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Jaradat NJ, Hatmal M, Alqudah D, Taha MO. Computational workflow for discovering small molecular binders for shallow binding sites by integrating molecular dynamics simulation, pharmacophore modeling, and machine learning: STAT3 as case study. J Comput Aided Mol Des 2023; 37:659-678. [PMID: 37597062 DOI: 10.1007/s10822-023-00528-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/02/2023] [Accepted: 07/26/2023] [Indexed: 08/21/2023]
Abstract
STAT3 belongs to a family of seven transcription factors. It plays an important role in activating the transcription of various genes involved in a variety of cellular processes. High levels of STAT3 are detected in several types of cancer. Hence, STAT3 inhibition is considered a promising therapeutic anti-cancer strategy. However, since STAT3 inhibitors bind to the shallow SH2 domain of the protein, it is expected that hydration water molecules play significant role in ligand-binding complicating the discovery of potent binders. To remedy this issue, we herein propose to extract pharmacophores from molecular dynamics (MD) frames of a potent co-crystallized ligand complexed within STAT3 SH2 domain. Subsequently, we employ genetic function algorithm coupled with machine learning (GFA-ML) to explore the optimal combination of MD-derived pharmacophores that can account for the variations in bioactivity among a list of inhibitors. To enhance the dataset, the training and testing lists were augmented nearly a 100-fold by considering multiple conformers of the ligands. A single significant pharmacophore emerged after 188 ns of MD simulation to represent STAT3-ligand binding. Screening the National Cancer Institute (NCI) database with this model identified one low micromolar inhibitor most likely binds to the SH2 domain of STAT3 and inhibits this pathway.
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Affiliation(s)
- Nour Jamal Jaradat
- Department of Medical Laboratory Sciences, Faculty of Applied Health Sciences, The Hashemite University, P.O. Box 330127, Zarqa, 13133, Jordan
| | - Mamon Hatmal
- Department of Medical Laboratory Sciences, Faculty of Applied Health Sciences, The Hashemite University, P.O. Box 330127, Zarqa, 13133, Jordan
| | - Dana Alqudah
- Cell Therapy Center, the University of Jordan, Amman, 11942, Jordan
| | - Mutasem Omar Taha
- Department of Pharmaceutical Sciences, Faculty of Pharmacy, University of Jordan, Amman, Jordan.
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Ashram M, Habashneh AY, Bardaweel S, Taha MO. A Click Synthesis, Molecular Docking and Biological Evaluation of 1,2,3-triazoles-benzoxazepine hybrid as potential anticancer agents. Med Chem Res 2022. [DOI: 10.1007/s00044-022-03001-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/27/2022]
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Alabed SJ, Zihlif M, Taha M. Discovery of new potent lysine specific histone demythelase-1 inhibitors (LSD-1) using structure based and ligand based molecular modelling and machine learning. RSC Adv 2022; 12:35873-35895. [PMID: 36545090 PMCID: PMC9751883 DOI: 10.1039/d2ra05102h] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/15/2022] [Accepted: 12/05/2022] [Indexed: 12/23/2022] Open
Abstract
Lysine-specific histone demethylase 1 (LSD-1) is an epigenetic enzyme that oxidatively cleaves methyl groups from monomethyl and dimethyl Lys4 of histone H3 and is highly overexpressed in different types of cancer. Therefore, it has been widely recognized as a promising therapeutic target for cancer therapy. Towards this end, we employed various Computer Aided Drug Design (CADD) approaches including pharmacophore modelling and machine learning. Pharmacophores generated by structure-based (SB) (either crystallographic-based or docking-based) and ligand-based (LB) (either supervised or unsupervised) modelling methods were allowed to compete within the context of genetic algorithm/machine learning and were assessed by Shapley additive explanation values (SHAP) to end up with three successful pharmacophores that were used to screen the National Cancer Institute (NCI) database. Seventy-five NCI hits were tested for their LSD-1 inhibitory properties against neuroblastoma SH-SY5Y cells, pancreatic carcinoma Panc-1 cells, glioblastoma U-87 MG cells and in vitro enzymatic assay, culminating in 3 nanomolar LSD-1 inhibitors of novel chemotypes.
