APPRAISE: ranking engineered proteins by target binding propensity using pair-wise, competitive structure modeling and physics-informed analysis

Deep learning-based methods for protein structure prediction, represented by AlphaFold2 [1] and RosettaFold [2] have achieved unprecedented accuracy. However, the power of these structure-prediction tools has not been fully harnessed to guide the engineering of protein-based therapeutics. For example, there is a gap between the ability to predict the structures of candidate protein molecules and the ability to assess which of those molecules are more likely to bind to a target receptor. Here we introduce Automated Pair-wise Peptide-Receptor binding model AnalysIs for Screening Engineered proteins (APPRAISE), a method for predicting the receptor binding propensity of engineered proteins. This method involves using an established structure-prediction tool to generate models of two engineered proteins competing for binding to a target protein. These structure models are then subjected to fast analysis (<1 CPU second per model) to generate a score that takes into account biophysical principles and geometrical constraints. As a proof-of-concept, we tested this tool on engineered Adeno-Associated Viral (AAV) vectors with surface displayed peptides. Using AlphaFold2-multimer [3] as the structure prediction engine, APPRAISE can accurately classify receptor-dependent vs. receptor-independent AAV capsids with a ROC-AUC of 0.87 in a set of 22 samples. When used to screen a library of 100 variants, APPRAISE correctly predicted a variant with a distinct sequence from previously known receptor binders to be a top receptor binder, which was confirmed by in vitro and in vivo experiments. Without further fine-tuning, APPRAISE can accurately rank other classes of engineered proteins, such as miniproteins binders and nanobodies, that bind to therapeutic receptors. With high accuracy, generalizability, and interpretability, the APPRAISE method would expand the utilities of current structural prediction capabilities and accelerate protein engineering for biomedical applications.