1
|
Al-Qadhi MA, Yahya TAA, El-Nassan HB. Recent Advances in the Discovery of CK2 Inhibitors. ACS OMEGA 2024; 9:20702-20719. [PMID: 38764653 PMCID: PMC11097362 DOI: 10.1021/acsomega.3c10478] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/29/2023] [Revised: 04/05/2024] [Accepted: 04/12/2024] [Indexed: 05/21/2024]
Abstract
CK2 is a vital enzyme that phosphorylates a large number of substrates and thereby controls many processes in the body. Its upregulation was reported in many cancer types. Inhibitors of CK2 might have anticancer activity, and two compounds are currently under clinical trials. However, both compounds are ATP-competitive inhibitors that may have off-target side effects. The development of allosteric and dual inhibitors can overcome this drawback. These inhibitors showed higher selectivity and specificity for the CK2 enzyme compared to the ATP-competitive inhibitors. The present review summarizes the efforts exerted in the last five years in the design of CK2 inhibitors.
Collapse
Affiliation(s)
- Mustafa A. Al-Qadhi
- Department
of Medicinal Chemistry, Faculty of Pharmacy, Sana’a University, 18084 Sana’a, Yemen
| | - Tawfeek A. A. Yahya
- Department
of Medicinal Chemistry, Faculty of Pharmacy, Sana’a University, 18084 Sana’a,Yemen
| | - Hala B. El-Nassan
- Pharmaceutical
Organic Chemistry Department, Faculty of Pharmacy, Cairo University, Cairo 11562, Egypt
| |
Collapse
|
2
|
Shahab M, Ziyu P, Waqas M, Zheng G, Bin Jardan YA, Fentahun Wondmie G, Bouhrhia M. Targeting human progesterone receptor (PR), through pharmacophore-based screening and molecular simulation revealed potent inhibitors against breast cancer. Sci Rep 2024; 14:6768. [PMID: 38514638 PMCID: PMC10958019 DOI: 10.1038/s41598-024-55321-0] [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: 09/13/2023] [Accepted: 02/22/2024] [Indexed: 03/23/2024] Open
Abstract
Breast cancer, the prevailing malignant tumor among women, is linked to progesterone and its receptor (PR) in both tumorigenesis and treatment responsiveness. Despite thorough investigation, the precise molecular mechanisms of progesterone in breast cancer remain unclear. The human progesterone receptor (PR) serves as an essential therapeutic target for breast cancer treatment, warranting the rapid design of small molecule therapeutics that can effectively inhibit HPR. By employing cutting-edge computational techniques like molecular screening, simulation, and free energy calculation, the process of identifying potential lead molecules from natural products has been significantly expedited. In this study, we employed pharmacophore-based virtual screening and molecular simulations to identify natural product-based inhibitors of human progesterone receptor (PR) in breast cancer treatment. High-throughput molecular screening of traditional Chinese medicine (TCM) and zinc databases was performed, leading to the identification of potential lead compounds. The analysis of binding modes for the top five compounds from both database provides valuable structural insights into the inhibition of HPR for breast cancer treatment. The top five hits exhibited enhanced stability and compactness compared to the reference compound. In conclusion, our study provides valuable insights for identifying and refining lead compounds as HPR inhibitors.
Collapse
Affiliation(s)
- Muhammad Shahab
- State Key Laboratories of Chemical Resources Engineering, Beijing University of Chemical Technology, Beijing, 100029, People's Republic of China
| | - Peng Ziyu
- School of chemistry and chemical engineering, Wuhan University of Science and Technology, Wuhan, 430081, People's Republic of China
| | - Muhammad Waqas
- Natural and Medical Sciences Research Center, University of Nizwa, Birkat Al-Mouz, 616, Nizwa, Oman
| | - Guojun Zheng
- State Key Laboratories of Chemical Resources Engineering, Beijing University of Chemical Technology, Beijing, 100029, People's Republic of China.
