while learning from shared experimental experience. Applied to glycoside hydrolases, the agents discovered enzymes with substantially altered substrate specificity toward non-native sugars and progressively learned the structure of the underlying sequence-function landscape. The resulting experimental experience also revealed determinants of substrate specificity and protein expression that were not specified as learning objectives. These results demonstrate that AI can autonomously interact with biology over extended periods to acquire knowledge through experience, establishing a framework for biological discovery driven by continuous experimental interaction. ### Competing Interest Statement The authors have declared no competing interest. National Institute of General Medical Sciences, 5R01GM150929

Signaling Pathway Reconstruction Analysis Streamliner (SPRAS) - Reed-CompBio/spras

In the Big Data era, a change of paradigm in the use of molecular dynamics is required. Trajectories should be stored under FAIR (findable, accessible, interoperable and reusable) requirements to favo...

This work introduces the Runs N’ Poses dataset for benchmarking deep learning methods on the protein–ligand complex prediction task. It shows that current methods rely on memorization, challenging the...

Maria Castellanos Hugo MacDermott-Opeskin It’s been more than a month since the OpenADMET-ExpansionRx challenge wrapped up, but the conversation is just getting started. Launched on October 27, 2025...

We are excited to announce the early-stage release of our S. cerevisiae  structural interactome mapping project. Using AlphaFold3 (AF3), w...

Early proteins likely arose from a chemically limited set of amino acids available through prebiotic chemistry, raising a central question in molecular evolution: could such primitive compositions yie...

Mirdita Lab builds scalable bioinformatics methods.

This is the official web page for the James Fraser Lab at UCSF.

Protein Set Transformer (PST) framework for training protein-language-model-based genome language models. Inference is possible for viral genomes using our pretrained viral foundation model. - Anan...

A genome language model, Protein Set Transformer, trained on viral datasets, uncovers evolutionary rules of protein content and organization driving precise virus identification, host prediction, and ...

Madison AI for Proteins

Leveraging billions of years of evolution, scientists have trained protein language models (pLMs) to understand the sequence and structure space of proteins aiding in the design of more functional pro...

Multi-omic Pathway Analysis of Cells (MPAC), integrates multi-omic data for understanding cellular mechanisms. It predicts novel patient groups with distinct pathway profiles as well as identifying ke...