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Updated: Jul 8, 2025
Identification of Kinase-substrate Pairs Using High Throughput Screening
Published on: August 29, 2015
Integrating machine learning and high throughput screening for the discovery of allosteric AKT1 inhibitors
Keerthana Karunakaran1, Rajiniraja Muniyan1
1School of Biosciences and Technology, Vellore Institute of Technology, Vellore, India.
Abstract:
Evidence from clinical and experimental investigations reveals the role of AKT in oral cancer, which has led to the development of therapeutic and pharmacological medications for inhibiting AKT protein. Despite prodigious effort, researchers are searching for new allosteric inhibitors as orthosteric inhibitors are non-selective and exert off-target effects. In the current study, we proposed an integrated computational workflow for identifying allosteric AKT1 inhibitors as this isoform is highly correlated with poor prognosis and survival. To achieve this objective, 84 classification QSAR models with six different machine learning algorithms were developed. The models created with RDKit_RF and RDKit_kstar outperformed internal and test set validation with an ROC of 0.98. The outperformed models were then used to screen Chembl, which contains over a million compounds, for AKT1 inhibitors. The Tanimoto similarity search approach identified the compounds structurally resembling AKT allosteric inhibitors. The filtered compounds were further subjected to docking phases, molecular dynamic simulation and mmpbsa to verify the binding mode of selected ones. All these analyses suggested hit 5 (CHEMBL3948083) as the potential allosteric inhibitor of AKT1 as the stability parameters, favourable binding affinity (-107.78 ± 11.56 KJ/mol) and ligand interaction were better in comparison to other compounds and reference compound. The residual analysis demonstrated that allosteric and isoform-specific residues such as Trp80 and Val270 contributed the larger energy for ligand binding. The proposed integrated approach in this study might achieve a futuristic outcome when employed in a pharmaceutical scheme different from the conventional method.Communicated by Ramaswamy H. Sarma.
Insights
Researchers developed a computational method to find new allosteric inhibitors for AKT1, a protein linked to oral cancer. This approach identified a promising compound (CHEMBL3948083) with strong binding affinity, offering a novel therapeutic strategy.
Area of Science:
- Biochemistry
- Computational Chemistry
- Pharmacology
Background:
- The AKT protein plays a crucial role in oral cancer development.
- Current orthosteric inhibitors for AKT are non-selective, leading to off-target effects.
- There is a need for novel, selective allosteric inhibitors targeting AKT, particularly AKT1, due to its association with poor prognosis.
Purpose of the Study:
- To develop an integrated computational workflow for identifying selective allosteric inhibitors of AKT1.
- To screen a large compound library for potential AKT1 allosteric inhibitors.
- To validate the identified inhibitors through molecular simulations and binding affinity analysis.
Main Methods:
- Development and validation of 84 Quantitative Structure-Activity Relationship (QSAR) models using six machine learning algorithms.
- Screening of the ChEMBL database using validated QSAR models and Tanimoto similarity search.
- In-depth analysis of top candidate compounds using molecular docking, molecular dynamics simulations, and MM/PBSA calculations.
Main Results:
- QSAR models using RDKit_RF and RDKit_kstar achieved an ROC of 0.98, demonstrating high predictive accuracy.
- The integrated workflow successfully identified potential allosteric AKT1 inhibitors from over a million compounds.
- Hit 5 (CHEMBL3948083) exhibited superior stability, binding affinity (-107.78 ± 11.56 KJ/mol), and ligand interactions compared to the reference compound.
- Residual analysis highlighted the contribution of specific residues (Trp80, Val270) to the binding energy.
Conclusions:
- The proposed integrated computational approach is effective for identifying selective allosteric AKT1 inhibitors.
- CHEMBL3948083 is a promising lead compound for the development of novel oral cancer therapeutics.
- This methodology offers a potentially futuristic alternative to conventional drug discovery methods in pharmaceutical research.
