02643nas a2200301   4500008004100000022001400041245005500055210005500110260006700165300001200232490000700244520171800251653001701969653001201986653001401998653001102012653002002023653000902043100002502052700002302077700002102100700002402121700002102145700003602166700002802202700002502230856008602255       2015                        eng d  a1615-985300aInvestigation of phosphoproteome in RAGE signaling0 aInvestigation of phosphoproteome in RAGE signaling  a111 RIVER ST, HOBOKEN 07030-5774, NJ USAbWILEY-BLACKWELLcJAN  a245-2590 v153 a<p>The receptor for advanced glycation end products (RAGE) is one of the most important proteins implicated in diabetes, cardiovascular diseases, neurodegenerative diseases, and cancer. It is a pattern recognition receptor by virtue of its ability to interact with multiple ligands, RAGE activates several signal transduction pathways through involvement of various kinases that phosphorylate their respective substrates. Only few substrates have been known to be phosphorylated in response to activation by RAGE (e.g., nuclear factor kappa B); however, it is possible that these kinases can phosphorylate multiple substrates depending upon their expression and localization, leading to altered cellular responses in different cell types and conditions. One such example is, glycogen synthase kinase 3 beta which is known to phosphorylate glycogen synthase, acts downstream to RAGE, and hyperphosphorylates microtubule-associated protein tau causing neuronal damage. Thus, it is important to understand the role of various RAGE-activated kinases and their substrates. Therefore, we have reviewed here the details of RAGE-activated kinases in response to different ligands and their respective phosphoproteome. Furthermore, we discuss the analysis of the data mined for known substrates of these kinases from the PhosphoSitePlus (http://www.phosphosite.org) database, and the role of some of the important substrates involved in cancer, diabetes, cardiovascular diseases, and neurodegenerative diseases. In summary, this review provides information on RAGE-activated kinases and their phosphoproteome, which will be helpful in understanding the possible role of RAGE and its ligands in progression of diseases.</p>10aCell biology10aDisease10aGlycation10aKinase10aPhosphoproteome10aRAGE1 aBatkulwar, Kedar, B.1 aBansode, Sneha, B.1 aPatil, Gouri, V.1 aGodbole, Rashmi, K.1 aKazi, Rubina, S.1 aChinnathambi, Subashchandrabose1 aShanmugam, Dhanasekaran1 aKulkarni, Mahesh, J.  uhttps://library.ncl.res.in/content/investigation-phosphoproteome-rage-signaling-002246nas a2200289   4500008004100000022001400041245013800055210006900193260000800262300001000270490000700280520126700287653001901554653001101573653001201584653002401596653002101620653003301641653002401674100001501698700001801713700001601731700003301747700002301780700002601803856012701829       2018                        eng d  a1545-596300aApplication of genetic programming (GP) formalism for building disease predictive models from protein-protein interactions (PPI) data0 aApplication of genetic programming GP formalism for building dis  cFEB  a27-370 v153 a<p>Protein-protein interactions (PPIs) play a vital role in the biological processes involved in the cell functions and disease pathways. The experimental methods known to predict PPIs require tremendous efforts and the results are often hindered by the presence of a large number of false positives. Herein, we demonstrate the use of a new Genetic Programming (GP) based Symbolic Regression (SR) approach for predicting PPIs related to a disease. In this case study, a dataset consisting of 135 PPI complexes related to cancer was used to construct a generic PPI predicting model with good PPI prediction accuracy and generalization ability. A high correlation coefficient (CC) magnitude of 0.893, and low root mean square error (RMSE), and mean absolute percentage error (MAPE) values of 478.221 and 0.239, respectively, were achieved for both the training and test set outputs. To validate the discriminatory nature of the model, it was applied on a dataset of diabetes complexes where it yielded significantly low CC values. Thus, the GP model developed here serves a dual purpose: (a) a predictor of the binding energy of cancer related PPI complexes, and (b) a classifier for discriminating PPI complexes related to cancer from those of other diseases.</p>10aBinding energy10acancer10aDisease10agenetic programming10amachine learning10aprotein-protein interactions10asymbolic regression1 aVyas, Renu1 aBapat, Sanket1 aGoel, Purva1 aKarthikeyan, Muthukumarasamy1 aTambe, Sanjeev, S.1 aKulkarni, Bhaskar, D.  uhttps://library.ncl.res.in/content/application-genetic-programming-gp-formalism-building-disease-predictive-models-protein