Computational Modelling of Tunicamycin C Interaction with Potential Protein Targets: Perspectives from Inverse...

Area of Science:

• Biochemistry and Molecular Biology
• Cancer Research
• Computational Biology

Background:

• Protein glycosylation is vital in cancer, affecting cell signaling, immune response, and metastasis.
• Targeting glycosylation offers therapeutic potential by disrupting cancer progression mechanisms.
• Tunicamycin C, a glycosylation inhibitor, shows promise in breast cancer but is unstudied in colorectal cancer (CRC).

Purpose of the Study:

• To investigate the potential therapeutic action of Tunicamycin C in colorectal cancer (CRC).
• To identify potential drug targets for Tunicamycin C in CRC using in silico methods.

Main Methods:

• Utilized HTDocking, Gene Ontology (GO), and KEGG pathway analysis to identify target proteins.
• Employed molecular dynamics (MD) modeling to analyze Tunicamycin C binding and its effect on protein structure and activity.
• Conducted serial validation studies to confirm binding site interactions and affinity.

Main Results:

• Identified thymidine kinase 1 (TK1) and cAMP-dependent protein kinase catalytic subunit alpha (PKAc) as Tunicamycin C targets.
• MD simulations revealed Tunicamycin C binding induces conformational changes, inhibiting TK1 and PKAc activity.
• Tunicamycin C demonstrated high binding affinity within the hydrophobic pockets of TK1 and PKAc.

Conclusions:

• Tunicamycin C effectively binds and inhibits TK1 and PKAc, suggesting therapeutic potential for CRC.
• TK1 and PKAc are identified as novel targets for developing glycosylation inhibitors in cancer therapy.
• This study provides a foundation for repurposing inhibitors and discovering new therapeutic strategies for CRC.