Virtual Screening in the Identification of Sirtuins’ Activity Modulators

Sirtuins are NAD⁺-dependent deac(et)ylases with different subcellular localization. The sirtuins’ family is composed of seven members, named SIRT-1 to SIRT-7. Their substrates include histones and also an increasing number of different proteins. Sirtuins regulate a wide range of different processes, ranging from transcription to metabolism to genome stability. Thus, their dysregulation has been related to the pathogenesis of different diseases. In this review, we discussed the pharmacological approaches based on sirtuins’ modulators (both inhibitors and activators) that have been attempted in in vitro and/or in in vivo experimental settings, to highlight the therapeutic potential of targeting one/more specific sirtuin isoform(s) in cancer, neurodegenerative disorders and type 2 diabetes. Extensive research has already been performed to identify SIRT-1 and -2 modulators, while compounds targeting the other sirtuins have been less studied so far. Beside sections dedicated to each sirtuin, in the present review we also included sections dedicated to pan-sirtuins’ and to parasitic sirtuins’ modulators. A special focus is dedicated to the sirtuins’ modulators identified by the use of virtual screening.

Identification of Potent and Selective JAK1 Lead Compounds Through Ligand-Based Drug Design Approaches

JAK1 plays a significant role in the intracellular signaling by interacting with cytokine receptors in different types of cells and is linked to the pathogenesis of various cancers and in the pathology of the immune system. In this study, ligand-based pharmacophore modeling combined with virtual screening and molecular docking methods was incorporated to identify the potent and selective lead compounds for JAK1. Initially, the ligand-based pharmacophore models were generated using a set of 52 JAK1 inhibitors named C-2 methyl/hydroxyethyl imidazopyrrolopyridines derivatives. Twenty-seven pharmacophore models with five and six pharmacophore features were generated and validated using potency and selectivity validation methods. During potency validation, the Guner-Henry score was calculated to check the accuracy of the generated models, whereas in selectivity validation, the pharmacophore models that are capable of identifying selective JAK1 inhibitors were evaluated. Based on the validation results, the best pharmacophore models ADHRRR, DDHRRR, DDRRR, DPRRR, DHRRR, ADRRR, DDHRR, and ADPRR were selected and taken for virtual screening against the Maybridge, Asinex, Chemdiv, Enamine, Lifechemicals, and Zinc database to identify the new molecules with novel scaffold that can bind to JAK1. A total of 4,265 hits were identified from screening and checked for acceptable drug-like properties. A total of 2,856 hits were selected after ADME predictions and taken for Glide molecular docking to assess the accurate binding modes of the lead candidates. Ninety molecules were shortlisted based on binding energy and H-bond interactions with the important residues of JAK1. The docking results were authenticated by calculating binding free energy for protein–ligand complexes using the MM-GBSA calculation and induced fit docking methods. Subsequently, the cross-docking approach was carried out to recognize the selective JAK1 lead compounds. Finally, top five lead compounds that were potent and selective against JAK1 were selected and validated using molecular dynamics simulation. Besides, the density functional theory study was also carried out for the selected leads. Through various computational studies, we observed good potency and selectivity of these lead compounds when compared with the drug ruxolitinib. Compounds such as T5923555 and T5923531 were found to be the best and can be further validated using in vitro and in vivo methods.

Computational prediction models for assessing endocrine disrupting potential of chemicals

Endocrine disrupting chemicals (EDCs) mimic natural hormones and disrupt endocrine function. Humans and wildlife are exposed to EDCs might alter endocrine functions through various mechanisms and lead to an adverse effects. Hence, EDCs identification is important to protect the ecosystem and to promote the public health. Leveraging in-vitro and in-vivo experiments to identify potential EDCs is time consuming and expensive. Hence, quantitative structure-activity relationship is applied to screen the potential EDCs. Here, we summarize the predictive models developed using various algorithms to forecast the binding activity of chemicals to the estrogen and androgen receptors, alpha-fetoprotein, and sex hormone binding globulin.

