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Discovery of Novel Noncovalent KRAS G12D Inhibitors through Structure-Based Virtual Screening and Molecular Dynamics
Zhenya Du1,2, Gao Tu1, Yaguo Gong1
1State Key Laboratory of Quality Research in Chinese Medicine, Dr. Neher's Biophysics Laboratory for Innovative Drug Discovery, Macau Institute for Applied Research in Medicine and Health, Faculty of Chinese Medicine, Macau University of Science and Technology, Macao 999078, China.
Abstract:
The development of effective inhibitors targeting the Kirsten rat sarcoma viral proto-oncogene (KRASG12D) mutation, a prevalent oncogenic driver in cancer, represents a significant unmet need in precision medicine. In this study, an integrated computational approach combining structure-based virtual screening and molecular dynamics simulation was employed to identify novel noncovalent inhibitors targeting the KRASG12D variant. Through virtual screening of over 1.7 million diverse compounds, potential lead compounds with high binding affinity and specificity were identified using molecular docking and scoring techniques. Subsequently, 200 ns molecular dynamics simulations provided critical insights into the dynamic behavior, stability, and conformational changes of the inhibitor-KRASG12D complexes, facilitating the selection of lead compounds with robust binding profiles. Additionally, in silico absorption, distribution, metabolism, excretion (ADME) profiling, and toxicity predictions were applied to prioritize the lead compounds for further experimental validation. The discovered noncovalent KRASG12D inhibitors exhibit promises as potential candidates for targeted therapy against KRASG12D-driven cancers. This comprehensive computational framework not only expedites the discovery of novel KRASG12D inhibitors but also provides valuable insights for the development of precision treatments tailored to this oncogenic mutation.
Insights
Researchers identified novel noncovalent inhibitors for the KRAS G12D mutation using computational methods. These promising drug candidates offer new avenues for targeted cancer therapy.
Area of Science:
- Oncology
- Computational Chemistry
- Drug Discovery
Background:
- KRAS G12D mutations are key drivers in many cancers, presenting a major challenge in precision medicine.
- Developing targeted therapies for KRAS G12D remains a significant unmet medical need.
Purpose of the Study:
- To identify novel noncovalent inhibitors specifically targeting the KRAS G12D oncogenic mutation.
- To leverage computational approaches for efficient drug discovery and lead compound selection.
Main Methods:
- Employed structure-based virtual screening of over 1.7 million compounds.
- Utilized molecular docking, scoring, and 200 ns molecular dynamics simulations for lead identification and validation.
- Conducted in silico ADME profiling and toxicity predictions for lead compound prioritization.
Main Results:
- Identified potential lead compounds with high binding affinity and specificity for KRAS G12D.
- Molecular dynamics simulations revealed stable inhibitor-KRAS G12D complexes.
- In silico assessments prioritized compounds for experimental validation.
Conclusions:
- Discovered promising noncovalent KRAS G12D inhibitors through a comprehensive computational strategy.
- These inhibitors show potential for targeted therapy in KRAS G12D-driven cancers.
- The computational framework accelerates the discovery of precision cancer treatments.
