DiffPIE: Guiding Deep Generative Models to Explore Protein Conformations under External Interactions

Proteins play crucial roles in life processes such as catalysis, signal transduction, immune response, and molecular transport. Many of these functions are mediated by external interactions between a protein and other chemical species, including proteins, nucleic acids, and organic molecules. Furthermore, in therapeutic applications, protein functions can be influenced by interactions with unnatural chemical species, such as molecular organic linkers, inorganic materials, and polymers. Predicting protein conformational ensembles under the influence of external interactions remains a significant challenge both experimentally and computationally. While deep generative models have been developed to predict protein structural ensembles, their application to systems involving external interactions remains largely unexplored. In this work, we present a method that integrates a deep generative model with physics-based interaction modeling to predict protein conformations under external constraints. Without requiring any retraining or fine-tuning of the generative model, our approach can efficiently and accurately predict protein conformations covalently constrained by organic linkers, as well as protein conformations adsorbed on gold surfaces.