Data from: A hybrid physics-deep learning framework for combinatorial de novo design of small-molecule binding proteins

Engineering small-molecule binding proteins de novo remains a
significant challenge as even advanced generative models struggle to model
the atom-level details of protein-ligand interactions with sufficient
accuracy. Higher experimental success rates have resulted from methods
that explicitly scaffold predefined binding interactions into helical
bundles. Here, we introduce a scaffolding strategy that generalizes to
alpha-beta architectures. By screening thousands of combinatorially
assembled protein-ligand interactions against diverse de novo backbones
with finely varied pocket geometries, the protocol allows for
high-fidelity accommodation of target interaction geometries. Our protocol
then integrates physics-based and deep learning methods for optimization
of interfacial interactions and sequence-structure compatibility,
considerably improving in silico design metrics. Applying this method to
two chemically similar steroids achieved a notable
experimental success rate (4/26 designs bind their targets), and NMR
structures of two designs are in good agreement with design models. Our
generalizable, atomically precise approach offers a robust framework for
small-molecule binder design, effectively eliminating the need for
high-throughput screening.