Prospecting the protein design landscape.

Abstract

Generative AI has driven remarkable breakthroughs in protein design, enabling the rapid, computationally guided creation of high-affinity binders against diverse targets. While remarkable experimental success has been demonstrated, the confidence metrics used to filter and evaluate designs remain optimized for static protein interfaces and can fail when applied to underrepresented or conformationally complex targets. In this review, we outline the current landscape of deep learning-driven protein design pipelines, discuss tailored applications in peptide, small molecule, binder, vaccine, and antibody design, and argue that the integration of ensemble-based methods represents a promising avenue for improving design success rates. Beyond single target binder design, we further highlight emerging strategies that expand the functional scope of designed proteins, including fold-switching scaffolds and molecular glues realized through engineered cyclic peptides, which enable context-dependent control of protein-protein interaction networks. Together, these advances position de novo protein design as a broadly applicable technology platform at the intersection of structural biology, biophysics, and molecular medicine.