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.
