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.

Combining pharmacophore models derived from DNA-encoded chemical libraries with structure-based exploration to predict Tankyrase 1 inhibitors.

DNA-encoded chemical libraries (DECLs) interrogate the interactions of a target of interest with vast numbers of molecules. DECLs hence provide abundant information about the chemical ligand space for therapeutic targets, and there is considerable interest in methods for exploiting DECL screening data to predict novel ligands. Here we introduce one such approach and demonstrate its feasibility using the cancer-related poly-(ADP-ribose)transferase tankyrase 1 (TNKS1) as a model target. First, DECL affinity selections resulted in structurally diverse TNKS1 inhibitors with high potency including compound 2 with an IC50 value of 0.8 nM. Additionally, TNKS1 hits from four DECLs were translated into pharmacophore models, which were exploited in combination with docking-based screening to identify TNKS1 ligand candidates in databases of commercially available compounds. This computational strategy afforded TNKS1 inhibitors that are outside the chemical space covered by the DECLs and yielded the drug-like lead compound 12 with an IC50 value of 22 nM. The study further provided insights in the reliability of screening data and the effect of library design on hit compounds. In particular, the study revealed that while in general DECL screening data are in good agreement with off-DNA ligand binding, unpredictable interactions of the DNA-attachment linker with the target protein contribute to the noise in the affinity selection data.