Research Article In Silico Designing and Analysis of Inhibitors against Target Protein Identified through Host-Pathogen Protein Interactions in Malaria Monika Samant, 1 Nidhi Chadha, 2 Anjani K. Tiwari, 2 and Yasha Hasija 1 1 Department of Biotechnology, Delhi Technological University, Main Bawana Road, Shahbad Daulatpur, Delhi 110042, India 2 Division of Cyclotron and Radiopharmaceutical Sciences, Institute of Nuclear Medicine and Allied Sciences, Brig. S. K. Mazumdar Road, Delhi 110054, India Correspondence should be addressed to Yasha Hasija; yashahasija@gmail.com Received 9 October 2015; Revised 13 November 2015; Accepted 17 November 2015 Academic Editor: Armando Rossello Copyright © 2016 Monika Samant et al. Tis is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Malaria, a life-threatening blood disease, has been a major concern in the feld of healthcare. One of the severe forms of malaria is caused by the parasite Plasmodium falciparum which is initiated through protein interactions of pathogen with the host proteins. It is essential to analyse the protein-protein interactions among the host and pathogen for better understanding of the process and characterizing specifc molecular mechanisms involved in pathogen persistence and survival. In this study, a complete protein- protein interaction network of human host and Plasmodium falciparum has been generated by integration of the experimental data and computationally predicting interactions using the interolog method. Te interacting proteins were fltered according to their biological signifcance and functional roles. -tubulin was identifed as a potential protein target and inhibitors were designed against it by modifcation of amiprophos methyl. Docking and binding afnity analysis showed two modifed inhibitors exhibiting better docking scores of −10.5 kcal/mol and −10.43 kcal/mol and an improved binding afnity of −83.80 kJ/mol and −98.16 kJ/mol with the target. Tese inhibitors can further be tested and validated in vivo for their properties as an antimalarial drug. 1. Introduction Malaria, one of the most distressing diseases, is caused by the parasitic protozoan Plasmodium falciparum. It takes away millions of lives with the rate increasing each growing year. According to WHO’s Factsheet on the World Malaria Report 2013, 1.2 billion people out of a total of an estimated 3.4 billion are at a high risk of malaria. Malaria is highly prevalent in sub-Saharan Africa where 90% of all malaria deaths occur (WHO 2013). A lot of research has been going on in the feld of malarial therapeutics. Nowadays, there are a wide variety of antimalarial drugs, such as chloroquine and artemisinin, and strategies available for the control and treatment of malaria [1–3]. Despite clinical researches in the feld of infectious diseases, it remains to be a major problem in the worldwide health issue [4–6]. Exploring the infection process in detail can help in deciphering the mechanisms that govern and control it. In the process of evolution, pathogens have evolved an infection mechanism and humans have evolved immune responses as defense mechanism. A majority of host-pathogen interactions are governed by specifc protein- protein interactions [7–9]. To obtain a deep understanding of the infection process, specifc interactions between the host and pathogen need to be studied [10–15]. Host-pathogen protein interactions are typically studied using conventional small-scale methods which focuses on single protein at a time. Few methods for large-scale discovery have also been developed such as yeast two-hybrid experiments which allow more comprehensive identifcation but are expensive and time consuming. Computational methods are less time con- suming and cost-efective and hence are a better alternative for the prediction of protein interactions [10, 16–20]. Several studies in the feld of computational prediction of host-pathogen interactions and drug discovery have shown signifcant results. Dyer et al. developed a computational Hindawi Publishing Corporation International Journal of Medicinal Chemistry Volume 2016, Article ID 2741038, 13 pages http://dx.doi.org/10.1155/2016/2741038