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Shahzadi A, Ashfaq UA, Khurshid M, Nisar MA, Syed A, Bahkali AH. Deciphering Multi-target Pharmacological Mechanism of Cucurbita pepo Seeds against Kidney Stones: Network Pharmacology and Molecular Docking Approach. Curr Pharm Des 2024; 30:295-309. [PMID: 38213175 DOI: 10.2174/0113816128271781231104151155] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/12/2023] [Revised: 09/17/2023] [Accepted: 10/03/2023] [Indexed: 01/13/2024]
Abstract
BACKGROUND Urolithiasis is a prevalent condition with significant morbidity and economic implications. The economic burden associated with urolithiasis primarily stems from medical expenses. Previous literature suggests that herbal plants, including Cucurbita pepo, have lithotriptic capabilities. C. pepo is an annual, herbaceous, widely grown, and monoecious vegetative plant known for its antioxidants, fibers, and fatty acids. Recent studies on C. pepo seeds have shown therapeutic potential in reducing bladder stones and urodynamic illnesses, like kidney stones. However, the precise molecular and pharmacological mechanisms are unclear. OBJECTIVE In this research, we employed network pharmacology and molecular docking to examine the active compounds and biological mechanisms of Cucurbita pepo against kidney stones. METHODS Active constituents were obtained from previous studies and the IMPPAT database, with their targets predicted using Swiss target prediction. Kidney stone-associated genes were collected from DisGeNET and GeneCards. The active constituent-target-pathway network was constructed using Cytoscape, and the target protein-protein interaction network was generated using the STRING database. Gene enrichment analysis of C. pepo core targets was conducted using DAVID. Molecular docking was performed to identify potential kidney stone-fighting agents. RESULTS The findings revealed that Cucurbita pepo contains 18 active components and has 192 potential gene targets, including AR, EGFR, ESR1, AKT1, MAPK3, SRC, and MTOR. Network analysis demonstrated that C. pepo seeds may prevent kidney stones by influencing disease-related signaling pathways. Molecular docking indicated that key kidney stone targets (mTOR, EGFR, AR, and ESR1) effectively bind with active constituents of C. pepo. CONCLUSION These findings provide insight into the anti-kidney stone effects of Cucurbita pepo at a molecular level. In conclusion, this study contributes to understanding the potential of Cucurbita pepo in combating kidney stones and lays the foundation for further research.
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Affiliation(s)
- Aqsa Shahzadi
- Department of Bioinformatics and Biotechnology, Government College University Faisalabad, Faisalabad, Pakistan
| | - Usman Ali Ashfaq
- Department of Bioinformatics and Biotechnology, Government College University Faisalabad, Faisalabad, Pakistan
| | - Mohsin Khurshid
- Institute of Microbiology, Government College University Faisalabad, Faisalabad, Pakistan
| | - Muhammad Atif Nisar
- College of Science and Engineering, Flinders University, Bedford Park 5042, Australia
| | - Asad Syed
- Department of Botany and Microbiology, College of Science, King Saud University, P.O. 2455, Riyadh 11451, Saudi Arabia
| | - Ali H Bahkali
- Department of Botany and Microbiology, College of Science, King Saud University, P.O. 2455, Riyadh 11451, Saudi Arabia
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Srinivas R, Verma N, Kraka E, Larson EC. Deep Learning-Based Ligand Design Using Shared Latent Implicit Fingerprints from Collaborative Filtering. J Chem Inf Model 2021; 61:2159-2174. [PMID: 33899481 DOI: 10.1021/acs.jcim.0c01355] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
Abstract
In their previous work, Srinivas et al. [ J. Cheminf. 2018, 10, 56] have shown that implicit fingerprints capture ligands and proteins in a shared latent space, typically for the purposes of virtual screening with collaborative filtering models applied on known bioactivity data. In this work, we extend these implicit fingerprints/descriptors using deep learning techniques to translate latent descriptors into discrete representations of molecules (SMILES), without explicitly optimizing for chemical properties. This allows the design of new compounds based upon the latent representation of nearby proteins, thereby encoding druglike properties including binding affinities to known proteins. The implicit descriptor method does not require any fingerprint similarity search, which makes the method free of any bias arising from the empirical nature of the fingerprint models [Srinivas, R.; J. Cheminf. 2018, 10, 56]. We evaluate the properties of the potentially novel drugs generated by our approach using physical properties of druglike molecules and chemical complexity. Additionally, we analyze the reliability of the biological activity of the new compounds generated using this method by employing models of protein-ligand interaction, which assists in assessing the potential binding affinity of the designed compounds. We find that the generated compounds exhibit properties of chemically feasible compounds and are predicted to be excellent binders to known proteins. Furthermore, we also analyze the diversity of compounds created using the Tanimoto distance and conclude that there is a wide diversity in the generated compounds.
