Purpose

We observed that ESMFold2 generated designs with higher α-helical content than natural proteins. We wanted to bring their secondary-structure compositions closer to those found in nature, without retraining the model.

We added a penalty for helix-like geometry that increased β-sheet and loop content across five targets, with lower confidence scores on average. We're sharing the code, predicted structures, and optimization traces for researchers who want to control binder composition or investigate how structural preferences emerge during design. These experiments establish how steering changes computational predictions. Whether the resulting proteins fold and bind their intended targets remains to be tested.

View the notebook

In the future, we hope to host notebook pubs directly on our publishing platform. Until that’s possible, we’ll create stubs like this with key metadata like the DOI, author roles, citation information, and an external link to the pub itself.