NGS data from: Deploying synthetic coevolution and machine learning to engineer protein-protein interactions

Fine-tuning of protein-protein interactions occurs naturally through
coevolution, but this process is difficult to recapitulate in the
laboratory. We describe a synthetic platform for protein-protein
coevolution that can isolate matched pairs of interacting muteins from
complex libraries. This large dataset of coevolved complexes 
drove a systems-level analysis of molecular recognition between Z
domain-affibody pairs spanning a wide range of structures, affinities,
cross-reactivities, and orthogonalities, and captured a broad spectrum of
coevolutionary networks. Furthermore, we harnessed pre-trained protein
language models to expand, in silico, the amino acid diversity of our
coevolution screen, predicting remodeled interfaces beyond the reach of
the experimental library. The integration of these approaches provides a
means of generating protein complexes with diverse molecular recognition
properties as tools for biotechnology and synthetic biology.