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Affiliation(s)
- Shada J Alabed
- Department of Pharmacy, Faculty of Pharmacy, Al-Zaytoonah University of Jordan Amman Jordan
| | - Malek Zihlif
- Department of Pharmacology, Faculty of Medicine, University of Jordan Amman Jordan
| | - Mutasem Taha
- Department of Pharmaceutical Sciences, Faculty of Pharmacy, University of Jordan Amman Jordan
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Al-Tawil MF, Daoud S, Hatmal MM, Taha MO. Discovery of new Cdc2-like kinase 4 (CLK4) inhibitors via pharmacophore exploration combined with flexible docking-based ligand/receptor contact fingerprints and machine learning. RSC Adv 2022; 12:10686-10700. [PMID: 35424985 PMCID: PMC8982525 DOI: 10.1039/d2ra00136e] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/08/2022] [Accepted: 03/31/2022] [Indexed: 11/21/2022] Open
Abstract
Cdc2-like kinase 4 (CLK4) inhibitors are of potential therapeutic value in many diseases particularly cancer. In this study, we combined extensive ligand-based pharmacophore exploration, ligand–receptor contact fingerprints generated by flexible docking, physicochemical descriptors and machine learning-quantitative structure–activity relationship (ML-QSAR) analysis to investigate the pharmacophoric/binding requirements for potent CLK4 antagonists. Several ML methods were attempted to tie these properties with anti-CLK4 bioactivities including multiple linear regression (MLR), random forests (RF), extreme gradient boosting (XGBoost), probabilistic neural network (PNN), and support vector regression (SVR). A genetic function algorithm (GFA) was combined with each method for feature selection. Eventually, GFA-SVR was found to produce the best self-consistent and predictive model. The model selected three pharmacophores, three ligand–receptor contacts and two physicochemical descriptors. The GFA-SVR model and associated pharmacophore models were used to screen the National Cancer Institute (NCI) structural database for novel CLK4 antagonists. Three potent hits were identified with the best one showing an anti-CLK4 IC50 value of 57 nM. Ligand-based pharmacophores, ligand–receptor contact fingerprints, physicochemical descriptors and machine learning were combined to probe binding of potent CLK4 antagonists. GFA-SVR gave the best model. Virtual screening identified 3 nanomolar hits.![]()
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Affiliation(s)
- Mai Fayiz Al-Tawil
- Department of Pharmaceutical Sciences, Faculty of Pharmacy, University of Jordan Amman 11942 Jordan
| | - Safa Daoud
- Department of Pharmaceutical Chemistry and Pharmacognosy, Faculty of Pharmacy, Applied Sciences Private University Amman Jordan
| | - Ma'mon M Hatmal
- Department of Medical Laboratory Sciences, Faculty of Applied Medical Sciences, The Hashemite University PO Box 330127 Zarqa 13133 Jordan
| | - Mutasem Omar Taha
- Department of Pharmaceutical Sciences, Faculty of Pharmacy, University of Jordan Amman 11942 Jordan
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Exploiting activity cliffs for building pharmacophore models and comparison with other pharmacophore generation methods: sphingosine kinase 1 as case study. J Comput Aided Mol Des 2022; 36:39-62. [PMID: 35059939 DOI: 10.1007/s10822-021-00435-0] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/14/2021] [Accepted: 11/24/2021] [Indexed: 12/20/2022]
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Hatmal MM, Abuyaman O, Taha M. Docking-generated multiple ligand poses for bootstrapping bioactivity classifying Machine Learning: Repurposing covalent inhibitors for COVID-19-related TMPRSS2 as case study. Comput Struct Biotechnol J 2021; 19:4790-4824. [PMID: 34426763 PMCID: PMC8373588 DOI: 10.1016/j.csbj.2021.08.023] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/22/2021] [Revised: 08/03/2021] [Accepted: 08/16/2021] [Indexed: 01/10/2023] Open
Abstract
In the present work we introduce the use of multiple docked poses for bootstrapping machine learning-based QSAR modelling. Ligand-receptor contact fingerprints are implemented as descriptor variables. We implemented this method for the discovery of potential inhibitors of the serine protease enzyme TMPRSS2 involved the infectivity of coronaviruses. Several machine learners were scanned, however, Xgboost, support vector machines (SVM) and random forests (RF) were the best with testing set accuracies reaching 90%. Three potential hits were identified upon using the method to scan known untested FDA approved drugs against TMPRSS2. Subsequent molecular dynamics simulation and covalent docking supported the results of the new computational approach.
