Integrated Machine Learning and Chemoinformatics-Based Screening of Mycotic Compounds against Kinesin Spindle ProteinEg5 for Lung Cancer Therapy.

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.

Identification of potent Antigen 85C inhibitors of Mycobacterium tuberculosis via in-house lichen library and binding free energy studies Part-II.

Tuberculosis remains the cause of mortality throughout the world. Currently, the available anti-tubercular drugs are not effective because of the existence of Multi-Drug resistant tuberculosis (MDR-TB) and Extensively-Drug resistant tuberculosis (XDR-TB). It has, therefore, become necessary to develop novel drugs that inhibit the activity of drug-resistant Mycobacterium tuberculosis. Due to the existence of MDR and XDR-TB, Mtb Ag85C has risen out as a propitious molecular drug target as it has importance in the synthesis of main components of the Mtb cell envelope which are essential for the virulence and survival of Mtb. In a previous paper, we studied a potential drug target by virtual high throughput screening of compounds and in continuation of the study on Mtb Ag85C, we further studied the role of lichen compounds in the inhibition of Ag85C. In the current research work, virtual screening of a lichen compounds library was performed against Ag85C. Further, ADMET analysis was employed to filter out the screened lichen compounds. Bioactivity score and toxicity prediction finalized four lichen compounds i.e. Portentol, Aspicilin, Parietinic acid and Polyporic acid as potential inhibitors of Ag85C. The stability and dynamic behavior of four compounds were analyzed by using Molecular dynamics simulation which indicated that they may be potential inhibitors of Ag85C. Therefore, based on the above results, Portentol, Aspicilin, Parietinic acid and Polyporic acid may be potential drug candidates against Mtb. We suggest that the use of these compounds can minimize the treatment time-period and the various side effects associated with the currently available anti-tubercular drugs.

Identification of potential Mycolyltransferase Ag85C inhibitors of Mycobacterium tuberculosis H37Rv via Virtual High Throughput Screening and Binding free energy studies.

Tuberculosis (TB) is a deadly disease which causes millions of death annually worldwide. Although TB is treatable but the rise of cases of multidrug-resistant and totally drug-resistant strains of Mycobacterium tuberculosis (Mtb) poses a great challenge to cure TB completely and this situation demands an urgent need for development of potential anti-tubercular drugs. In this regard, the antigen 85C (Ag85C) has emerged as an essential mycobacterial drug target as it plays a central role in synthesizing major components of the inner and outer layers of outer membrane of Mtb. In this research, we have identified four novel potential inhibitors as a potent inhibitor of the Mtb Ag85C from CHEMBL24, MolPort, Zinc and PubChem library by High Throughput Virtual Screening. The results of molecular dynamics show that these compounds bind to Ag85C protein with high stability. The ADMET profiling and pharmacophore analysis indicate that these compounds may act as potential anti-mycobacterial candidates. On the basis of findings our work, we propose that these compounds are novel potential inhibitors of Mtb Ag85C with similar or better properties than the classic inhibitor and they can potentially shorten the treatment duration and may have anti-mycobacterial activity against drug-resistant Mtb strains.

Virtual screening of natural compounds as inhibitors of EGFR 696-1022 T790M associated with non-small cell lung cancer.

Non-small cell lung cancer (NSCLC) is the most dominating and lethal type of lung cancer triggering more than 1.3 million deaths per year. The most effective line of treatment against NSCLC is to target epidermal growth factor receptor (EGFR) activating mutation. The present study aims to identify the novel anti-lung cancer compounds form nature against EGFR 696-1022 T790M by using in silico approaches. A library of 419 compounds from several natural resources was subjected to pre-screen through machine learning model using Random Forest classifier resulting 63 screened molecules with active potential. These molecules were further screened by molecular docking against the active site of EGFR 696-1022 T790M protein using AutoDock Vina followed by rescoring using X-Score. As a result 4 compounds were finally screened namely Granulatimide, Danorubicin, Penicinoline and Austocystin D with lowest binding energy which were -6.5 kcal/mol, -6.1 kcal/mol, -6.3 kcal/mol and -7.1 kcal/mol respectively. The drug likeness of the screened compounds was evaluated using FaF-Drug3 server. Finally toxicity of the hit compounds was predicted in cell line using the CLC-Pred server where their cytotoxic ability against various lung cancer cell lines was confirmed. We have shown 4 potential compounds, which could be further exploited as efficient drug candidates against lung cancer.