Identification of novel NLRP3 inhibitors as therapeutic options for epilepsy by machine learning-based virtual screening, molecular docking and biomolecular simulation studies
Maryam Zulfat,
Mohammed Ageeli Hakami,
Ali Hazazi,
Arif Mahmood,
Asaad Khalid,
Roaya S. Alqurashi,
Ashraf N. Abdalla,
Junjian Hu,
Abdul Wadood,
Xiaoyun Huang
Affiliations
Maryam Zulfat
Department of Biochemistry, Computational Medicinal Chemistry Laboratory, Abdul Wali Khan University, Mardan, Pakistan
Mohammed Ageeli Hakami
Department of Clinical Laboratory Sciences, College of Applied Medical Sciences, Shaqra University, Al-Quwayiyah-19257, Riyadh, Saudi Arabia
Ali Hazazi
Department of Pathology and Laboratory Medicine, Security Forces Hospital Program, Riyadh, Saudi Arabia; College of Medicine, Alfaisal University, Riyadh, Saudi Arabia
Arif Mahmood
Department of Biochemistry, Quaid-i-Azam University Islamabad, Pakistan
Asaad Khalid
Substance Abuse and Toxicology Research Center, Jazan University, P.O. Box: 114, Jazan 45142, Saudi Arabia; Corresponding author.
Roaya S. Alqurashi
Department of Pharmacology and Toxicology, College of Pharmacy, Umm Al-Qura University, Makkah 21955, Saudi Arabia
Ashraf N. Abdalla
Department of Pharmacology and Toxicology, College of Pharmacy, Umm Al-Qura University, Makkah 21955, Saudi Arabia
Junjian Hu
Department of Central Laboratory, SSL, Central Hospital of Dongguan City, Affiliated Dongguan Shilong People's Hospital of Guangdong Medical University, Dongguan, China
Abdul Wadood
Department of Biochemistry, Computational Medicinal Chemistry Laboratory, Abdul Wali Khan University, Mardan, Pakistan; Corresponding author.
Xiaoyun Huang
Corresponding author. Department of Neurology, Houjie Hospital and Clinical College of Guangdong Medical University, Dongguan, China; Department of Biochemistry, Computational Medicinal Chemistry Laboratory, Abdul Wali Khan University, Mardan, Pakistan; Department of Clinical Laboratory Sciences, College of Applied Medical Sciences, Shaqra University, Al-Quwayiyah-19257, Riyadh, Saudi Arabia; Department of Pathology and Laboratory Medicine, Security Forces Hospital Program, Riyadh, Saudi Arabia; College of Medicine, Alfaisal University, Riyadh, Saudi Arabia; Department of Biochemistry, Quaid-i-Azam University Islamabad, Pakistan; Substance Abuse and Toxicology Research Center, Jazan University, P.O. Box: 114, Jazan 45142, Saudi Arabia; Department of Pharmacology and Toxicology, College of Pharmacy, Umm Al-Qura University, Makkah 21955, Saudi Arabia; Department of Central Laboratory, SSL, Central Hospital of Dongguan City, Affiliated Dongguan Shilong People's Hospital of Guangdong Medical University, Dongguan, China; Department of Neurology, Houjie Hospital and Clinical College of Guangdong Medical University, Dongguan, China
The NOD-Like Receptor Protein-3 (NLRP3) inflammasome is a key therapeutic target for the treatment of epilepsy and has been reported to regulate inflammation in several neurological diseases. In this study, a machine learning-based virtual screening strategy has investigated candidate active compounds that inhibit the NLRP3 inflammasome. As machine learning-based virtual screening has the potential to accurately predict protein-ligand binding and reduce false positives outcomes compared to traditional virtual screening. Briefly, classification models were created using Support Vector Machine (SVM), Random Forest (RF), and K-Nearest Neighbor (KNN) machine learning methods. To determine the most crucial features of a molecule's activity, feature selection was carried out. By utilizing 10-fold cross-validation, the created models were analyzed. Among the generated models, the RF model obtained the best results as compared to others. Therefore, the RF model was used as a screening tool against the large chemical databases. Molecular operating environment (MOE) and PyRx software's were applied for molecular docking. Also, using the Amber Tools program, molecular dynamics (MD) simulation of potent inhibitors was carried out. The results showed that the KNN, SVM, and RF accuracy was 0.911 %, 0.906 %, and 0.946 %, respectively. Moreover, the model has shown sensitivity of 0.82 %, 0.78 %, and 0.86 % and specificity of 0.95 %, 0.96 %, and 0.98 % respectively. By applying the model to the ZINC and South African databases, we identified 98 and 39 compounds, respectively, potentially possessing anti-NLRP3 activity. Also, a molecular docking analysis produced ten ZINC and seven South African compounds that has comparable binding affinities to the reference drug. Moreover, MD analysis of the two complexes revealed that the two compounds (ZINC000009601348 and SANC00225) form stable complexes with varying amounts of binding energy. The in-silico studies indicate that both compounds most likely display their inhibitory effect by inhibiting the NLRP3 protein.
