Artificial Intelligence Steering Molecular Therapy in the Absence of Information on Target Structure and Regulation

Abstract

Protein associations are at the core of biological activity, and the drug-based disruption of dysfunctional associations poses a major challenge to targeted therapy. The problem becomes daunting when the structure and regulated modulation of the complex are unknown. To address the challenge, we leverage an artificial intelligence platform that learns from structural and epistructural data and infers regulation-susceptible regions that also generate interfacial tension between protein and water, thereby promoting protein associations. The input consists of sequence-derived 1D-features. The network is configured with evolutionarily coupled residues and taught to search for phosphorylation-modulated binding epitopes. The discovery platform is benchmarked against a PDB-derived testing set and validated against experimental data on a therapeutic disruptor designed according to the inferred epitope for a large deregulated complex known to be recruited in heart failure. Thus, dysfunctional "molecular brakes" of cardiac contractility get released through a therapeutic intervention guided by artificial intelligence.