Zero-shot design of drug-binding proteins via neural selection-expansion

Abstract

Computational design of molecular recognition remains challenging despite advances in deep learning1-3. The design of proteins that bind to small molecules has been particularly difficult because it requires simultaneous optimization of protein sequence, protein structure, and ligand conformation1-7. Despite their promise, current deep-learning algorithms have struggled to navigate this landscape, precluding the zero- or few-shot design of binders. Here we show that the combination of two neural networks in an iterative design algorithm can create small-molecule binding proteins from scratch with high accuracy. To optimize a design in the joint distribution of sequence, structure, and ligand conformation, we use a pair of neural networks that were trained on reciprocal tasks. We train and use a graph neural network, LASErMPNN, to design protein sequence given protein-ligand co-structure, and we use RoseTTAFold-All Atom8 (RFAA) to predict protein-ligand co-structure given protein sequence. We iteratively apply these two networks to design proteins that bind the drug, exatecan, a topoisomerase I inhibitor that is prone to inactivation by hydrolysis9. Each of four experimentally tested designs bound the drug, with the lowest dissociation constant (Kd) near 100 nM. The hit rate and highest affinity design each surpassed the current state-of-the-art method by 5- and 70-fold, respectively. We further show that LASErMPNN can improve upon its own designs in a manner resembling chain-of-thought reasoning. Without experimental input, LASErMPNN suggested two mutations that increased affinity by over two orders of magnitude (Kd = 1.2 {+/-} 0.2 nM). Designs were selective, structurally accurate, and achieved their intended purpose to protect the drug from hydrolysis. Our work describes a recipe for using neural networks to automate the design of high affinity small-molecule binding proteins, which should have wide application in the creation of novel drug-delivery vehicles, antidotes, sensors, and enzymes.