1
|
Zia MP, Jain M, Muthukumaran J, Singh AK. Exploration of potential hit compounds targeting 1-deoxy-d-xylulose 5-phosphate reductoisomerase (IspC) from Acinetobacter baumannii: an in silico investigation. 3 Biotech 2024; 14:72. [PMID: 38362590 PMCID: PMC10864239 DOI: 10.1007/s13205-024-03923-w] [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: 07/11/2023] [Accepted: 01/07/2024] [Indexed: 02/17/2024] Open
Abstract
The emergence of carbapenem-resistant Acinetobacter baumannii, a highly concerning bacterial species designated as a Priority 1: Critical pathogen by the WHO, has become a formidable global threat. In this study, we utilised computational methods to explore the potent molecules capable of inhibiting the IspC enzyme, which plays a crucial role in the methylerythritol 4-phosphate (MEP) biosynthetic pathway. Employing high-throughput virtual screening of small molecules from the Enamine library, we focused on the highly conserved substrate binding site of the DXR target protein, resulting in the identification of 1000 potential compounds. Among these compounds, we selected the top two candidates (Z2615855584 and Z2206320703) based on Lipinski's rule of Five and ADMET filters, along with FR900098, a known IspC inhibitor, and DXP, the substrate of IspC, for molecular dynamics (MD) simulations. The MD simulation trajectories revealed remarkable structural and thermodynamic stability, as well as strong binding affinity, for all the IspC-ligand complexes. Furthermore, binding free energy calculations based on MM/PBSA (Molecular Mechanics/Poisson-Boltzmann Surface Area) methodology demonstrated significant interactions between the selected ligand molecules and IspC. Taking into consideration all the aforementioned criteria, we suggest Z2206320703 as the potent lead candidate against IspC. Supplementary Information The online version contains supplementary material available at 10.1007/s13205-024-03923-w.
Collapse
Affiliation(s)
- Mahrukh Parveez Zia
- Department of Biotechnology, School of Engineering and Technology, Sharda University, P.C. 201310, Greater Noida, Uttar Pradesh India
| | - Monika Jain
- Department of Biotechnology, School of Engineering and Technology, Sharda University, P.C. 201310, Greater Noida, Uttar Pradesh India
| | - Jayaraman Muthukumaran
- Department of Biotechnology, School of Engineering and Technology, Sharda University, P.C. 201310, Greater Noida, Uttar Pradesh India
| | - Amit Kumar Singh
- Department of Biotechnology, School of Engineering and Technology, Sharda University, P.C. 201310, Greater Noida, Uttar Pradesh India
| |
Collapse
|
2
|
Guo X, Lin Y, He F, Jin Y, Chen S, Li T, Wu C, Zhang L, Chen X. Identification of active compounds of traditional chinese medicine derived from maxing shigan decoction for COVID-19 treatment: a meta-analysis and in silico study. Expert Rev Anti Infect Ther 2023; 21:871-889. [PMID: 37481738 DOI: 10.1080/14787210.2023.2238899] [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: 03/01/2023] [Accepted: 07/03/2023] [Indexed: 07/25/2023]
Abstract
BACKGROUND Coronavirus 2019 (COVID-19) poses a serious threat to human health. In China, traditional Chinese medicine (TCM), mainly based on the Maxing Shigan decoction (MXSGD), is used in conjunction with western medicine to treat COVID-19. RESEARCH DESIGN AND METHODS We conducted a network meta-analysis to investigate whether MXSGD-related TCM combined with western medicine is more effective in treating COVID-19 compared to western medicine alone. Additionally, using network pharmacology, cross-docking, and molecular dynamics (MD) simulation to explore the potential active compounds and possible targets underlying the therapeutic effects of MXSGD-related TCM. RESULTS MXSGD-related TCM combined with western medicine was better for treating COVID-19 compared to western medicine alone. Network pharmacological analysis identified 43 shared ingredients in the MXSGD-related TCM prescriptions and 599 common target genes. Cross-docking of the 43 compounds with 154 proteins that matched these genes led to the identification of 60 proteins. Pathway profiling revealed that the active ingredients participated in multiple signaling pathways that contribute to their efficacy. Molecular docking and MD simulation demonstrated that MOL007214, the most promising molecule, could stably bind to the active site of SARS-CoV-2 3CLpro. CONCLUSION This study demonstrates the important role of MXSGD-related TCM in the treatment of COVID-19.
