Integrated machine learning and chemoinformatics-based screening of mycotic compounds against kinesin spindle proteinEg5 for lung cancer therapy
| العنوان: | Integrated machine learning and chemoinformatics-based screening of mycotic compounds against kinesin spindle proteinEg5 for lung cancer therapy |
|---|---|
| المؤلفون: | Priyanka Maiti, Priyanka Sharma, Mahesha Nand, Indra D. Bhatt, Muthannan Andavar Ramakrishnan, Shalini Mathpal, Tushar Joshi, Ragini Pant, Shafi Mahmud, Jesus Simal-Gandara, Sultan Alshehri, Mohammed M. Ghoneim, Maha Alruwaily, Ahmed Abdullah Al Awadh, Mohammed Merae Alshahrani, Subhash Chandra |
| المصدر: | Investigo. Repositorio Institucional de la Universidade de Vigo Universidade de Vigo (UVigo) Molecules; Volume 27; Issue 5; Pages: 1639 |
| بيانات النشر: | Molecules, 2022. |
| سنة النشر: | 2022 |
| مصطلحات موضوعية: | Molecular Docking Simulation, Chemistry (miscellaneous), 3208 Farmacodinámica, Eg5, lung cancer, secondary metabolites, fungi, machine learning, molecular docking, Indian Himalayan Region, Organic Chemistry, Drug Discovery, 3205.08 Enfermedades Pulmonares, Molecular Medicine, Pharmaceutical Science, Physical and Theoretical Chemistry, 3207.03 Carcinogénesis, Analytical Chemistry |
| الوصف: | Among the various types of cancer, lung cancer is the second most-diagnosed cancer worldwide. The kinesin spindle protein, Eg5, is a vital protein behind bipolar mitotic spindle establishment and maintenance during mitosis. Eg5 has been reported to contribute to cancer cell migration and angiogenesis impairment and has no role in resting, non-dividing cells. Thus, it could be considered as a vital target against several cancers, such as renal cancer, lung cancer, urothelial carcinoma, prostate cancer, squamous cell carcinoma, etc. In recent years, fungal secondary metabolites from the Indian Himalayan Region (IHR) have been identified as an important lead source in the drug development pipeline. Therefore, the present study aims to identify potential mycotic secondary metabolites against the Eg5 protein by applying integrated machine learning, chemoinformatics based in silico-screening methods and molecular dynamic simulation targeting lung cancer. Initially, a library of 1830 mycotic secondary metabolites was screened by a predictive machine-learning model developed based on the random forest algorithm with high sensitivity (1) and an ROC area of 0.99. Further, 319 out of 1830 compounds screened with active potential by the model were evaluated for their drug-likeness properties by applying four filters simultaneously, viz., Lipinski’s rule, CMC-50 like rule, Veber rule, and Ghose filter. A total of 13 compounds passed from all the above filters were considered for molecular docking, functional group analysis, and cell line cytotoxicity prediction. Finally, four hit mycotic secondary metabolites found in fungi from the IHR were screened viz., (−)-Cochlactone-A, Phelligridin C, Sterenin E, and Cyathusal A. All compounds have efficient binding potential with Eg5, containing functional groups like aromatic rings, rings, carboxylic acid esters, and carbonyl and with cell line cytotoxicity against lung cancer cell lines, namely, MCF-7, NCI-H226, NCI-H522, A549, and NCI H187. Further, the molecular dynamics simulation study confirms the docked complex rigidity and stability by exploring root mean square deviations, root mean square fluctuations, and radius of gyration analysis from 100 ns simulation trajectories. The screened compounds could be used further to develop effective drugs against lung and other types of cancer. |
| وصف الملف: | application/pdf |
| الوصول الحر: | https://explore.openaire.eu/search/publication?articleId=doi_dedup___::992c0b3b84ba1df593f026819de77eb0Test http://hdl.handle.net/11093/3249Test |
| حقوق: | OPEN |
| رقم الانضمام: | edsair.doi.dedup.....992c0b3b84ba1df593f026819de77eb0 |
| قاعدة البيانات: | OpenAIRE |
| الوصف غير متاح. |