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Affiliation(s)
- Raghuram Srinivas
- Department of Computer Science, Southern Methodist University, Dallas, Texas 75205, United States
| | - Niraj Verma
- Department of Chemistry, Southern Methodist University, Dallas, Texas 75205, United States
| | - Elfi Kraka
- Department of Chemistry, Southern Methodist University, Dallas, Texas 75205, United States
| | - Eric C Larson
- Department of Computer Science, Southern Methodist University, Dallas, Texas 75205, United States
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Computer-Aided Drug Designing. Adv Bioinformatics 2021. [DOI: 10.1007/978-981-33-6191-1_9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/20/2022] Open
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Zhang X, Shen T, Zhou X, Tang X, Gao R, Xu L, Wang L, Zhou Z, Lin J, Hu Y. Network pharmacology based virtual screening of active constituents of Prunella vulgaris L. and the molecular mechanism against breast cancer. Sci Rep 2020; 10:15730. [PMID: 32978480 PMCID: PMC7519149 DOI: 10.1038/s41598-020-72797-8] [Citation(s) in RCA: 35] [Impact Index Per Article: 8.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/02/2019] [Accepted: 09/04/2020] [Indexed: 02/08/2023] Open
Abstract
Prunella vulgaris L, a perennial herb widely used in Asia in the treatment of various diseases including cancer. In vitro studies have demonstrated the therapeutic effect of Prunella vulgaris L. against breast cancer through multiple pathways. However, the nature of the biological mechanisms remains unclear. In this study, a Network pharmacology based approach was used to explore active constituents and potential molecular mechanisms of Prunella vulgaris L. for the treatment of breast cancer. The methods adopted included active constituents prescreening, target prediction, GO and KEGG pathway enrichment analysis. Molecular docking experiments were used to further validate network pharmacology results. The predicted results showed that there were 19 active ingredients in Prunella vulgaris L. and 31 potential gene targets including AKT1, EGFR, MYC, and VEGFA. Further, analysis of the potential biological mechanisms of Prunella vulgaris L. against breast cancer was performed by investigating the relationship between the active constituents, target genes and pathways. Network analysis showed that Prunella vulgaris L. exerted a promising preventive effect on breast cancer by acting on tumor-associated signaling pathways. This provides a basis to understand the mechanism of the anti-breast cancer activity of Prunella vulgaris L.
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Affiliation(s)
- Xiaobo Zhang
- School of Basic Medicine, Chengdu University of Traditional Chinese Medicine, Chengdu, 611137, China
| | - Tao Shen
- School of Basic Medicine, Chengdu University of Traditional Chinese Medicine, Chengdu, 611137, China.
| | - Xin Zhou
- School of Basic Medicine, Chengdu University of Traditional Chinese Medicine, Chengdu, 611137, China
| | - Xuehua Tang
- Academic Department, Zhuhai Ebang Pharmaceutical Co., Ltd, Zhuhai, 519040, China
| | - Rui Gao
- School of Basic Medicine, Chengdu University of Traditional Chinese Medicine, Chengdu, 611137, China
| | - Lu Xu
- School of Basic Medicine, Chengdu University of Traditional Chinese Medicine, Chengdu, 611137, China
| | - Long Wang
- School of Basic Medicine, Chengdu University of Traditional Chinese Medicine, Chengdu, 611137, China
| | - Zubin Zhou
- School of Basic Medicine, Chengdu University of Traditional Chinese Medicine, Chengdu, 611137, China
| | - Jingjing Lin
- School of Basic Medicine, Chengdu University of Traditional Chinese Medicine, Chengdu, 611137, China
| | - Yuanzhang Hu
- College of Information Engineering, Chengdu University of Traditional Chinese Medicine, Chengdu, 611137, China
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Abstract
Quantum mechanics (QM) methods provide a fine description of receptor-ligand interactions and of chemical reactions. Their use in drug design and drug discovery is increasing, especially for complex systems including metal ions in the binding sites, for the design of highly selective inhibitors, for the optimization of bi-specific compounds, to understand enzymatic reactions, and for the study of covalent ligands and prodrugs. They are also used for generating molecular descriptors for predictive QSAR/QSPR models and for the parameterization of force fields. Thanks to the continuous increase of computational power offered by GPUs and to the development of sophisticated algorithms, QM methods are becoming part of the standard tools used in computer-aided drug design (CADD). We present the most used QM methods and software packages, and we discuss recent representative applications in drug design and drug discovery.
