In silico fragment-based discovery of CIB1-directed anti-tumor agents by FRASE-bot.

Chemical probes are an indispensable tool for translating biological discoveries into new therapies, though are increasingly difficult to identify since novel therapeutic targets are often hard-to-drug proteins. We introduce FRASE-based hit-finding robot (FRASE-bot), to expedite drug discovery for unconventional therapeutic targets. FRASE-bot mines available 3D structures of ligand-protein complexes to create a database of FRAgments in Structural Environments (FRASE). The FRASE database can be screened to identify structural environments similar to those in the target protein and seed the target structure with relevant ligand fragments. A neural network model is used to retain fragments with the highest likelihood of being native binders. The seeded fragments then inform ultra-large-scale virtual screening of commercially available compounds. We apply FRASE-bot to identify ligands for Calcium and Integrin Binding protein 1 (CIB1), a promising drug target implicated in triple negative breast cancer. FRASE-based virtual screening identifies a small-molecule CIB1 ligand (with binding confirmed in a TR-FRET assay) showing specific cell-killing activity in CIB1-dependent cancer cells, but not in CIB1-depletion-insensitive cells.

Machine Learning-driven Fragment-based Discovery of CIB1-directed Anti-Tumor Agents by FRASE-bot.

Chemical probes are an indispensable tool for translating biological discoveries into new therapies, though are increasingly difficult to identify. Novel therapeutic targets are often hard-to-drug proteins, such as messengers or transcription factors. Computational strategies arise as a promising solution to expedite drug discovery for unconventional therapeutic targets. FRASE-bot exploits big data and machine learning (ML) to distill 3D information relevant to the target protein from thousands of protein-ligand complexes to seed it with ligand fragments. The seeded fragments can then inform either (i) de novo design of 3D ligand structures or (ii) ultra-large-scale virtual screening of commercially available compounds. Here, FRASE-bot was applied to identify ligands for Calcium and Integrin Binding protein 1 (CIB1), a promising but ligand-orphan drug target implicated in triple negative breast cancer. The signaling function of CIB1 relies on protein-protein interactions and its structure does not feature any natural ligand-binding pocket. FRASE-based virtual screening identified the first small-molecule CIB1 ligand (with binding confirmed in a TR-FRET assay) showing specific cell-killing activity in CIB1-dependent cancer cells, but not in CIB1-depleted cells.

Discovery and Structural Basis of the Selectivity of Potent Cyclic Peptide Inhibitors of MAGE-A4.

MAGE proteins are cancer testis antigens (CTAs) that are characterized by highly conserved MAGE homology domains (MHDs) and are increasingly being found to play pivotal roles in promoting aggressive cancer types. MAGE-A4, in particular, increases DNA damage tolerance and chemoresistance in a variety of cancers by stabilizing the E3-ligase RAD18 and promoting trans-lesion synthesis (TLS). Inhibition of the MAGE-A4:RAD18 axis could sensitize cancer cells to chemotherapeutics like platinating agents. We use an mRNA display of thioether cyclized peptides to identify a series of potent and highly selective macrocyclic inhibitors of the MAGE-A4:RAD18 interaction. Co-crystal structure indicates that these inhibitors bind in a pocket that is conserved across MHDs but take advantage of A4-specific residues to achieve high isoform selectivity. Cumulatively, our data represent the first reported inhibitor of the MAGE-A4:RAD18 interaction and establish biochemical tools and structural insights for the future development of MAGE-A4-targeted cellular probes.