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Chakraborty, Chiranjib,Mallick, Bidyut,Sharma, Ashish Ranjan,Sharma, Garima,Jagga, Supriya,Doss, C George Priya,Nam, Ju-Suk,Lee, Sang-Soo Royan Institute 2017 Cell journal (Yakhteh) Vol.19 No.1
<P><B>Objective</B></P><P> Druggability of a target protein depends on the interacting micro-environment between the target protein and drugs. Therefore, a precise knowledge of the interacting micro-environment between the target protein and drugs is requisite for drug discovery process. To understand such micro-environment, we performed in silico interaction analysis between a human target protein, Dipeptidyl Peptidase-IV (DPP-4), and three anti-diabetic drugs (saxagliptin, linagliptin and vildagliptin). </P><P><B>Materials and Methods</B></P><P> During the theoretical and bioinformatics analysis of micro-environmental properties, we performed drug-likeness study, protein active site predictions, docking analysis and residual interactions with the protein-drug interface. Micro-environmental landscape properties were evaluated through various parameters such as binding energy, intermolecular energy, electrostatic energy, van der Waals’+H-bond+desolvo energy (E<SUB>VHD</SUB>) and ligand efficiency (LE) using different in silico methods. For this study, we have used several servers and software, such as Molsoft prediction server, CASTp server, AutoDock software and LIGPLOT server. </P><P><B>Results</B></P><P> Through micro-environmental study, highest log P value was observed for linagliptin (1.07). Lowest binding energy was also observed for linagliptin with DPP-4 in the binding plot. We also identified the number of H-bonds and residues involved in the hydrophobic interactions between the DPP-4 and the anti-diabetic drugs. During interaction, two H-bonds and nine residues, two H-bonds and eleven residues as well as four H-bonds and nine residues were found between the saxagliptin, linagliptin as well as vildagliptin cases and DPP-4, respectively. </P><P><B>Conclusion</B></P><P>Our in silico data obtained for drug-target interactions and micro-environmental signature demonstrates linagliptin as the most stable interacting drug among the tested anti-diabetic medicines.</P>
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Medriano, Carl Angelo D.,Na, Jinhyuk,Lim, Kyung-min,Chung, Jin-ho,Park, Youngja H. Royan Institute 2017 Cell journal (Yakhteh) Vol.19 No.1
<P><B>Objective</B></P><P> This study attempted to identify altered metabolism and pathways related to non-Hodgkin’s lymphoma (NHL) and myeloma patients. </P><P><B>Materials and Methods</B></P><P> In this retrospective study, we collected plasma samples from 11 patients-6 healthy controls with no evidence of any blood cancers and 5 patients with either multiple myeloma (n=3) or NHL (n=2) during the preliminary study period. Samples were analyzed using quadrupole time-of-flight liquid chromatography mass spectrometry (LC-MS). Significant features generated after statistical analyses were used for metabolomics and pathway analysis.</P><P><B>Results</B></P><P> Data after false discovery rate (FDR) adjustment at q=0.05 of features showed 136 for positive and 350 significant features for negative ionization mode in NHL patients as well as 262 for positive and 98 features for negative ionization mode in myeloma patients. Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis determined that pathways such as steroid hormone biosynthesis, ABC transporters, and arginine and proline metabolism were affected in NHL patients. In myeloma patients, pyrimidine metabolism, carbon metabolism, and bile secretion pathways were potentially affected by the disease.</P><P><B>Conclusion</B></P><P>The results have shown tremendous differences in the metabolites of healthy individuals compared to myeloma and lymphoma patients. Validation through quantitative metabolomics is encouraged, especially for the metabolites with significantly expression in blood cancer patients.</P>
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