Machine Learning-driven Fragment-based Discovery of CIB1-directed Anti-Tumor Agents by FRASE-bot.
Dmitri B KireevYi AnJiwoong LimMarta GlavatskikhXiaowen WangJacqueline NorrisPaul HardyTina LeisnerKenneth H PearcePublished in: Research square (2023)
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.
Keyphrases
- machine learning
- big data
- small molecule
- binding protein
- artificial intelligence
- protein protein
- drug discovery
- transcription factor
- induced apoptosis
- high throughput
- single molecule
- deep learning
- stem cells
- healthcare
- living cells
- cell cycle arrest
- single cell
- cell therapy
- oxidative stress
- health information
- mesenchymal stem cells
- endoplasmic reticulum stress
- dna binding
- mass spectrometry
- adverse drug