Machine Learning-Based Virtual Screening Strategy RevealsSome Natural Compounds as Potential PAK4 Inhibitors in Triple Negative Breast Cancer

Title:Machine Learning-Based Virtual Screening Strategy RevealsSome Natural Compounds as Potential PAK4 Inhibitors in Triple Negative Breast Cancer

Volume: 18 Issue: 5

Author(s): Opeyemi Iwaloye*, Olusola O. Elekofehinti, Babatomiwa Kikiowo, Emmanuel A. Oluwarotimi and Toyin M. Fadipe

Affiliation:

• Department of Biochemistry, Bioinformatics and Molecular Biology Unit, Federal University of Technology Akure, Ondo State,Nigeria

Keywords: P-21 activating kinase 4, QSAR model, e-pharmacophore model, potential inhibitors, virtual screening method, molecular docking.

Abstract:

Background: P-21 activating kinase 4 (PAK4) is implicated in the poor prognosis of many cancers, especially in the progression of Triple Negative Breast Cancer (TNBC). The present study was aimed at designing some potential drug candidates as PAK4 inhibitors for breast cancer therapy.

Objective: This study aimed to finding novel inhibitors of PAK4 from natural compounds using computational approach.

Methods: An e-pharmacophore model was developed from docked PAK4-co-ligand complex and used to screen over a thousand natural compounds downloaded from BIOFACQUIM and NPASS databases to match a minimum of 5 sites for selected (ADDDHRR) hypothesis. The robustness of the virtual screening method was accessed by well-established methods including EF, ROC, BEDROC, AUAC, and the RIE. Compounds with fitness score greater than one were filtered by applying molecular docking (HTVS, SP, XP and Induced fit docking) and ADME prediction. Using Machine learning-based approach QSAR model was generated using Automated QSAR. The computed top model kpls_des_17 (R2= 0.8028, RMSE = 0.4884 and Q2 = 0.7661) was used to predict the pIC50 of the lead compounds. Internal and external validations were accessed to determine the predictive quality of the model. Finally, the binding free energy calculation was computed.

Results: The robustness/predictive quality of the models was affirmed. The hits had better binding affinity than the reference drug and interacted with key amino acids for PAK4 inhibition. Overall, the present analysis yielded three potential inhibitors that are predicted to bind with PAK4 better than the reference drug tamoxifen. The three potent novel inhibitors, vitexin, emodin and ziganein recorded IFD score of -621.97 kcal/mol, -616.31 kcal/mol and -614.95 kcal/mol, respectively while showing moderation for ADME properties and inhibition constant.

Conclusion: It is expected that the findings reported in this study may provide insight for designing effective and less toxic PAK4 inhibitors for triple negative breast cancer.

Cite this article as:

Iwaloye Opeyemi *, Elekofehinti O. Olusola , Kikiowo Babatomiwa, Oluwarotimi A. Emmanuel and Fadipe M. Toyin , Machine Learning-Based Virtual Screening Strategy RevealsSome Natural Compounds as Potential PAK4 Inhibitors in Triple Negative Breast Cancer, Current Proteomics 2021; 18 (5) . https://dx.doi.org/10.2174/1570164618999201223092209