Collapse
Affiliation(s)
- Xiaodan Guo
- Fujian Provincial Key Laboratory of Innovative Drug Target Research and State Key Laboratory of Cellular Stress Biology, School of Pharmaceutical Sciences, Xiamen University, Xiamen, China
| | - Yihua Lin
- Department of Respiratory and Critical Care Medicine, School of Medicine, Xiamen University, Xiamen, China
| | - Fengming He
- Fujian Provincial Key Laboratory of Innovative Drug Target Research and State Key Laboratory of Cellular Stress Biology, School of Pharmaceutical Sciences, Xiamen University, Xiamen, China
| | - Ying Jin
- Department of Cardiology, Xiamen Key Laboratory of Cardiac Electrophysiology, Xiamen Institute of Cardiovascular Diseases, School of Medicine, Xiamen University, Xiamen, China
| | - Simian Chen
- Fujian Provincial Key Laboratory of Innovative Drug Target Research and State Key Laboratory of Cellular Stress Biology, School of Pharmaceutical Sciences, Xiamen University, Xiamen, China
| | - Ting Li
- Fujian Provincial Key Laboratory of Innovative Drug Target Research and State Key Laboratory of Cellular Stress Biology, School of Pharmaceutical Sciences, Xiamen University, Xiamen, China
| | - Caisheng Wu
- Fujian Provincial Key Laboratory of Innovative Drug Target Research and State Key Laboratory of Cellular Stress Biology, School of Pharmaceutical Sciences, Xiamen University, Xiamen, China
| | - Lin Zhang
- School of Population Medicine and Public Health, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Xueqin Chen
- Department of Traditional Chinese Medicine, School of Medicine, Xiamen University, Xiamen, China
| |
Collapse
|
3
|
Parveez Zia M, Singh E, Jain M, Muthukumaran J, Singh AK. Structural and functional characterization of 1-deoxy-D-xylulose-5-phosphate synthase (DXS) from Acinetobacter baumannii: identification of promising lead molecules from virtual screening, molecular docking and molecular dynamics simulations. J Biomol Struct Dyn 2023; 41:11598-11611. [PMID: 36752319 DOI: 10.1080/07391102.2023.2174598] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/26/2022] [Accepted: 12/27/2022] [Indexed: 02/09/2023]
Abstract
The advent of multi drug resistance and extensive-drug resistance among various pathogens has caused a rise in nosocomial infections, which in turn has led to rising hospital-acquired infection-related mortality rates. Amongst them, carbapenem-resistant Acinetobacter baumannii is one of the most notorious bacterial species, categorized as a Priority 1: Critical pathogen by the WHO. Therefore, the discovery and development of novel antibiotics, as well as the identification of potential inhibitors, have become the need of the hour. In this study, we have employed computational methods to explore and identify molecules capable of inhibiting enzymes essential in the methylerythritol 4-phosphate (MEP) biosynthetic pathway. The high throughput virtual screening of small molecules (Enamine Advanced Collection (AC) library) against the highly conserved substrate-binding site of the DXS target protein provided us with a total of 1000 molecules. The top four potential candidate molecules, namely-Z3353989070, Z3353989049, Z2295848528, and Z1685501455, alongside fluoropyruvate (control), a known inhibitor of DXS, was chosen for a molecular dynamic simulation study. The molecular dynamic simulation trajectories suggested high structural and thermodynamical stability and strong binding affinity of all the DXS-ligand complexes. Moreover, the MM/PBSA-based binding free energy calculations also exhibited strong interactions of the selected ligand molecules with DXS. In conclusion, we have found that all four molecules displayed better results and stronger binding affinity than the control. In the end, based on all the above-mentioned criteria, we have proposed Z3353989049 to be the promising lead candidate against DXS from A. baumannii.Communicated by Ramaswamy H. Sarma.
