Deep Learning and Structure-Based Virtual Screening for Drug Discovery against NEK7: A Novel Target for the Treatment of Cancer (original) (raw)
Related papers
Journal of Biomolecular Structure and Dynamics
NEK7 plays a crucial role in many signaling pathways and contributes to a variety of cancers. Therefore, NEK7 has long been considered an attractive drug target in anti-cancer drug discovery. However, only a few efforts have been made in development of NEK7 inhibitors with selectivity. In the present study, we have investigated FDA approved kinase inhibitors as potential NEK7 inhibitors. Although more than 200 FDA approved drugs are available but none is known to inhibit NEK7 protein. These ndings motivated us to design in-silico approach for investigation and identi cation of NEK7 protein. In the current study, Structure-based virtual screening and molecular docking were carried out to identify potential NEK7 inhibitors. Dacomitinib and Neratinib was selected depending upon their potential activities against various cancer cell lines. These drugs were subjected to density functional theory calculations which demonstrated the chemical reactivity of both drugs. Furthermore, molecular docking studies were conducted using Molecular Operating environment 2015.10 and conformations with high docking scores and strong interactions were selected for further analysis. Absorption, distribution, metabolism, elimination and toxicity (ADMET) pro le evaluation was also carried out to ascertain toxicity pro le of both drugs. The proposed inhibitorprotein complexes were further subjected to Molecular Dynamics (MD) Simulations studies involving root-mean-square deviation and root-mean-square uctuation to explore the binding mode stability inside active pockets. Finally, both drugs were obtained as potential inhibitors of NEK7 protein. All these analyses provide a reference for the further development of NEk7 inhibitors.
Identification of potent inhibitors of NEK7 protein using a comprehensive computational approach
Scientific Reports
NIMA related Kinases (NEK7) plays an important role in spindle assembly and mitotic division of the cell. Over expression of NEK7 leads to the progression of different cancers and associated malignancies. It is becoming the next wave of targets for the development of selective and potent anti-cancerous agents. The current study is the first comprehensive computational approach to identify potent inhibitors of NEK7 protein. For this purpose, previously identified anti-inflammatory compound i.e., Phenylcarbamoylpiperidine-1,2,4-triazole amide derivatives by our own group were selected for their anti-cancer potential via detailed Computational studies. Initially, the density functional theory (DFT) calculations were carried out using Gaussian 09 software which provided information about the compounds' stability and reactivity. Furthermore, Autodock suite and Molecular Operating Environment (MOE) software’s were used to dock the ligand database into the active pocket of the NEK7 pro...
The Scientific World Journal, 2014
Nek6 is a member of the NIMA (never in mitosis, gene A)-related serine/threonine kinase family that plays an important role in the initiation of mitotic cell cycle progression. This work is an attempt to emphasize the structural and functional relationship of Nek6 protein based on homology modeling and binding pocket analysis. The three-dimensional structure of Nek6 was constructed by molecular modeling studies and the best model was further assessed by PROCHECK, ProSA, and ERRAT plot in order to analyze the quality and consistency of generated model. The overall quality of computed model showed 87.4% amino acid residues under the favored region. A 3 ns molecular dynamics simulation confirmed that the structure was reliable and stable. Two lead compounds (Binding database ID: 15666, 18602) were retrieved through structure-based virtual screening and induced fit docking approaches as novel Nek6 inhibitors. Hence, we concluded that the potential compounds may act as new leads for Nek6 inhibitors designing.
Discovery of potential ALK inhibitors by virtual screening approach
3 Biotech, 2016
Crizotinib is an anticancer drug used for the treatment of non-small cell lung cancer. Evidences available suggest that there is a development of an acquired resistance against crizotinib action due to the emergence of several mutations in the ALK gene. It is therefore necessary to develop potent anti-cancer drugs for the treatment of crizotinib resistance non-small cell lung cancer types. In the present study, a novel class of lead molecule was identified using virtual screening, molecular docking and molecular dynamic approach. The virtual screening analysis was done using PubChem database by employing crizotinib as query and the data reduction was carried out by using molecular docking techniques. The bioavailability of the lead compounds was examined with the help of Lipinski rule of five. The screened lead molecules were analyzed for toxicity profiles, drug-likeness and other physico-chemical properties of drugs by OSIRIS program. Finally, molecular dynamics simulation was also performed to validate the binding property of the lead compound. Our analysis clearly indicates that CID 11562217, a nitrile containing compound (pyrazole-substituted aminoheteroaryl), could be the potential ALK inhibitor certainly helpful to overcome the drug resistance in non-small cell lung cancer.
Deep Docking - a Deep Learning Approach for Virtual Screening of Big Chemical Datasets
2019
Drug discovery is an extensive and rigorous process that requires up to 2 billion dollars of investments and more than ten years of research and development to bring a molecule "from bench to a bedside". While virtual screening can significantly enhance drug discovery workflow, it ultimately lags the current rate of expansion of chemical databases that already incorporate billions of purchasable compounds. This surge of available small molecules presents great opportunities for drug discovery but also demands for faster virtual screening methods and protocols. In order to address this challenge, we herein introduce Deep Docking (D 2)-a novel deep learning-based approach which is suited for docking billions of molecular structures. The developed D 2-platform utilizes quantitative structure-activity relationship (QSAR) based deep models trained on docking scores of subsets of a large chemical library (Big Base) to approximate the docking outcome for yet unprocessed molecular entries and to remove unfavorable structures in an iterative manner. We applied D 2 to virtually screen 1.36 billion molecules form the ZINC15 library against 12 prominent target proteins, and demonstrated up to 100-fold chemical data reduction and 6,000-fold enrichment for top hits, without notable loss of well-docked entities. The developed D 2 protocol can readily be used in conjunction with any docking program and was made publicly available. Drug discovery is an expensive and time-demanding process that faces many challenges, including low hit rates of high-throughput screening approaches among many others 1-2. Methods of computer-aided drug discovery (CADD) can significantly speed up the pace of screening and help improving hit discovery rates 3. Molecular docking is routinely used to process virtual libraries containing millions of molecular structures against a variety of drug targets with known three- .