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Affiliation(s)
- Ma'mon M. Hatmal
- Department of Medical Laboratory Sciences, Faculty of Applied Medical Sciences, The Hashemite University, PO Box 330127, Zarqa 13133, Jordan
| | - Omar Abuyaman
- Department of Medical Laboratory Sciences, Faculty of Applied Medical Sciences, The Hashemite University, PO Box 330127, Zarqa 13133, Jordan
| | - Mutasem Taha
- Department of Pharmaceutical Sciences, Faculty of Pharmacy, University of Jordan, Amman 11942, Jordan
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Hijjawi MS, Abutayeh RF, Taha MO. Structure-Based Discovery and Bioactivity Evaluation of Novel Aurora-A Kinase Inhibitors as Anticancer Agents via Docking-Based Comparative Intermolecular Contacts Analysis (dbCICA). Molecules 2020; 25:molecules25246003. [PMID: 33353031 PMCID: PMC7766225 DOI: 10.3390/molecules25246003] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/15/2020] [Revised: 12/11/2020] [Accepted: 12/11/2020] [Indexed: 01/12/2023] Open
Abstract
Aurora-A kinase plays a central role in mitosis, where aberrant activation contributes to cancer by promoting cell cycle progression, genomic instability, epithelial-mesenchymal transition, and cancer stemness. Aurora-A kinase inhibitors have shown encouraging results in clinical trials but have not gained Food and Drug Administration (FDA) approval. An innovative computational workflow named Docking-based Comparative Intermolecular Contacts Analysis (dbCICA) was applied—aiming to identify novel Aurora-A kinase inhibitors—using seventy-nine reported Aurora-A kinase inhibitors to specify the best possible docking settings needed to fit into the active-site binding pocket of Aurora-A kinase crystal structure, in a process that only potent ligands contact critical binding-site spots, distinct from those occupied by less-active ligands. Optimal dbCICA models were transformed into two corresponding pharmacophores. The optimal one, in capturing active hits and discarding inactive ones, validated by receiver operating characteristic analysis, was used as a virtual in-silico search query for screening new molecules from the National Cancer Institute database. A fluorescence resonance energy transfer (FRET)-based assay was used to assess the activity of captured molecules and five promising Aurora-A kinase inhibitors were identified. The activity was next validated using a cell culture anti-proliferative assay (MTT) and revealed a most potent lead 85(NCI 14040) molecule after 72 h of incubation, scoring IC50 values of 3.5–11.0 μM against PANC1 (pancreas), PC-3 (prostate), T-47D and MDA-MB-231 (breast)cancer cells, and showing favorable safety profiles (27.5 μM IC50 on fibroblasts). Our results provide new clues for further development of Aurora-A kinase inhibitors as anticancer molecules.
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Affiliation(s)
- Majd S Hijjawi
- Department of Pharmacology, Faculty of Medicine, The University of Jordan, Amman 11942, Jordan
| | - Reem Fawaz Abutayeh
- Department of Pharmaceutical Chemistry and Pharmacognosy, Faculty of Pharmacy, Applied Science Private University, Amman 11931, Jordan
| | - Mutasem O Taha
- Department of Pharmaceutical Sciences, Faculty of Pharmacy, University of Jordan, Amman 11942, Jordan
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Hatmal MM, Alshaer W, Al-Hatamleh MAI, Hatmal M, Smadi O, Taha MO, Oweida AJ, Boer JC, Mohamud R, Plebanski M. Comprehensive Structural and Molecular Comparison of Spike Proteins of SARS-CoV-2, SARS-CoV and MERS-CoV, and Their Interactions with ACE2. Cells 2020; 9:E2638. [PMID: 33302501 PMCID: PMC7763676 DOI: 10.3390/cells9122638] [Citation(s) in RCA: 122] [Impact Index Per Article: 30.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/31/2020] [Revised: 12/01/2020] [Accepted: 12/04/2020] [Indexed: 01/03/2023] Open
Abstract
The severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) has recently emerged in China and caused a disease called coronavirus disease 2019 (COVID-19). The virus quickly spread around the world, causing a sustained global outbreak. Although SARS-CoV-2, and other coronaviruses, SARS-CoV and Middle East respiratory syndrome CoV (MERS-CoV) are highly similar genetically and at the protein production level, there are significant differences between them. Research has shown that the structural spike (S) protein plays an important role in the evolution and transmission of SARS-CoV-2. So far, studies have shown that various genes encoding primarily for elements of S protein undergo frequent mutation. We have performed an in-depth review of the literature covering the structural and mutational aspects of S protein in the context of SARS-CoV-2, and compared them with those of SARS-CoV and MERS-CoV. Our analytical approach consisted in an initial genome and transcriptome analysis, followed by primary, secondary and tertiary protein structure analysis. Additionally, we investigated the potential effects of these differences on the S protein binding and interactions to angiotensin-converting enzyme 2 (ACE2), and we established, after extensive analysis of previous research articles, that SARS-CoV-2 and SARS-CoV use different ends/regions in S protein receptor-binding motif (RBM) and different types of interactions for their chief binding with ACE2. These differences may have significant implications on pathogenesis, entry and ability to infect intermediate hosts for these coronaviruses. This review comprehensively addresses in detail the variations in S protein, its receptor-binding characteristics and detailed structural interactions, the process of cleavage involved in priming, as well as other differences between coronaviruses.
