Application of High Performance Computing for Development of Highly Predictive 3d-qsar Models

Infectious diseases such as malaria, leishmaniasis and a plethora of bacterial diseases have been and continue to be among the major problems for United States Military personnel deployed in disease endemic regions of the world. We currently employ computer-aided rational drug design and discovery methods to discover new and better drugs. Here, we compute the mathematical equation correlating the observed biological activity of the drug molecule to the various descriptors, such as physicochemical properties, electrostatic and steric fields and chemical functions of the drug molecules. In brief, QSAR involves computation of the conformational model of the drug molecules, alignment of the conformers in a biologically meaningful way, computation of the descriptors, and lastly using statistical techniques such as linear regression analysis to compute the QSAR model. The traditional approach of global minimum energy conformation of the drug molecules fails to deliver good predictive QSAR models for flexible molecules. To address this issue we have developed a novel method viz. bioactive conformation mining, which consistently delivered good predictive QSAR models. Development of Antimicrobial peptides (AMP) based antibacterials: Antimicrobial peptides (AMP) are involved in the defense mechanism of animals against invading microorganisms. The mechanism of action for AMP is via disruption of cell membranes. We have developed a series of AMPs employing unnatural amino acids by strategically controlling the 3D-physicochemical properties to exhibit different in vitro activity against Staphylococcus aureus (SA) and Mycobacterium ranae (MR) bacteria. We present the PC based 3D-QSAR studies, which provide valuable insights in the design of novel AMPs and also the mechanism of action. Development of novel DEET based insect repellents: Mosquitoes transmit a variety of parasites and pathogens. Keeping the mosquitoes away using insect repellents is, therefore, a significant preventive approach against these deadly diseases. N,N-diethyl-3-methyl benzamide (DEET) is the most effective and widely used insect repellent. We computed a PC based 3D-QSAR model to assist in prediction of insect repellency protection time of novel DEET based insect repellents. The QSAR model also provides valuable insight into the mechanism of action of DEET analogs and derivatives.