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Affiliation(s)
- Martin Kotev
- Global Research Informatics/Cheminformatics and Drug Design, Evotec (France) SAS, Toulouse, France
| | - Laurie Sarrat
- Global Research Informatics/Cheminformatics and Drug Design, Evotec (France) SAS, Toulouse, France
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Bruno A, Costantino G, Sartori L, Radi M. The In Silico Drug Discovery Toolbox: Applications in Lead Discovery and Optimization. Curr Med Chem 2019; 26:3838-3873. [PMID: 29110597 DOI: 10.2174/0929867324666171107101035] [Citation(s) in RCA: 29] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/07/2017] [Revised: 09/27/2017] [Accepted: 09/28/2017] [Indexed: 01/04/2023]
Abstract
BACKGROUND Discovery and development of a new drug is a long lasting and expensive journey that takes around 20 years from starting idea to approval and marketing of new medication. Despite R&D expenditures have been constantly increasing in the last few years, the number of new drugs introduced into market has been steadily declining. This is mainly due to preclinical and clinical safety issues, which still represent about 40% of drug discontinuation. To cope with this issue, a number of in silico techniques are currently being used for an early stage evaluation/prediction of potential safety issues, allowing to increase the drug-discovery success rate and reduce costs associated with the development of a new drug. METHODS In the present review, we will analyse the early steps of the drug-discovery pipeline, describing the sequence of steps from disease selection to lead optimization and focusing on the most common in silico tools used to assess attrition risks and build a mitigation plan. RESULTS A comprehensive list of widely used in silico tools, databases, and public initiatives that can be effectively implemented and used in the drug discovery pipeline has been provided. A few examples of how these tools can be problem-solving and how they may increase the success rate of a drug discovery and development program have been also provided. Finally, selected examples where the application of in silico tools had effectively contributed to the development of marketed drugs or clinical candidates will be given. CONCLUSION The in silico toolbox finds great application in every step of early drug discovery: (i) target identification and validation; (ii) hit identification; (iii) hit-to-lead; and (iv) lead optimization. Each of these steps has been described in details, providing a useful overview on the role played by in silico tools in the decision-making process to speed-up the discovery of new drugs.
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Affiliation(s)
- Agostino Bruno
- Experimental Therapeutics Unit, IFOM - The FIRC Institute for Molecular Oncology Foundation, Via Adamello 16 - 20139 Milano, Italy
| | - Gabriele Costantino
- Dipartimento di Scienze degli Alimenti e del Farmaco, Universita degli Studi di Parma, Viale delle Scienze, 27/A, 43124 Parma, Italy
| | - Luca Sartori
- Experimental Therapeutics Unit, IFOM - The FIRC Institute for Molecular Oncology Foundation, Via Adamello 16 - 20139 Milano, Italy
| | - Marco Radi
- Dipartimento di Scienze degli Alimenti e del Farmaco, Universita degli Studi di Parma, Viale delle Scienze, 27/A, 43124 Parma, Italy
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Srinivas R, Klimovich PV, Larson EC. Implicit-descriptor ligand-based virtual screening by means of collaborative filtering. J Cheminform 2018; 10:56. [PMID: 30467684 PMCID: PMC6755561 DOI: 10.1186/s13321-018-0310-y] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/26/2018] [Accepted: 11/13/2018] [Indexed: 12/20/2022] Open
Abstract
Current ligand-based machine learning methods in virtual screening rely heavily on molecular fingerprinting for preprocessing, i.e., explicit description of ligands’ structural and physicochemical properties in a vectorized form. Of particular importance to current methods are the extent to which molecular fingerprints describe a particular ligand and what metric sufficiently captures similarity among ligands. In this work, we propose and evaluate methods that do not require explicit feature vectorization through fingerprinting, but, instead, provide implicit descriptors based only on other known assays. Our methods are based upon well known collaborative filtering algorithms used in recommendation systems. Our implicit descriptor method does not require any fingerprint similarity search, which makes the method free of the bias arising from the empirical nature of the fingerprint models. We show that implicit methods significantly outperform traditional machine learning methods, and the main strengths of implicit methods are their resilience to target-ligand sparsity and high potential for spotting promiscuous ligands.
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Affiliation(s)
- Raghuram Srinivas
- Department of Computer Science and Engineering, Bobby B. Lyle School of Engineering, Southern Methodist University, 3145 Dyer Street, Dallas, TX, 75205, USA. .,DataScience@SMU, Dallas, 75205, TX, USA.
| | - Pavel V Klimovich
- Department of Computer Science and Engineering, Bobby B. Lyle School of Engineering, Southern Methodist University, 3145 Dyer Street, Dallas, TX, 75205, USA.,The Dedman College Interdisciplinary Institute, 3225 Daniel Avenue, Dallas, TX, 75205, USA
| | - Eric C Larson
- Department of Computer Science and Engineering, Bobby B. Lyle School of Engineering, Southern Methodist University, 3145 Dyer Street, Dallas, TX, 75205, USA
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