Structure-Based Virtual Screening for Drug Discovery: a Problem-Centric Review
The AAPS Journal, 2012
Structure-based virtual screening (SBVS) has been widely applied in early-stage drug discovery. From a problem-centric perspective, we reviewed the recent advances and applications in SBVS with a special focus on docking-based virtual screening. We emphasized the researchers' practical efforts in real projects by understanding the ligand-target binding interactions as a premise. We also highlighted the recent progress in developing target-biased scoring functions by optimizing current generic scoring functions toward certain target classes, as well as in developing novel ones by means of machine learning techniques.
Chapter 3. Pharmacophore-based Virtual Screening in Drug Discovery
Chemoinformatics Approaches to Virtual Screening
p38-α Mitogen activated protein kinase (MAPK) is a serine/threonine kinase activated by environmental stimuli like stress and by various proinflammatory cytokines like tumor necrosis factor-α (TNF-α) and interleukin-1β (IL-1β). Excessive production of TNF-α and IL-1β may lead to various diseases such as rheumatoid arthritis, psoriasis and inflammatory bowel. Hence inhibition of p38-α MAPK can be a novel approach for the development of new anti-inflammatory agents. In this study a combined use of pharmacophore generation, atom based 3D-quantitative structure activity relationship (3D-QSAR), molecular docking and virtual screening has been performed for a series of pyridopyridazin-6-ones exhibiting p38-α MAP kinase inhibition activity. A five point pharmacophore (AAAHR): three hydrogen bond acceptors (AAA), one hydrophobic (H) and one aromatic ring (R) feature was obtained. A statistically significant 3D-QSAR model was obtained using this pharmacophore hypothesis with correlation coefficient (r 2 = 0.91) and high Fisher ratio (F =90.3) for the training set of 47 compounds. Also, the predictive power of generated model was found to be significant which was confirmed by the high value of cross validated correlation coefficient (q 2 = 0.80) and Pearson-R (0.90) for the test set of 16 compounds. Further, docking study revealed the binding orientations of active ligand at active site amino acid residues Val30, Gly31, Lys53, Leu75, Asp88, and Met109 of p38-α MAP kinase. The results of ligand based pharmacophore hypothesis and atom based 3D-QSAR explore the detailed structural insights and also highlights the important binding features of pyridopyridazin-6-ones with p38-α MAP kinase. These findings may provide useful guidelines for rational design of compounds as better p38-α MAP kinase inhibitors.
Structure-based virtual screening of chemical libraries for drug discovery
Current Opinion in Chemical Biology, 2006
One of the main goals in drug discovery is to identify new chemical entities that have a high likelihood of binding to the target protein to elicit the desired biological response. To this end, virtual screening is being increasingly used as a complement to high-throughput screening to improve the speed and efficiency of the drug discovery and development process. The availability of inexpensive high-performance computing platforms in recent years has transformed this field into one that is highly diverse and rapidly evolving, where large chemical databases have been successfully screened to identify hits for a wide range of targets such as Bcl-2 family proteins, G protein-coupled receptors, kinases, metalloproteins, nuclear hormone receptors, proteases and many more.
Journal of Molecular Graphics & Modelling, 2020
Breast carcinoma is the most common invasive cancer to affect the women in the North America and the world. Cancer of breast is the number one cancer overall with estimated 1.5 lakh new cases during 2016. The success of the current endocrine therapies is often limited due to the development of resistance. Therefore, there is a need to develop new lead compounds for breast cancer treatment. As 70% of breast carcinoma is ERþ, and it is well known previously that estrogen receptor alpha (ERa) is overexpressed in ER þ cases, so in the current work we attempt to develop some novel potent analogues against ERa. To achieve this, we have adopted an integrative computational approach that involves multiple sequence alignment, virtual screening (ligand and structure based), molecular docking, fingerprint based clustering and molecular dynamics simulation. The approach envisaged vital information about the binding site residues, conserved sequence among different species, ligand and protein conformations, binding energy of compound to bind into the active site of the receptor. Molecular docking analysis revealed that some analogues exhibited significant binding towards ERa. The top docked complexes showing good docking scores, hydrogen bond and hydrophobic interactions were selected for molecular dynamics simulation studies. RMSD revealed that the systems were quite stable with RMSD value below 3 Å. The RMSF analysis calculated residue wise fluctuations and revealed that the residues are flexible enough to interact with the ligand. The residue at C-terminal showed more flexibility as compared to other residues. To confirm binding of these analogues, MMGBSA analysis was performed which revealed binding energy of the ligands. Further, per-residue decomposition energy analysis revealed that Glu353, Leu346, Leu387 and Arg394 contributed towards ligand binding. The results visibly indicated that MMGBSA can act as filter in virtual screening experiments and play a major role in facilitating drug discovery.