Simple baselines rival protein language models in mutation-dense design tasks

Computational protein design demands generally applicable models that reliably predict or generate unmeasured variants with superior functional properties. Recent studies have proposed protein...

Science | AAAS

ProteinGuide: On-the-fly property guidance for protein sequence...

Sequence generative models are transforming protein engineering. However, no principled framework exists for conditioning these models on auxiliary information, such as experimental data, without...

Offline Materials Optimization with CliqueFlowmer

Recent advances in deep learning inspired neural network-based approaches to computational materials discovery (CMD). A plethora of problems in this field involve finding materials that optimize a...

Structural ontogeny of protein-protein interactions

Understanding how protein binding sites evolve interactions with other proteins could hold clues to targeting “undruggable” surfaces. We used synthetic coevolution to engineer new interactions...

Active Learning-Assisted Directed Evolution

Directed evolution (DE) is a powerful tool to optimize protein fitness for a specific application. However, DE can be inefficient when mutations exhibit non-additive, or epistatic, behavior. Here, we...