Collapse
Affiliation(s)
- Mahrukh Parveez Zia
- Department of Biotechnology, School of Engineering and Technology, Sharda University, Greater Noida, Uttar Pradesh, India
| | - Ekampreet Singh
- Department of Biotechnology, School of Engineering and Technology, Sharda University, Greater Noida, Uttar Pradesh, India
| | - Monika Jain
- Department of Biotechnology, School of Engineering and Technology, Sharda University, Greater Noida, Uttar Pradesh, India
| | - Jayaraman Muthukumaran
- Department of Biotechnology, School of Engineering and Technology, Sharda University, Greater Noida, Uttar Pradesh, India
| | - Amit Kumar Singh
- Department of Biotechnology, School of Engineering and Technology, Sharda University, Greater Noida, Uttar Pradesh, India
| |
Collapse
|
4
|
Parthiban A, Sachithanandam V, Sarangapany S, Misra R, Muthukrishnan P, Jeyakumar TC, Purvaja R, Ramesh R. Green synthesis of gold nanoparticles using quercetin biomolecule from mangrove plant, Ceriops tagal: Assessment of antiproliferative properties, cellular uptake and DFT studies. J Mol Struct 2023. [DOI: 10.1016/j.molstruc.2022.134167] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
|
5
|
Gao K, Wang R, Chen J, Cheng L, Frishcosy J, Huzumi Y, Qiu Y, Schluckbier T, Wei X, Wei GW. Methodology-Centered Review of Molecular Modeling, Simulation, and Prediction of SARS-CoV-2. Chem Rev 2022; 122:11287-11368. [PMID: 35594413 PMCID: PMC9159519 DOI: 10.1021/acs.chemrev.1c00965] [Citation(s) in RCA: 31] [Impact Index Per Article: 15.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/06/2023]
Abstract
Despite tremendous efforts in the past two years, our understanding of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), virus-host interactions, immune response, virulence, transmission, and evolution is still very limited. This limitation calls for further in-depth investigation. Computational studies have become an indispensable component in combating coronavirus disease 2019 (COVID-19) due to their low cost, their efficiency, and the fact that they are free from safety and ethical constraints. Additionally, the mechanism that governs the global evolution and transmission of SARS-CoV-2 cannot be revealed from individual experiments and was discovered by integrating genotyping of massive viral sequences, biophysical modeling of protein-protein interactions, deep mutational data, deep learning, and advanced mathematics. There exists a tsunami of literature on the molecular modeling, simulations, and predictions of SARS-CoV-2 and related developments of drugs, vaccines, antibodies, and diagnostics. To provide readers with a quick update about this literature, we present a comprehensive and systematic methodology-centered review. Aspects such as molecular biophysics, bioinformatics, cheminformatics, machine learning, and mathematics are discussed. This review will be beneficial to researchers who are looking for ways to contribute to SARS-CoV-2 studies and those who are interested in the status of the field.
Collapse
Affiliation(s)
- Kaifu Gao
- Department
of Mathematics, Michigan State University, East Lansing, Michigan 48824, United States
| | - Rui Wang
- Department
of Mathematics, Michigan State University, East Lansing, Michigan 48824, United States
| | - Jiahui Chen
- Department
of Mathematics, Michigan State University, East Lansing, Michigan 48824, United States
| | - Limei Cheng
- Clinical
Pharmacology and Pharmacometrics, Bristol
Myers Squibb, Princeton, New Jersey 08536, United States
| | - Jaclyn Frishcosy
- Department
of Mathematics, Michigan State University, East Lansing, Michigan 48824, United States
| | - Yuta Huzumi
- Department
of Mathematics, Michigan State University, East Lansing, Michigan 48824, United States
| | - Yuchi Qiu
- Department
of Mathematics, Michigan State University, East Lansing, Michigan 48824, United States
| | - Tom Schluckbier
- Department
of Mathematics, Michigan State University, East Lansing, Michigan 48824, United States
| | - Xiaoqi Wei
- Department
of Mathematics, Michigan State University, East Lansing, Michigan 48824, United States
| | - Guo-Wei Wei
- Department
of Mathematics, Michigan State University, East Lansing, Michigan 48824, United States
- Department
of Electrical and Computer Engineering, Michigan State University, East Lansing, Michigan 48824, United States
- Department
of Biochemistry and Molecular Biology, Michigan
State University, East Lansing, Michigan 48824, United States
| |
Collapse
|