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A PIM-1 Kinase Inhibitor Docking Optimization Study Based on Logistic Regression Models and Interaction Analysis
George Nicolae Daniel Ion1, George Mihai Nitulescu1, Dragos Paul Mihai1
1Faculty of Pharmacy, "Carol Davila" University of Medicine and Pharmacy, Traian Vuia 6, 020956 Bucharest, Romania.
Abstract:
PIM-1 kinase is a serine-threonine phosphorylating enzyme with implications in multiple types of malignancies, including prostate, breast, and blood cancers. Developing better search methodologies for PIM-1 kinase inhibitors may be a good strategy to speed up the discovery of an oncological drug approved for targeting this specific kinase. Computer-aided screening methods are promising approaches for the discovery of novel therapeutics, although certain limitations should be addressed. A frequent omission that is encountered in molecular docking is the lack of proper implementation of scoring functions and algorithms on the post-docking results, which usually alters the outcome of the virtual screening. The current study suggests a method for post-processing docking results, expressed either as binding affinity or score, that considers different binding modes of known inhibitors to the studied targets while making use of in vitro data, where available. The docking protocol successfully discriminated between known PIM-1 kinase inhibitors and decoy molecules, although binding energies alone were not sufficient to ensure a successful prediction. Logistic regression models were trained to predict the probability of PIM-1 kinase inhibitory activity based on binding energies and the presence of interactions with identified key amino acid residues. The selected model showed 80.9% true positive and 81.4% true negative rates. The discussed approach can be further applied in large-scale molecular docking campaigns to increase hit discovery success rates.
Insights
This study introduces an improved computational method to identify PIM-1 kinase inhibitors, enhancing drug discovery for cancers like prostate and breast cancer. The new approach refines molecular docking results for more accurate identification of potential cancer therapeutics.
Area of Science:
- Medicinal Chemistry
- Computational Biology
- Oncology
Background:
- PIM-1 kinase is a key target in multiple cancers, including prostate, breast, and blood cancers.
- Accelerating the discovery of PIM-1 kinase inhibitors is crucial for developing targeted oncological drugs.
- Computer-aided screening offers a promising avenue for novel therapeutic discovery but faces limitations.
Purpose of the Study:
- To develop an improved post-processing method for molecular docking results in virtual screening.
- To enhance the accuracy of identifying PIM-1 kinase inhibitors by integrating binding modes and in vitro data.
- To refine computational strategies for hit discovery in kinase inhibitor research.
Main Methods:
- A novel post-processing method for molecular docking scores and binding affinities was developed.
- The method incorporates known inhibitor binding modes and available in vitro data.
- Logistic regression models were trained using binding energies and key amino acid residue interactions to predict inhibitory activity.
Main Results:
- The docking protocol effectively distinguished known PIM-1 kinase inhibitors from decoy molecules.
- Binding energies alone were insufficient for accurate prediction; incorporating binding modes and interactions improved results.
- The logistic regression model achieved 80.9% true positive and 81.4% true negative rates in predicting PIM-1 inhibitory activity.
Conclusions:
- The proposed post-processing approach enhances the success rate of hit discovery in virtual screening campaigns.
- This method offers a more reliable strategy for identifying potential PIM-1 kinase inhibitors.
- The approach can be applied to large-scale molecular docking for accelerated therapeutic development.
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