1
|
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.
Collapse
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.
| |
Collapse
|
2
|
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.
Collapse
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
| |
Collapse
|
3
|
Abudayah A, Daoud S, Al-Sha'er M, Taha M. Pharmacophore Modeling of Targets Infested with Activity Cliffs via Molecular Dynamics Simulation Coupled with QSAR and Comparison with other Pharmacophore Generation Methods: KDR as Case Study. Mol Inform 2022; 41:e2200049. [PMID: 35973966 DOI: 10.1002/minf.202200049] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/27/2022] [Accepted: 08/15/2022] [Indexed: 11/07/2022]
Abstract
Activity cliffs (ACs) are defined as pairs of structurally similar compounds with large difference in their potencies against certain biotarget. We recently proposed that potent AC members induce significant entropically-driven conformational modifications of the target that unveil additional binding interactions, while their weakly-potent counterparts are enthalpically-driven binders with little influence on the protein target. We herein propose to extract pharmacophores for ACs-infested target(s) from molecular dynamics (MD) frames of purely "enthalpic" potent binder(s) complexed within the particular target. Genetic function algorithm/machine learning (GFA/ML) can then be employed to search for the best possible combination of MD pharmacophore(s) capable of explaining bioactivity variations within a list of inhibitors. We compared the performance of this approach with established ligand-based and structure-based methods. Kinase inserts domain receptor (KDR) was used as a case study. KDR plays a crucial role in angiogenic signaling and its inhibitors have been approved in cancer treatment. Interestingly, GFA/ML selected, MD-based, pharmacophores were of comparable performances to ligand-based and structure-based pharmacophores. The resulting pharmacophores and QSAR models were used to capture hits from the national cancer institute list of compounds. The most active hit showed anti-KDR IC50 of 2.76 µM.
Collapse
Affiliation(s)
| | | | | | - Mutasem Taha
- Faculty of pharmacy,University of jordan, JORDAN
| |
Collapse
|
4
|
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]
|
5
|
AKT Inhibitors: The Road Ahead to Computational Modeling-Guided Discovery. Int J Mol Sci 2021; 22:ijms22083944. [PMID: 33920446 PMCID: PMC8070654 DOI: 10.3390/ijms22083944] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/14/2021] [Revised: 04/07/2021] [Accepted: 04/08/2021] [Indexed: 12/26/2022] Open
Abstract
AKT, is a serine/threonine protein kinase comprising three isoforms-namely: AKT1, AKT2 and AKT3, whose inhibitors have been recognized as promising therapeutic targets for various human disorders, especially cancer. In this work, we report a systematic evaluation of multi-target Quantitative Structure-Activity Relationship (mt-QSAR) models to probe AKT' inhibitory activity, based on different feature selection algorithms and machine learning tools. The best predictive linear and non-linear mt-QSAR models were found by the genetic algorithm-based linear discriminant analysis (GA-LDA) and gradient boosting (Xgboost) techniques, respectively, using a dataset containing 5523 inhibitors of the AKT isoforms assayed under various experimental conditions. The linear model highlighted the key structural attributes responsible for higher inhibitory activity whereas the non-linear model displayed an overall accuracy higher than 90%. Both these predictive models, generated through internal and external validation methods, were then used for screening the Asinex kinase inhibitor library to identify the most potential virtual hits as pan-AKT inhibitors. The virtual hits identified were then filtered by stepwise analyses based on reverse pharmacophore-mapping based prediction. Finally, results of molecular dynamics simulations were used to estimate the theoretical binding affinity of the selected virtual hits towards the three isoforms of enzyme AKT. Our computational findings thus provide important guidelines to facilitate the discovery of novel AKT inhibitors.
