AI-powered chemical proteomics for drug discovery targeting orphan proteins

Abstract Genome-Wide Association Studies, whole-genome sequencing, and high-throughput techniques have generated vast amounts of diverse omics data. However, these sets of data have not yet been fully explored to improve the effectiveness and efficiency of drug discovery. Only 5-10% of druggable proteins are targeted by approved drugs. The undrugged orphan proteins are potential targets of yet-incurable diseases but whose endogenous and exogeneous ligands are unknown. Furthermore, there is a knowledge gap to link drug-target binding affinities to clinical outcomes. We know little if the target is activated or inhibited by the binder (i.e., function activity: agonist vs. antagonist). To date, few experimental and computational tools can determine genome-wide protein-ligand interactions (PLIs) for orphan proteins and ligand-induced functional activities (LIFAs) for both orphan proteins and majority of well-studied proteins. Existing machine learning techniques are mostly unsuccessful in predicting the ligand of orphan proteins due to an out-of-distribution (OOD) problem, i.e., they cannot reliably predict the function of an unseen protein if it is significantly different from the proteins in the training data in terms of sequence and structure. Commonly used computational tools for structure-based drug design, such as protein-ligand docking/scoring and Molecular Dynamics simulations, are neither scalable nor particularly reliable. As a result, we only have a limited capability of compound screening for orphan proteins. This proposal seeks to develop and experimentally validate innovative methods for predicting genome-wide PLIs and LIFAs to address aforementioned challenges. Building on our successful proof-of-concept studies and our close multidisciplinary collaborations between experimental and computational laboratories, we will develop a novel computational framework to model drug actions on a multi-scale by integrating big data from chemical and structural genomics and developing innovative deep learning algorithms. Specifically, we will develop a structure- enhanced deep learning framework to reliably and accurately predict protein-ligand interactions for orphan proteins on a genome-scale. We will integrate functional genomics with chemical genomics to predict ligand- induced functional activity. We will apply the methods developed to design and experimentally test inhibitors of orphan anti-cancer target AVIL and dual antagonists of dopamine receptors for opioid use disorder (OUD). The proposed research offers an innovative concept, methodology, and translational applications. Completing this research will fill a critical knowledge gap in understanding drug actions in a biological system and significantly impact drug discovery for complex diseases, many of which lack effective and safe treatments. The developed methodology and platform will not only immediately impact the NIH’s “Illuminating the Druggable Genome” Program but also has potentially broad applications in other areas of biomedical research.