A deep learning pipeline for controlling protein interactions

One of the LPDI's de novo protein binders (red) bound to the protein Bcl2 (blue) in complex with FDA-approved drug Venetoclax (beige) LPDI EPFL In 2023, scientists in the joint School of Engineering and School of Life Sciences Laboratory of Protein Design and Immunoengineering (LPDI), led by Bruno Correia, published a deep-learning pipeline for designing new proteins to interact with therapeutic targets. MaSIF can rapidly scan millions of proteins to identify optimal matches between molecules based on their chemical and geometric surface properties, enabling scientists to engineer novel protein-protein interactions that play key roles in cell regulation and therapeutics. A year and a half later, the team has reported an exciting advancement of this technology. They have used MaSIF to design novel protein binders to interact with known protein complexes involving small molecules like therapeutic drugs or hormones. Because these bound small molecules induce subtle changes in the surface properties ('neosurfaces') of these protein-drug complexes, they can act as'on' or'off' switches for the fine control of cellular functions like DNA transcription or protein degradation.