| | - Yousef A Bin Jardan
- Department of Pharmaceutics, College of Pharmacy, King Saud University, P. O. BOX 2455, 11451, Riyadh, Saudi Arabia
| | | | - Mohammed Bouhrhia
- Laboratory of Biotechnology and Natural Resources Valorization, Faculty of Sciences, Ibn Zohr University, 80060, Agadir, Morocco
| |
Collapse
|
3
|
Siddiqui NF, Vishwakarma P, Thakur S, Jadhav HR. Bioactivity predictions and virtual screening using machine learning predictive model. J Biomol Struct Dyn 2024:1-20. [PMID: 38217308 DOI: 10.1080/07391102.2023.2300132] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/20/2023] [Accepted: 12/23/2023] [Indexed: 01/15/2024]
Abstract
Recently, there has been significant attention on machine learning algorithms for predictive modeling. Prediction models for enzyme inhibitors are limited, and it is essential to account for chemical biases while developing them. The lack of repeatability in available models and chemical bias issues constrain drug discovery and development. A new prediction model for enzyme inhibitors has been developed, and the model efficacy was checked using Dipeptidyl peptidase 4 (DPP-4) inhibitors. A Python script was prepared and can be provided for personal use upon request. Among various machine learning algorithms, it was found that Random Forest offers the best accuracy. Two models were compared, one with diverse training and test data and the other with a random split. It was concluded that machine learning predictive models based on the Murcko scaffold can address chemical bias concerns. In-silico screening of the Drug Bank database identified two molecules against DPP-4, which are previously proven hit molecules. The approach was further validated through molecular docking studies and molecular dynamics simulations, demonstrating the credibility and relevance of the developed model for future investigations and potential translation into clinical applications.Communicated by Ramaswamy H. Sarma.
Collapse
Affiliation(s)
- Noor Fatima Siddiqui
- Department of Pharmacy, Pharmaceutical Chemistry Research Laboratory, Birla Institute of Technology and Science Pilani, Pilani, RJ, India
| | - Pinky Vishwakarma
- Department of Pharmacy, Pharmaceutical Chemistry Research Laboratory, Birla Institute of Technology and Science Pilani, Pilani, RJ, India
| | - Shikha Thakur
- Department of Pharmacy, Pharmaceutical Chemistry Research Laboratory, Birla Institute of Technology and Science Pilani, Pilani, RJ, India
| | - Hemant R Jadhav
- Department of Pharmacy, Pharmaceutical Chemistry Research Laboratory, Birla Institute of Technology and Science Pilani, Pilani, RJ, India
| |
Collapse
|
4
|
Ojo OA, Adegboyega AE, Taiwo OA, Olowosoke CB, Johnson GI, Umedum NL, Onuh K, Adeduro MN, Nwobodo VO, Elekan AO, Alemika TE, Johnson TO. Lead optimization of Allium sativum L. compounds for PTP1B inhibition in diabetes treatment: in silico molecular docking and dynamics simulation. J Biomol Struct Dyn 2023:1-15. [PMID: 38109128 DOI: 10.1080/07391102.2023.2294179] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/14/2022] [Accepted: 12/01/2023] [Indexed: 12/19/2023]
Abstract
Protein tyrosine phosphatase 1B (PTP1B) has been identified as a promising drug target for the development of diabetes medications via an inhibition mechanism. Using a computational approach, this study investigates the binding mechanism of lead optimized natural compounds from Allium sativum against the human PTP1B. The molecular docking, induced-fit docking, and binding free energy calculations were analyzed using Schrödinger Suite 2021-2. MD simulation, and gene enrichment analysis was achieved via the Desmond module of Schrödinger to identify best compounds as inhibitors against PTP1B in diabetes management. The docking scores of the lead optimized compounds were good; 5280443_121 from apigenin had the best binding score of -9.345 kcal/mol, followed by 5280443_129 with a binding score of -9.200 kcal/mol, and 5280863_177 from kaempferol had a binding score of -8.528 kcal/mol, followed by 5280863_462 with a binding score of -8.338 kcal/mol. The top two lead optimized compounds, docked better than the standard PTP1B inhibitor (-7.155 kcal/mol), suggesting them as potent inhibitors than the standard PTP1B inhibitor. The outcomes of the induced-fit docking were consistent with the increased binding affinity used in the Glide computation of the five conformed poses between the derivatives (5280443_121, 5280443_129, 5280863_177, and 5280863_462) and the protein (PTP1B). Based on the binding fee energies (MM-GBSA), the lead optimized compounds from kaempferol exhibited more stability than those from apigenin. In the pharmacophore development, all the models exhibit good results across the different metrics. The best performing model with five of five matches on a 1.34 and 1.33 phase score was DDRRR_1, DDRRR_2, and DDDRR_1. The average BEDROC value (= 160.9) was 1, while the average EF 1% value across all models was 101. There were no substantial conformational modifications during the MD simulation process, indicating that the apigenin derivatives (5280443_121) was stable in the protein's active site in 100 ns. IGF1R, EGFR, INSR, PTPN1, SRC, JAK2, GRB2, BCAR1, and IRS1 are among the 11 potential targets found in the protein-protein interaction (PPI) of A. sativum against PTP1B that may be important in A. sativum's defense against PTP1B. Sixty-four (64) pathways were found by KEGG pathway enrichment analysis to be potentially involved in the anti-PTP1B of A. sativum. Consequently, data obtained indicates the effectiveness of the in silico studies in identifying potential lead compounds in A. sativum against PTP1B target.Communicated by Ramaswamy H. Sarma.
