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Identification of TBK1 inhibitors against breast cancer using a computational approach supported by machine learning
Arif Jamal Siddiqui1, Arshad Jamal1, Mubashir Zafar2
1Department of Biology, College of Science, University of Ha'il, Ha'il, Saudi Arabia.
Abstract:
Introduction: The cytosolic Ser/Thr kinase TBK1 is of utmost importance in facilitating signals that facilitate tumor migration and growth. TBK1-related signaling plays important role in tumor progression, and there is need to work on new methods and workflows to identify new molecules for potential treatments for TBK1-affecting oncologies such as breast cancer. Methods: Here, we propose the machine learning assisted computational drug discovery approach to identify TBK1 inhibitors. Through our computational ML-integrated approach, we identified four novel inhibitors that could be used as new hit molecules for TBK1 inhibition. Results and Discussion: All these four molecules displayed solvent based free energy values of -48.78, -47.56, -46.78 and -45.47 Kcal/mol and glide docking score of -10.4, -9.84, -10.03, -10.06 Kcal/mol respectively. The molecules displayed highly stable RMSD plots, hydrogen bond patterns and MMPBSA score close to or higher than BX795 molecule. In future, all these compounds can be further refined or validated by in vitro as well as in vivo activity. Also, we have found two novel groups that have the potential to be utilized in a fragment-based design strategy for the discovery and development of novel inhibitors targeting TBK1. Our method for identifying small molecule inhibitors can be used to make fundamental advances in drug design methods for the TBK1 protein which will further help to reduce breast cancer incidence.
Insights
Researchers developed a machine learning approach to discover novel TBK1 inhibitors for cancer treatment. Four promising drug candidates were identified, showing potential for further development against TBK1-related oncologies like breast cancer.
Area of Science:
- Biochemistry
- Computational Chemistry
- Drug Discovery
Background:
- The Ser/Thr kinase TBK1 is crucial for signaling pathways that promote tumor migration and growth.
- TBK1 dysregulation is implicated in various cancers, necessitating novel therapeutic strategies.
- Targeting TBK1 offers a promising avenue for developing new anti-cancer treatments, particularly for breast cancer.
Purpose of the Study:
- To identify novel small molecule inhibitors of TBK1 using a machine learning-assisted computational drug discovery approach.
- To discover potential hit molecules for the development of new therapies against TBK1-driven cancers.
- To explore new drug design strategies for TBK1 inhibitors.
Main Methods:
- Utilized a machine learning-integrated computational approach for drug discovery.
- Performed virtual screening and molecular docking to identify potential TBK1 inhibitors.
- Evaluated binding free energy, docking scores, RMSD, hydrogen bonding, and MMPBSA for candidate molecules.
Main Results:
- Identified four novel small molecules with significant binding affinities for TBK1.
- These molecules exhibited favorable binding free energy values (ranging from -45.47 to -48.78 Kcal/mol) and glide docking scores (ranging from -9.84 to -10.4 Kcal/mol).
- Discovered two novel molecular groups suitable for fragment-based drug design targeting TBK1.
Conclusions:
- The developed machine learning approach is effective in identifying novel TBK1 inhibitors.
- The identified compounds represent promising hit molecules for further preclinical development against TBK1-related cancers.
- This study advances computational drug design methodologies for TBK1, potentially aiding in reducing breast cancer incidence.
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