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Affiliation(s)
- Ma’mon M. Hatmal
- Department of Medical Laboratory Sciences, Faculty of Applied Health Sciences, The Hashemite University, Zarqa 13133, Jordan
| | - Walhan Alshaer
- Cell Therapy Center (CTC), The University of Jordan, Amman 11942, Jordan
| | - Mohammad A. I. Al-Hatamleh
- Department of Immunology, School of Medical Sciences, Universiti Sains Malaysia, Kubang Kerian, Kelantan 16150, Malaysia; (M.A.I.A.-H.); (R.M.)
| | | | - Othman Smadi
- Department of Biomedical Engineering, Faculty of Engineering, The Hashemite University, Zarqa 13133, Jordan;
| | - Mutasem O. Taha
- Drug Design and Discovery Unit, Department of Pharmaceutical Sciences, Faculty of Pharmacy, The University of Jordan, Amman 11942, Jordan;
| | - Ayman J. Oweida
- Department of Nuclear Medicine and Radiobiology, Faculty of Medicine and Health Sciences, Université de Sherbrooke, Sherbrooke, QC J1H 5N4, Canada;
| | - Jennifer C. Boer
- Translational Immunology and Nanotechnology Unit, School of Health and Biomedical Sciences, RMIT University, Bundoora 3083, Australia; (J.C.B.); (M.P.)
| | - Rohimah Mohamud
- Department of Immunology, School of Medical Sciences, Universiti Sains Malaysia, Kubang Kerian, Kelantan 16150, Malaysia; (M.A.I.A.-H.); (R.M.)
- Hospital Universiti Sains Malaysia, Health Campus, Kubang Kerian, Kelantan 16150, Malaysia
| | - Magdalena Plebanski
- Translational Immunology and Nanotechnology Unit, School of Health and Biomedical Sciences, RMIT University, Bundoora 3083, Australia; (J.C.B.); (M.P.)
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Tuffaha GO, Hatmal MM, Taha MO. Discovery of new JNK3 inhibitory chemotypes via QSAR-Guided selection of docking-based pharmacophores and comparison with other structure-based pharmacophore modeling methods. J Mol Graph Model 2019; 91:30-51. [PMID: 31158642 DOI: 10.1016/j.jmgm.2019.05.015] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/19/2019] [Revised: 05/13/2019] [Accepted: 05/17/2019] [Indexed: 12/21/2022]
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Al-Sha'er MA, Al-Aqtash RA, Taha MO. Discovery of New Phosphoinositide 3-kinase Delta (PI3Kδ) Inhibitors via Virtual Screening using Crystallography-derived Pharmacophore Modelling and QSAR Analysis. Med Chem 2019; 15:588-601. [PMID: 30799792 DOI: 10.2174/1573406415666190222125333] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/11/2018] [Revised: 01/31/2019] [Accepted: 02/07/2019] [Indexed: 01/29/2023]
Abstract
BACKGROUND PI3Kδ is predominantly expressed in hematopoietic cells and participates in the activation of leukocytes. PI3Kδ inhibition is a promising approach for treating inflammatory diseases and leukocyte malignancies. Accordingly, we decided to model PI3Kδ binding. METHODS Seventeen PI3Kδ crystallographic complexes were used to extract 94 pharmacophore models. QSAR modelling was subsequently used to select the superior pharmacophore(s) that best explain bioactivity variation within a list of 79 diverse inhibitors (i.e., upon combination with other physicochemical descriptors). RESULTS The best QSAR model (r2 = 0.71, r2 LOO = 0.70, r2 press against external testing list of 15 compounds = 0.80) included a single crystallographic pharmacophore of optimal explanatory qualities. The resulting pharmacophore and QSAR model were used to screen the National Cancer Institute (NCI) database for new PI3Kδ inhibitors. Two hits showed low micromolar IC50 values. CONCLUSION Crystallography-based pharmacophores were successfully combined with QSAR analysis for the identification of novel PI3Kδ inhibitors.
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Affiliation(s)
- Mahmoud A Al-Sha'er
- Faculty of Pharmacy, Zarqa University, P.O. Box 132222, Zarqa, 13132, Jordan
| | - Rua'a A Al-Aqtash
- Faculty of Pharmacy, Zarqa University, P.O. Box 132222, Zarqa, 13132, Jordan
| | - Mutasem O Taha
- Department of Pharmaceutical Sciences, Faculty of Pharmacy, The University of Jordan, Amman, Jordan
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