Computational Design of Protein Interfaces For Therapeutic Development

Abstract

Recent advances in deep learning have revolutionized the de novo design of protein interfaces, offering unprecedented control over structural features of binders and epitope targeting. Unlike conventional antibodies, de novo protein binders offer superior stability, ease of production, and cost-effectiveness, making them ideal for diagnostic and therapeutic applications. In Chapter 1, we address the challenge of targeting flexible bioactive helical peptides. We also introduce a novel approach to improving affinity of designs by using generative tools for dock refinement. We further demonstrate the utility of these binders as robust tools for hormone detection in biosensors and mass spectrometry. In Chapter 2, we present a generalizable platform to solubilize G-protein coupled receptors (GPCRs) by transplanting their orthosteric binding sites onto stable designed scaffolds. We demonstrate that these soluble analogs enable high-throughput screening and the discovery of novel antagonists for complex membrane targets. Together, these chapters establish design principles and computational workflows that broaden the scope of de novo binder design for drug discovery.