In silico analysis of Human and Zebrafish ?-2 Adrenergic Receptors

?-2 adrenoceptors, belong to class of  Rhodopsin-like G-protein coupled receptors. Proteins of the G-protein coupled receptor (GPCR) family are involved in many pathophysiological conditions and hence are targets for various drug discovery methods. The current information on the structure of GPCRs is limited to few structures like Rhodopsin, ? adrenergic receptors, adenosine A2A receptors, Human Dopamine D3 and Chemokine receptor. In our study ?-2 adrenergic receptors of Human and Zebrafish were modeled using MODELLER with Human Dopamine D3 receptor (PDB ID: 3PBL) as template.  Through our modeling studies we have identified the critical role played by Proline residues (2.38, 2.59, 4.39, 4.59, 4.60, 7.50) of transmembrane helices and extracellular loop in stabilizing structural deviations in the transmembrane. Novel ligand binding residues S/T (6.56) and F (7.35) along with the positional significance of Y (3.28), Y (6.55) in regulating function were identified. Our models have shown that the Phenylalanine at 7.39 in TM7 can favourably interact with positively charged N-methyl group of the catecholamine ligands via hydrophobic contacts rather than 7.38 as reported previously. Furthermore, we are able to correctly show the orientation of Serine at 5.42 and 5.46 and discuss the relevance of residues at position 3.37 and 5.43 in the receptor regulation. We also demonstrate and propose that the orientation of V (2.61)/S should be taken into account in drug/ pharmacophore design specific for ?-2 adrenergic receptors. We believe that these findings will open new lead for ligand/ pharmacophore design, in silico leading further to experimental validation using Zebrafish as experimental model. Keywords: ?-2 adrenergic receptor, Zebrafish, ligand binding, residue conservation, homology modeling, ionic lock, toggle switch

Identifying new targets in leukemogenesis using computational approaches

AbstractThere is a need to identify novel targets in Acute Lymphoblastic Leukemia (ALL), a hematopoietic cancer affecting children, to improve our understanding of disease biology and that can be used for developing new therapeutics. Hence, the aim of our study was to find new genes as targets using in silico studies; for this we retrieved the top 10% overexpressed genes from Oncomine public domain microarray expression database; 530 overexpressed genes were short-listed from Oncomine database. Then, using prioritization tools such as ENDEAVOUR, DIR and TOPPGene online tools, we found fifty-four genes common to the three prioritization tools which formed our candidate leukemogenic genes for this study. As per the protocol we selected thirty training genes from PubMed. The prioritized and training genes were then used to construct STRING functional association network, which was further analyzed using cytoHubba hub analysis tool to investigate new genes which could form drug targets in leukemia. Analysis of the STRING protein network built from these prioritized and training genes led to identification of two hub genes, SMAD2 and CDK9, which were not implicated in leukemogenesis earlier. Filtering out from several hundred genes in the network we also found MEN1, HDAC1 and LCK genes, which re-emphasized the important role of these genes in leukemogenesis. This is the first report on these five additional signature genes in leukemogenesis. We propose these as new targets for developing novel therapeutics and also as biomarkers in leukemogenesis, which could be important for prognosis and diagnosis

Mapping the <i style="mso-bidi-font-style:normal">p53 </i>gene using STRING software to study the alterations modulating the functioning of associated genes in leukemia

451-461Alteration in the <i style="mso-bidi-font-style: normal">p53 gene leads to uncontrolled cell proliferation and when these changes accumulate, it may result in carcinogenesis. A plethora of proteins have been reported that bind to the various regions of p53 in order to regulate the specificity of its activity. In the present study, our aim was to understand these connections so we have analyzed the networking role of p53 gene using ‘STRING’ software in leukemia [acute lymphoblastic leukemia (ALL), acute myeloid leukemia (AML), chronic myeloid leukemia (CML) & <span style="mso-bidi-language: EN-US" lang="EN-GB">chronic lymphocytic leukemia (CLL)] with emphasis on ALL, being most prevalent in children. The TP53 protein is an important tumor suppressor protein, found altered in many cancers. Using STRING tool, we successfully determined the protein-protein interaction network and studied its functional interaction-partners with which we could decipher some of the major pathways that may be deregulated in ALL. Applying the clustering algorithm, currently accessible in STRING, i.e., k-Means Clustering, we identified 8 specific non-overlapping clusters of various sizes, which emerged from this huge network of protein-interactors. Since the functionally interacting partners are closely associated with each other, alterations in one might affect the other, thus contributing to disease etiology. In conclusion, our results highlight the interaction of p53 gene network, which modulates hundreds of proteins with a trigger of MDM signaling. Investigating these modulator or trigger proteins as key signaling factors could form targets for new therapeutic intervention sites. Further, these functionally interacting partners of disease proteins could help uncover novel disease mechanisms. From a scientific point of view, our ‘STRING’ results have clearly shown the importance of p53 signaling pathways in leukemia. </span

Drug Targets for Cell Cycle Dysregulators in Leukemogenesis: <i>In Silico</i> Docking Studies

<div><p>Alterations in cell cycle regulating proteins are a key characteristic in neoplastic proliferation of lymphoblast cells in patients with Acute Lymphoblastic Leukemia (ALL). The aim of our study was to investigate whether the routinely administered ALL chemotherapeutic agents would be able to bind and inhibit the key deregulated cell cycle proteins such as - Cyclins E1, D1, D3, A1 and Cyclin Dependent Kinases (CDK) 2 and 6. We used Schrödinger Glide docking protocol to dock the chemotherapeutic drugs such as Doxorubicin and Daunorubicin and others which are not very common including Clofarabine, Nelarabine and Flavopiridol, to the crystal structures of these proteins. We observed that the drugs were able to bind and interact with cyclins E1 and A1 and CDKs 2 and 6 while their docking to cyclins D1 and D3 were not successful. This binding proved favorable to interact with the G1/S cell cycle phase proteins that were examined in this study and may lead to the interruption of the growth of leukemic cells. Our observations therefore suggest that these drugs could be explored for use as inhibitors for these cell cycle proteins. Further, we have also highlighted residues which could be important in the designing of pharmacophores against these cell cycle proteins. This is the first report in understanding the mechanism of action of the drugs targeting these cell cycle proteins in leukemia through the visualization of drug-target binding and molecular docking using computational methods.</p></div

Docked pose of Cyclin E1 (CCNE1) with Doxorubicin.

<p>a. Structural view wherein hydrogen bonding is shown as yellow dashed line. b. Ligand interaction diagram with pink arrows representing electrostatic interactions and green line represent π-π interactions.</p

CDK2 Glide docking scores.

<p>CDK2 Glide docking scores.</p

Flowchart of the methodology followed while docking target protein to ligand.

<p>Flowchart of the methodology followed while docking target protein to ligand.</p

Cell cycle phases showing some of the check point proteins that can be deregulated in leukemia.

<p>Cell cycle phases showing some of the check point proteins that can be deregulated in leukemia.</p

CCNE1 Glide docking scores.

<p>CCNE1 Glide docking scores.</p

CCND1 Glide docking scores.

<p>CCND1 Glide docking scores.</p