Using AI machine learning to map hidden molecular interactions in bacteria
A new study from Oregon Health & Science University has uncovered how small molecules within bacteria interact with proteins, revealing a network of molecular connections that could improve drug discovery and cancer research.
The multi-disciplinary research team, led by Andrew Emili, Ph.D., professor of systems biology and oncological sciences in the OHSU School of Medicine and OHSU Knight Cancer Institute, alongside Dima Kozakov, Ph.D., professor at Stony Brook University, studied Escherichia coli, or E. coli, a simple model organism, to map how metabolites—small molecules essential for life—interact with key proteins such as enzymes and transcription factors. These interactions control important processes such as cell growth, division and gene expression, but how exactly they influence protein function is not always clear.
The team used advanced tools like chemo-proteomics—developed in the Emili lab—and artificial intelligence-driven structural modeling—developed by the Kozakov lab—to identify nearly 300 ligands, which are molecules, and their binding sites on bacterial proteins critical for the bacteria's survival.
"In cancer cells, metabolism is remarkably changed," Emili said. "People don't necessarily think about the molecular consequences of that dysregulation. Our work in E. coli shows that small molecules interact dynamically with many proteins inside cells and change their behavior. In cancer cells, these interactions could be major drivers of tumor growth, proliferation and potentially even immune evasion."
This realization opens new possibilities for targeting cancer. Small molecules might influence how transcription factors are activated, potentially altering gene expression programs and reshaping the cancer cell's biology. By understanding these interactions, the researchers hope to identify vulnerabilities in cancer cells that can be exploited for treatment.
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