Collapse
|
6
|
Daoud S, Taha MO. Pharmacophore modeling of JAK1: A target infested with activity-cliffs. J Mol Graph Model 2020; 99:107615. [PMID: 32339898 DOI: 10.1016/j.jmgm.2020.107615] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/25/2020] [Revised: 04/01/2020] [Accepted: 04/02/2020] [Indexed: 12/14/2022]
Abstract
Janus kinase 1 (JAK1) is protein kinase involved in autoimmune diseases (AIDs). JAK1 inhibitors have shown promising results in treating AIDs. JAK1 inhibitors are known to exhibit regions of SAR discontinuity or activity cliffs (ACs). ACs represent fundamental challenge to successful QSAR/pharmacophore modeling because QSAR modeling rely on the basic premise that activity is a smooth continuous function of structure. We propose that ACs exist because active ACs members exhibit subtle, albeit critical, enthalpic features absent from their inactive twins. In this context we compared the performances of two computational modeling workflows in extracting valid pharmacophores from 151 diverse JAK1 inhibitors that include ACs: QSAR-guided pharmacophore selection versus docking-based comparative intermolecular contacts analysis (db-CICA). The two methods were judged based on the receiver operating characteristic (ROC) curves of their corresponding pharmacophore models and their abilities to distinguish active members among established JAK1 ACs. db-CICA modeling significantly outperformed ligand-based pharmacophore modeling. The resulting optimal db-CICA pharmacophore was used as virtual search query to scan the National Cancer Institute (NCI) database for novel JAK1 inhibitory leads. The most active hit showed IC50 of 1.04 μM. This study proposes the use of db-CICA modeling as means to extract valid pharmacophores from SAR data infested with ACs.
Collapse
Affiliation(s)
- Safa Daoud
- Department of Pharmaceutical Chemistry and Pharmacognosy, Faculty of Pharmacy, Applied Science Private University, Amman, Jordan
| | - Mutasem O Taha
- Department of Pharmaceutical Sciences, Faculty of Pharmacy, University of Jordan, Amman, Jordan.
| |
Collapse
|
7
|
Fratev F, Gutierrez DA, Aguilera RJ, Tyagi A, Damodaran C, Sirimulla S. Discovery of new AKT1 inhibitors by combination of in silico structure based virtual screening approaches and biological evaluations. J Biomol Struct Dyn 2020; 39:368-377. [DOI: 10.1080/07391102.2020.1715835] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/26/2022]
Affiliation(s)
- Filip Fratev
- Department of Pharmaceutical Sciences, School of Pharmacy, The University of Texas at El Paso, El Paso, TX, USA
- Micar Innovation (Micar 21) Ltd, Sofia, Bulgaria
| | - Denisse A. Gutierrez
- Department of Biological Sciences, The University of Texas at El Paso, El Paso, TX, USA
| | - Renato J. Aguilera
- Department of Biological Sciences, The University of Texas at El Paso, El Paso, TX, USA
| | - Ashish Tyagi
- Department of Urology, University of Louisville, Louisville, KY, USA
| | - Chendil Damodaran
- Department of Urology, University of Louisville, Louisville, KY, USA
| | - Suman Sirimulla
- Department of Pharmaceutical Sciences, School of Pharmacy, The University of Texas at El Paso, El Paso, TX, USA
| |
Collapse
|
8
|
Li H, Guo D, Zhang Y, Yang S, Zhang R. miR-664b-5p Inhibits Hepatocellular Cancer Cell Proliferation Through Targeting Oncogene AKT2. Cancer Biother Radiopharm 2020; 35:605-614. [PMID: 31967930 DOI: 10.1089/cbr.2019.3043] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/16/2022] Open
Abstract
Background: miR-664b-5p accelerates the development of certain cancers, but the role of miR-664b-5p in hepatocellular carcinoma (HCC) has been less reported. Therefore, the authors aimed to study the role of miR-664b-5p in HCC progression. Materials and Methods: miR-664b-5p expression in liver cancer and adjacent tissues, and in HepG2 and SUN-475 cells, was measured by quantitative real-time polymerase chain reaction (qRT-PCR). Relationship between miR-664b-5p and AKT2 was predicted by TargetScan and confirmed by dual-luciferase reporter assay, and gene or protein expressions were determined by performing qRT-PCR and Western blotting. The viability and apoptosis, and the migration and invasion of HepG2 and SUN-475 cells were determined by CCK-8 assay and flow cytometry, and transwell assay, respectively. Results: Downregulated miR-664b-5p was observed in hepatocellular cancer tissues. Functional analyses revealed that miR-664b-5p mimic suppressed viability, migration, and invasion, but promoted apoptosis in HepG2 and SUN-475 cells. AKT2 was a target of miR-664b-5p, whose mimics inhibited the expression of AKT2. However, upregulated AKT2 promoted viability, migration, and invasion, but inhibited apoptosis in HepG2 and SUN-475 cells, and such effects were reversed by miR-664b-5p mimics. Conclusions: miR-664b-5p acts as a cancer suppressor through negatively regulating AKT2 expression in HepG2 and SUN-475 cells, suggesting that miR-664b-5p could be a protective target for HCC patients.
