Protein design enables the creation of novel structures and functions beyond those found in nature, with recent progress accelerated by computational modeling and machine learning. However, many automated methods act as black boxes, limiting mechanistic insight. Here we highlight the continuing importance of rational protein design, defined as an approach rooted in physical principles, chemical intuition, and sequence-structure-function relationships. We outline three complementary strategies: backbone-first, sequence-first, and function-first, which provide interpretable design frameworks and enable robust scaffold generation, motif incorporation, and functional engineering. Looking forward, we argue that hybrid workflows combining rational principles with machine learning offer the most promising route to dynamic, explainable, and generalizable protein design.
Chubb et al. (2026) studied this question.