De novo engineering of protein interactions: Retrospective and current advances

New deep learning-based methods for modeling and generation of protein structures have opened a new chapter in the field of protein design, transforming many previously unattainable challenges into routine tasks. Protein-binder design, an important and challenging task in protein engineering, has also experienced significant progress, promising to provide solutions to many therapeutic and bioengineering problems. Novel protein folds of tailored surface complementarity to their target can be generated and stabilized by amino acid sequences with unprecedentedly high experimental success rates. These advancements can be largely attributed to the power of the new structure prediction models, such as AlphaFold, as well as deep generative models that learn data distributions and allow sampling of new molecules conditioned on function-related features. In this review, we will discuss the development of binder design approaches, focusing on the state-of-the-art methods and their applications as well as new challenges.