Collapse
Affiliation(s)
- Oluwafemi Adeleke Ojo
- Bowen University SDG 03 (Good Health and Wellbeing Research Group), Iwo, Nigeria
- Phytomedicine, Molecular Toxicology, and Computational Biochemistry Research Laboratory (PMTCB-RL), Department of Biochemistry, Bowen University, Iwo, Nigeria
| | - Abayomi Emmanuel Adegboyega
- Department of Biochemistry, Faculty of Basic Medical Sciences, University of Jos, Jos, Nigeria
- Jaris Computational Biology Centre, Jos, Nigeria
| | | | - Christopher Busayo Olowosoke
- Department of Biotechnology, Federal University of Technology, Ondo, Nigeria
- Department of Biotechnology, Chrisland University, Abeokuta, Nigeria
| | - Grace Inioluwa Johnson
- Jaris Computational Biology Centre, Jos, Nigeria
- College of Health Sciences, University of Jos, Jos, Nigeria
| | - Ngozi Lillian Umedum
- Department of Organic and Medicinal Chemistry, Nnamdi Azikwe University, Akwa, Anambra, Nigeria
| | - Kingsley Onuh
- Department of Biotechnology, Nigerian Defence Academy, Kaduna
| | - Mary Nneka Adeduro
- Department of Pharmaceutical Chemistry, Univervisty of Lagos, Lagos, Nigeria
| | | | - Ayodele O Elekan
- Department of Biochemistry, Adekunle Ajasin University, Ondo, Nigeria
| | | | - Titilayo Omolara Johnson
- Department of Biochemistry, Faculty of Basic Medical Sciences, University of Jos, Jos, Nigeria
- Jaris Computational Biology Centre, Jos, Nigeria
| |
Collapse
|
5
|
Guan Y, Wang Y, Fu X, Bai G, Li X, Mao J, Yan Y, Hu L. Multiple functions of stress granules in viral infection at a glance. Front Microbiol 2023; 14:1138864. [PMID: 36937261 PMCID: PMC10014870 DOI: 10.3389/fmicb.2023.1138864] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/06/2023] [Accepted: 02/08/2023] [Indexed: 03/05/2023] Open
Abstract
Stress granules (SGs) are distinct RNA granules induced by various stresses, which are evolutionarily conserved across species. In general, SGs act as a conservative and essential self-protection mechanism during stress responses. Viruses have a long evolutionary history and viral infections can trigger a series of cellular stress responses, which may interact with SG formation. Targeting SGs is believed as one of the critical and conservative measures for viruses to tackle the inhibition of host cells. In this systematic review, we have summarized the role of SGs in viral infection and categorized their relationships into three tables, with a particular focus on Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) infection. Moreover, we have outlined several kinds of drugs targeting SGs according to different pathways, most of which are potentially effective against SARS-CoV-2. We believe this review would offer a new view for the researchers and clinicians to attempt to develop more efficacious treatments for virus infection, particularly for the treatment of SARS-CoV-2 infection.
Collapse
Affiliation(s)
- Yuelin Guan
- The Children’s Hospital, Zhejiang University School of Medicine, National Clinical Research Center for Child Health, Hangzhou, China
| | - Yan Wang
- The Children’s Hospital, Zhejiang University School of Medicine, National Clinical Research Center for Child Health, Hangzhou, China
| | - Xudong Fu
- Center of Stem Cell and Regenerative Medicine, and Bone Marrow Transplantation Center of the First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China
- Zhejiang Laboratory for Systems and Precision Medicine, Zhejiang University Medical Center, Hangzhou, Zhejiang, China
| | - Guannan Bai
- The Children’s Hospital, Zhejiang University School of Medicine, National Clinical Research Center for Child Health, Hangzhou, China
| | - Xue Li
- Department of Big Data in Health Science School of Public Health and The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China
| | - Jianhua Mao
- The Children’s Hospital, Zhejiang University School of Medicine, National Clinical Research Center for Child Health, Hangzhou, China
| | - Yongbin Yan
- State Key Laboratory of Membrane Biology, School of Life Sciences, Tsinghua University, Beijing, China
- *Correspondence: Yongbin Yan,
| | - Lidan Hu
- The Children’s Hospital, Zhejiang University School of Medicine, National Clinical Research Center for Child Health, Hangzhou, China
- Lidan Hu,
| |
Collapse
|