Title: Neural network based prediction of less side effect causing cancer drug targets in the network of MAPK pathways
Authors: V.K. Md Aksam; V.M. Chandrasekaran; Sundaramurthy Pandurangan
Addresses: GrayMatter Software Services Pvt. Ltd., Bangalore, 560103, India ' School of Advanced Sciences, VIT University, Vellore, 632014, India ' Department of Basic Sciences, Alliance College of Engineering and Design, Alliance University, Bangalore, 562106, India
Abstract: Computational side-effect prediction tools assist in rational drug design to decrease the late-stage failure of the drugs. Irrational selection of cancer drug targets in the deregulated MAPK pathways causes side effects. Network centralities and biological features - Degree, Radiality, Eccentricity, Closeness, Bridging, Stress, Pagerank centralities, essentiality, pathway-specific proteins, disease-causing proteins, protein domains are exploited quantitatively. We train an artificial neural network (ANN) with 15 selected features for the binary classification of side effects causing and less side-effect causing drug targets among the non-targeted proteins. Top ranked proteins among the Degree, Eccentricity, betweenness centralities, possessing GO-based molecular function, involved in more than one Biocarta pathways, domain content are prone to cause a number of side effects than other centralities and functional features. We predicted the following 15 less side effect causing cancer drug targets - Shc, Rap 1a, Mos, Tpl-2, PAC1, 4EBP1, GAB1, LAD, MEF2, ZAK, GADD45, TAB2, TAB1, ELK1 and SRF.
Keywords: cancer drug targets identification; network of MAPK pathways; side effects; essential proteins; graph theory.
International Journal of Bioinformatics Research and Applications, 2021 Vol.17 No.1, pp.69 - 79
Received: 20 Dec 2017
Accepted: 20 May 2018
Published online: 21 Mar 2021 *