With this, we want to show that capturing family-wide mutation effects is essential to allow the training of accurate quantitative models of protein-protein interactions.

With out extensive dataset, we trained a neural network that could accurately predict binding from sequence and even enable the design of highly specific synthetic bZIPs.

We found that mutation effects are mostly additive in the context of close family members but more complex in distant regions of the sequence landscape indicating that the genetic architecture of protein function is more complex than previously thought.

In total, we quantitatively measured about 2M interactions of all possible single amino acid mutants of all 54 human leucine zipper domains with their wild-type interaction partners.

I am happy to finally share this preprint of my PhD project in @guillaumediss.bsky.social lab at the FMI in Basel.
We used ddPCA to map the genetic architecture of the entire human bZIP interaction network.

www.biorxiv.org/content/10.1...

Thanks to all our co-authors for the great collaboration!