Dataset Information

QSAR modelling and molecular docking studies for anti-cancer compounds against melanoma cell line SK-MEL-2.

ABSTRACT: A dataset of seventy-two (72) cytotoxic compounds of the National Cancer Institute (NCI) was studied by QSAR and docking approaches to gain deeper insights into ligands selectivity on SK-MEL-2 cell line. The QSAR model was built using fifty (50) molecules and the best-generated model based on multiple linear regression showed, respectively good quality of fits ( R2 (0.864), Radjusted2 (0.845), Q2 cv (0.799) and Rpred2 (0.706)). The model's predictive ability was determined by a test set of twenty-two (22) compounds. Compounds 30 and 41 were selected as templates for in silico design because they had high pGI50 activity and are in the model's applicability domain. The obtained information from the model was explored to design novel molecules by introducing various modifications. Moreover, the designed compounds with better-predicted activity (pGI50) values were selected and docked on the active site of the protein (PDB-CODE: 3OG7) which is responsible for melanoma cancer to elucidate their binding mode. AN2 (-12.1kcalmol-1) and AC4 (-12.4kcalmol-1) showed a better binding score for the target when compared with (vemurafenib, -11.3kcalmol-1) the known inhibitor of the target (V600E-BRAF). These findings may be very helpful in early anti-cancer drug development.

SUBMITTER: Umar AB

PROVIDER: S-EPMC7110328 | biostudies-literature | 2020 Mar

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

QSAR modelling and molecular docking studies for anti-cancer compounds against melanoma cell line SK-MEL-2.

Umar Abdullahi Bello AB Uzairu Adamu A Shallangwa Gideon Adamu GA Uba Sani S

Heliyon 20200327 3

A dataset of seventy-two (72) cytotoxic compounds of the National Cancer Institute (NCI) was studied by QSAR and docking approaches to gain deeper insights into ligands selectivity on SK-MEL-2 cell line. The QSAR model was built using fifty (50) molecules and the best-generated model based on multiple linear regression showed, respectively good quality of fits ( R2 (0.864), Radjusted2 (0.845), Q<sup>2</sup> <sub>cv</sub> (0.799) and Rpred2 (0.706)). The model's predictive ability was determined ...[more]

PMID: 32258485

Similar Datasets

Project description:Melanoma tumor antigen p97 or melanotransferrin (MTf) is an iron (Fe)-binding protein with high homology to serum transferrin. MTf is expressed at very low levels in normal tissues and in high amounts in melanoma cells. The over-expression of MTf in tumor cells was hypothesized to assist rapidly proliferating neoplastic cells with their increased Fe requirements. However, our recent characterization of the MTf knockout (MTf -/-) mouse demonstrated that MTf did not have an essential role in Fe metabolism. To understand the function of MTf, we utilized whole-genome microarray analysis to examine the gene expression profile of five models after modulating MTf expression. These models included two new stably transfected MTf hyper-expression models (SK-N-MC neuroepithelioma and LMTK- fibroblasts) and one cell type (SK-Mel-28 melanoma) where MTf was down-regulated by post-transcriptional gene silencing. These findings were compared to alterations in gene expression identified using the MTf -/- mouse. In addition, the changes identified from the gene array data were also assessed in a new model of MTf down-regulation in SK-Mel-2 melanoma cells. In the cell line models, MTf hyper-expression led to increased cellular proliferation, while MTf down-regulation resulted in decreased proliferation. Across all five models of MTf down- and up-regulation, we identified three genes modulated by MTf expression. These included ATP-binding cassette sub-family B member 5 (Abcb5), whose change in expression mirrored MTf down- or up-regulation. In addition, thiamine triphosphatase (Thtpa) and transcription factor 4 (Tcf4) were inversely expressed relative to MTf levels across all five models. The products of these three genes are involved in membrane transport, thiamine phosphorylation and cell proliferation/survival, respectively. This study identifies novel molecular targets directly or indirectly regulated by MTf and potential pathways involved in its function. These molecular targets could be involved, at least in part, to the role of MTf in modulating proliferation. Experiment Overall Design: To prepare a construct capable of generating hairpin siRNA specific for human MTf mRNA, the expression vector, pSilencer™ 3.1-H1 neo (Ambion, Texas, USA) was used. The vector was used to clone transgene of 66-bp that transcribe 19-mer double-stranded hairpin RNAs of the target gene. The transgene were specifically targeted to positions 2031-2049-bp in the MTf gene (Genbank Accession: NM_005929). Transfections of pS-MTf transgenes or pS-scrambled vectors into human SK-Mel-28 melanoma cells were performed with LipofectamineTM 2000 reagent (Invitrogen, Melbourne, Australia). Total RNA was isolated from the cells using TRIzol Reagent® (Sigma-Aldrich) according to the manufacturer’s protocol. Total RNA from the stably-transfected SK-Mel-28 melanoma cell lines were prepared and hybridized onto Human Genome U133 Plus 2.0 Array. The human GeneChip® U133 Plus 2.0 consists of greater than 47,000 transcripts and variants from over 38,500 well characterized human genes (Affymetrix, Santa Clara, CA).

Project description:BackgroundInfluenza virus disease remains one of the most contagious diseases that aided the deaths of many patients, especially in this COVID-19 pandemic era. Recent discoveries have shown that the high prevalence of influenza and SARS-CoV-2 coinfection can rapidly increase the death rate of patients. Hence, it became necessary to search for more potent inhibitors for influenza disease therapy. The present study utilized some computational modeling concepts such as 2D-QSAR, 3D-QSAR, molecular docking simulation, and ADMET predictions of some 1,3-thiazine derivatives as inhibitors of influenza neuraminidase (NA).ResultsThe 2D-QSAR modeling results showed GFA-MLR ( Rtrain2 = 0.9192, Q 2 = 0.8767, R 2 adj = 0.8991, RMSE = 0.0959, Rtest2 = 0.8943, Rpred2 = 0.7745) and GFA-ANN ( Rtrain2 = 0.9227, Q 2 = 0.9212, RMSE = 0.0940, Rtest2 = 0.8831, Rpred2 = 0.7763) models with the computed descriptors as ATS7s, SpMax5_Bhv, nHBint6, and TDB9m for predicting the NA inhibitory activities of compounds which have passed the global criteria of accepting QSAR model. The 3D-QSAR modeling was carried out based on the comparative molecular field analysis (CoMFA) and comparative similarity indices analysis (CoMSIA). The CoMFA_ES ( Rtrain2 = 0.9620, Q 2 = 0.643) and CoMSIA_SED ( Rtrain2 = 0.8770, Q 2 = 0.702) models were found to also have good and reliable predicting ability. The compounds were also virtually screened based on their binding scores via molecular docking simulations with the active site of the NA (H1N1) target receptor which also confirms their resilient potency. Four potential lead compounds (4, 7, 14, and 15) with the relatively high inhibitory rate (> 50%) and docking (> - 6.3 kcal/mol) scores were identified as the possible lead candidates for in silico exploration of improved anti-influenza agents.ConclusionThe drug-likeness and ADMET predictions of the lead compounds revealed non-violation of Lipinski's rule and good pharmacokinetic profiles as important guidelines for rational drug design. Hence, the outcome of this research set a course for the in silico design and exploration of novel NA inhibitors with improved potency.

Dataset Information

QSAR modelling and molecular docking studies for anti-cancer compounds against melanoma cell line SK-MEL-2.

Publications

QSAR modelling and molecular docking studies for anti-cancer compounds against melanoma cell line SK-MEL-2.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets