<?xml version="1.0" encoding="UTF-8"?><xml><records><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Vyas, Renu</style></author><author><style face="normal" font="default" size="100%">Bapat, Sanket</style></author><author><style face="normal" font="default" size="100%">Jain, Esha</style></author><author><style face="normal" font="default" size="100%">Karthikeyan, Muthukumarasamy</style></author><author><style face="normal" font="default" size="100%">Tambe, Sanjeev</style></author><author><style face="normal" font="default" size="100%">Kulkarni, Bhaskar D.</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Building and analysis of protein-protein interactions related to diabetes mellitus using support vector machine, biomedical text mining and network analysis</style></title><secondary-title><style face="normal" font="default" size="100%">Computational Biology and Chemistry</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2016</style></year><pub-dates><date><style  face="normal" font="default" size="100%">DEC</style></date></pub-dates></dates><volume><style face="normal" font="default" size="100%">65</style></volume><pages><style face="normal" font="default" size="100%">37-44</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">In order to understand the molecular mechanism underlying any disease, knowledge about the interacting proteins in the disease pathway is essential. The number of revealed protein-protein interactions (PPI) is still very limited compared to the available protein sequences of different organisms. Experiment based high-throughput technologies though provide some data about these interactions, those are often fairly noisy. Computational techniques for predicting protein protein interactions therefore assume significance. 1296 binary fingerprints that encode a combination of structural and geometric properties were developed using the crystallographic data of 15,000 protein complexes in the pdb server. In a case study, these fingerprints were created for proteins implicated in the Type 2 diabetes mellitus disease. The fingerprints were input into a SVM based model for discriminating disease proteins from non disease proteins yielding a classification accuracy of 78.2% (AUC value of 0.78) on an external data set composed of proteins retrieved via text mining of diabetes related literature. A PPI network was constructed and analysed to explore new disease targets. The integrated approach exemplified here has a potential for identifying disease related proteins, functional annotation and other proteomics studies. (C) 2016 Elsevier Ltd. All rights reserved.</style></abstract><custom3><style face="normal" font="default" size="100%">Foreign</style></custom3><custom4><style face="normal" font="default" size="100%">1.014</style></custom4></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Karthikeyan, Muthukumarasamy</style></author><author><style face="normal" font="default" size="100%">Vyas, Renu</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Biomedical literature mining for protein-protein interactions analysis using electronic mailing system</style></title><secondary-title><style face="normal" font="default" size="100%">253rd National Meeting of the American-Chemical-Society (ACS) on Advanced Materials, Technologies, Systems, and Processes</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2017</style></year><pub-dates><date><style  face="normal" font="default" size="100%">APR </style></date></pub-dates></dates><publisher><style face="normal" font="default" size="100%">Amer chemical soc, 1155 16TH ST, NW, Washington, DC 20036 USA</style></publisher><pub-location><style face="normal" font="default" size="100%"> San Francisco, CA</style></pub-location><language><style face="normal" font="default" size="100%">eng</style></language><custom3><style face="normal" font="default" size="100%">&lt;p&gt;Foreign&lt;/p&gt;</style></custom3></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Qazi, Sahar</style></author><author><style face="normal" font="default" size="100%">Jit, Bimal Prasad</style></author><author><style face="normal" font="default" size="100%">Das, Abhishek</style></author><author><style face="normal" font="default" size="100%">Karthikeyan, Muthukumarasamy</style></author><author><style face="normal" font="default" size="100%">Saxena, Amit</style></author><author><style face="normal" font="default" size="100%">Ray, M. D.</style></author><author><style face="normal" font="default" size="100%">Singh, Angel Rajan</style></author><author><style face="normal" font="default" size="100%">Raza, Khalid</style></author><author><style face="normal" font="default" size="100%">Jayaram, B.</style></author><author><style face="normal" font="default" size="100%">Sharma, Ashok</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">BESFA: bioinformatics based evolutionary, structural &amp; functional analysis of prostate, Placenta, Ovary, Testis, and Embryo (POTE) paralogs</style></title><secondary-title><style face="normal" font="default" size="100%">Heliyon</style></secondary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">(MMGBSA)</style></keyword><keyword><style  face="normal" font="default" size="100%">Adaptive divergence</style></keyword><keyword><style  face="normal" font="default" size="100%">Evolution</style></keyword><keyword><style  face="normal" font="default" size="100%">generalized born surface area</style></keyword><keyword><style  face="normal" font="default" size="100%">Homology</style></keyword><keyword><style  face="normal" font="default" size="100%">Mechanics</style></keyword><keyword><style  face="normal" font="default" size="100%">Molecular docking</style></keyword><keyword><style  face="normal" font="default" size="100%">Molecular dynamic simulation molecular</style></keyword><keyword><style  face="normal" font="default" size="100%">POTE paralogs</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2022</style></year><pub-dates><date><style  face="normal" font="default" size="100%">SEP</style></date></pub-dates></dates><volume><style face="normal" font="default" size="100%">8</style></volume><pages><style face="normal" font="default" size="100%">e10476</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;
	The POTE family comprises 14 paralogues and is primarily expressed in Prostate, Placenta, Ovary, Testis, Embryo (POTE), and cancerous cells. The prospective function of the POTE protein family under physiological conditions is less understood. We systematically analyzed their cellular localization and molecular docking analysis to elucidate POTE proteins' structure, function, and Adaptive Divergence. Our results suggest that group three POTE paralogs (POTEE, POTEF, POTEI, POTEJ, and POTEKP (a pseudogene)) exhibits significant variation among other members could be because of their Adaptive Divergence. Furthermore, our molecular docking studies on POTE protein revealed the highest binding affinity with NCI-approved anticancer compounds. Additionally, POTEE, POTEF, POTEI, and POTEJ were subject to an explicit molecular dynamic simulation for 50ns. MM-GBSA and other essential electrostatics were calculated that showcased that only POTEE and POTEF have absolute binding affinities with minimum energy exploitation. Thus, this study's outcomes are expected to drive cancer research to successful utilization of POTE genes family as a new biomarker, which could pave the way for the discovery of new therapies.&lt;/p&gt;
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