Structure-based pharmacophore design and virtual screening for novel potential inhibitors of epidermal growth factor receptor as an approach to breast cancer chemotherapy

Cancer cells are described with features of uncontrolled growth, invasion and metastasis. The epidermal growth factor receptor subfamily of tyrosine kinases (EGFR-TK) plays a crucial regulatory role in the control of cellular proliferation and progression of various cancers. Therefore, its inhibition might lead to the discovery of a new generation of anticancer drugs. In the present study, structure-based pharmacophore modeling, molecular docking and molecular dynamics simulations were applied to identify potential hits, which exhibited good inhibition on the proliferation of MCF-7 breast cancer cell line and favorable binding interactions on EGFR-TK. Selected compounds were examined for their anticancer activity against the Michigan Cancer Foundation-7 (MCF-7) breast cancer cell line which overexpresses EGFR using the MTT (3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide) tetrazolium reduction assay. Compounds 1 and 2, with an isoindoline-1-one core, induced significant inhibition of breast cancer cells proliferation with IC[Formula: see text] values 327 and 370 nM, respectively.

Molecular modelling studies of sirtuin 2 inhibitors using three-dimensional structure-activity relationship analysis and molecular dynamics simulations

Sirtuin 2 (SIRT2) is a nicotinamide-adenine-dinucleotide-dependent histone deacetylase that plays a vital role in various biological processes related to DNA regulation, metabolism, and longevity. Recent studies on SIRT2 have indicated its therapeutic potential for neurodegenerative diseases such as Parkinson's disease. In this study, a series of SIRT2 inhibitors with a 2-anilinobenzamide core was analysed using a combination of molecular modelling techniques. A three-dimensional structure-activity relationship (3D-QSAR) model adopting a comparative molecular field analysis (CoMFA) method with a non-cross-validated correlation coefficient R(2) = 0.992 (for training set) and a correlation coefficient Rtest(2) = 0.804 (for test set) was generated to determine the structural requirements for inhibitory activity. Furthermore, we employed molecular dynamics (MD) simulations and the molecular mechanics/generalized Born surface area (MM/GBSA) method to compare the binding modes of a potent and selective compound interacting with SIRT1, SIRT2, and SIRT3 and also their binding free energies to shed light on the selectivity of the footing of structural and energetic investigations. The steric and electrostatic contour maps from the 3D-QSAR analysis identified several key interactions also observed in the MD simulations. According to these results, we provide guidelines for developing novel potent and selective SIRT2 inhibitors.

Molecular docking and dynamics simulation, receptor-based hypothesis: application to identify novel sirtuin 2 inhibitors

Sirtuin, NAD(+)-dependent histone deacetylase enzyme, emerged as a potential therapeutic target, and modulations by small molecules could be effective drugs for various diseases. Owing to the absence of complex structure of sirtuin 2 (SIRT2), sirtinol was docked in the NAD(+) binding site and subjected to 5-nseconds molecular dynamics (MD) simulation. LigandScout was used to develop hypotheses based on 3-representative SIRT2 complex structures from MD. Three structure-based hypotheses are generated and merged to form dynamics hypothesis. The dynamics hypothesis was validated using test and decoy sets. The results showed that dynamic hypothesis represents the complementary features of SIRT2 active site. Dynamic hypothesis was used to screen ChemDiv database, and hits were filtered through ADMET, rule of five, and two different molecular docking studies. Finally, 21 molecules were selected as potent leads based on consensus score from LigandFit, Gold fitness score, binding affinity from VINA as well as based on the important interactions with critical residues in SIRT2 active site. Hence, we suggest that the dynamic hypothesis will be reliable in the identification of SIRT2 new lead as well as to reduce time and cost in the drug discovery process.

Molecular modeling study for conformational changes of Sirtuin 2 due to substrate and inhibitor binding

Sirtuin is a member of NAD(+)-dependent deacetylase family. The structural details of Sirtuin 2 (SIRT2) complex will be very useful to discover the drug which might have beneficial effects on various diseases like cancer, diabetes, etc. Unfortunately, SIRT2 complex structure is not available yet, hence molecular docking was carried out to dock the substrate (NAD(+) and acetylated lysine) and inhibitor (sirtinol) in the NAD(+) binding site. The suitable binding orientation of substrate and inhibitor in the SIRT2 active site was selected and subjected to 5 ns molecular dynamics simulations to adjust the binding orientation of inhibitor and substrate as well as to identify the conformational changes in the active site. The result provides an insight about 3D SIRT2 structural details as well as the importance of F96 in deacetylation function. In addition, our simulations revealed the displacement of F96 upon substrate and inhibitor binding, inducing an extended conformation of loop3 and changing its interactions with the rest of SIRT2. We believe that our study could be helpful to gain a structural insight of SIRT2 and to design the receptor-based inhibitors.