02122nas a2200193   4500008004100000245016100041210006900202260000800271300001000279490000700289520137600296100001501672700001801687700001501705700003301720700001901753700002601772856013001798       2016                        eng d00aBuilding and analysis of protein-protein interactions related to diabetes mellitus using support vector machine, biomedical text mining and network analysis0 aBuilding and analysis of proteinprotein interactions related to   cDEC  a37-440 v653 aIn 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.1 aVyas, Renu1 aBapat, Sanket1 aJain, Esha1 aKarthikeyan, Muthukumarasamy1 aTambe, Sanjeev1 aKulkarni, Bhaskar, D.  uhttps://library.ncl.res.in/content/building-and-analysis-protein-protein-interactions-related-diabetes-mellitus-using-support00598nas a2200109   4500008004100000245010700041210006900148260009200217100003300309700001500342856013100357       2017                        eng d00aBiomedical literature mining for protein-protein interactions analysis using electronic mailing system0 aBiomedical literature mining for proteinprotein interactions ana  a San Francisco, CAbAmer chemical soc, 1155 16TH ST, NW, Washington, DC 20036 USAcAPR 1 aKarthikeyan, Muthukumarasamy1 aVyas, Renu  uhttps://library.ncl.res.in/content/biomedical-literature-mining-protein-protein-interactions-analysis-using-electronic-mailing02385nas a2200349   4500008004100000245014400041210006900185260000800254300001100262490000600273520123500279653001301514653002401527653001401551653003401565653001301599653001401612653002201626653004301648653001801691100001601709700002301725700001801748700003301766700001701799700001601816700002401832700001701856700001601873700001801889856012801907       2022                        eng d00aBESFA: bioinformatics based evolutionary, structural & functional analysis of prostate, Placenta, Ovary, Testis, and Embryo (POTE) paralogs0 aBESFA bioinformatics based evolutionary structural functional an  cSEP  ae104760 v83 a<p>
	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.</p>
10a(MMGBSA)10aAdaptive divergence10aEvolution10ageneralized born surface area10aHomology10aMechanics10aMolecular docking10aMolecular dynamic simulation molecular10aPOTE paralogs1 aQazi, Sahar1 aJit, Bimal, Prasad1 aDas, Abhishek1 aKarthikeyan, Muthukumarasamy1 aSaxena, Amit1 aRay, M., D.1 aSingh, Angel, Rajan1 aRaza, Khalid1 aJayaram, B.1 aSharma, Ashok  uhttps://library.ncl.res.in/content/besfa-bioinformatics-based-evolutionary-structural-functional-analysis-prostate-placenta