Collapse
Affiliation(s)
- Hongwei Li
- The First Inpatient Ward of General Surgery, Shanxi Provincial People's Hospital, Taiyuan, China
| | - Dawei Guo
- The First Department of General surgery, The Fourth Affiliated Hospital of China Medical University, Shenyang, China
| | - Yuhong Zhang
- The First Inpatient Ward of General Surgery, Shanxi Provincial People's Hospital, Taiyuan, China
| | - Shiming Yang
- The First Inpatient Ward of General Surgery, Shanxi Provincial People's Hospital, Taiyuan, China
| | - Rui Zhang
- The First Inpatient Ward of General Surgery, Shanxi Provincial People's Hospital, Taiyuan, China
| |
Collapse
|
9
|
Mansi IA, Al-Sha'er MA, Mhaidat NM, Taha MO, Shahin R. Investigation of Binding Characteristics of Phosphoinositide-dependent Kinase-1 (PDK1) Co-crystallized Ligands Through Virtual Pharmacophore Modeling Leading to Novel Anti-PDK1 Hits. Med Chem 2019; 16:860-880. [PMID: 31339076 DOI: 10.2174/1573406415666190724131048] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/22/2019] [Revised: 07/11/2019] [Accepted: 07/11/2019] [Indexed: 11/22/2022]
Abstract
BACKGROUND 3-Phosphoinositide Dependent Protein Kinase-1 (PDK1) is being lately considered as an attractive and forthcoming anticancer target. A Protein Data Bank (PDB) cocrystallized crystal provides not only rigid theoretical data but also a realistic molecular recognition data that can be explored and used to discover new hits. OBJECTIVE This incited us to investigate the co-crystallized ligands' contacts inside the PDK1 binding pocket via a structure-based receptor-ligand pharmacophore generation technique in Discovery Studio 4.5 (DS 4.5). METHODS Accordingly, 35 crystals for PDK1 were collected and studied. Every single receptorligand interaction was validated and the significant ones were converted into their corresponding pharmacophoric features. The generated pharmacophores were scored by the Receiver Operating Characteristic (ROC) curve analysis. RESULTS Consequently, 169 pharmacophores were generated and sorted, 11 pharmacophores acquired good ROC-AUC results of 0.8 and a selectivity value above 8. Pharmacophore 1UU3_2_01 was used in particular as a searching filter to screen NCI database because of its acceptable validity criteria and its distinctive positive ionizable feature. Several low micromolar PDK1 inhibitors were revealed. The most potent hit illustrated anti-PDK1 IC50 values of 200 nM with 70% inhibition against SW480 cell lines. CONCLUSION Eventually, the active hits were docked inside the PDK1 binding pocket and the recognition points between the active hits and the receptor were analyzed that led to the discovery of new scaffolds as potential PDK1 inhibitors.
Collapse
Affiliation(s)
- Iman A Mansi
- Faculty of Pharmaceutical Sciences, The Hashemite University, P.O. Box 330127 Zarqa, 13133 Jordan
| | | | - Nizar M Mhaidat
- Clinical Pharmacy Department, Faculty of Pharmacy, Jordan University of Science & Technology, Irbid, Jordan
| | - Mutasem O Taha
- Drug Design Center, Faculty of Pharmacy, University of Jordan, Amman, Jordan,Faculty of Pharmacy, Applied Science University, Amman, Jordan
| | - Rand Shahin
- Faculty of Pharmaceutical Sciences, The Hashemite University, P.O. Box 330127 Zarqa, 13133 Jordan
| |
Collapse
|
10
|
Abutayeh RF, Taha MO. Discovery of novel Flt3 inhibitory chemotypes through extensive ligand-based and new structure-based pharmacophore modelling methods. J Mol Graph Model 2019; 88:128-151. [PMID: 30703688 DOI: 10.1016/j.jmgm.2019.01.011] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/27/2018] [Revised: 01/03/2019] [Accepted: 01/17/2019] [Indexed: 01/10/2023]
|