Computational Studies of HIV-1 Protease Inhibitors

University dissertation from Uppsala : Acta Universitatis Upsaliensis

Abstract: Human Immunodeficiency Virus (HIV) is the causative agent of the pandemic disease Acquired Immune Deficiency Syndrome (AIDS). HIV acts to disrupt the immune system which makes the body susceptible to opportunistic infections. Untreated, AIDS is generally fatal. Twenty years of research by countless scientists around the world has led to the discovery and exploitation of several targets in the replication cycle of HIV. Many lives have been saved, prolonged and improved as a result of this massive effort. One particularly successful target has been the inhibition of HIV protease. In combination with the inhibition of HIV reverse transcriptase, protease inhibitors have helped to reduce viral loads and partially restore the immune system. Unfortunately, viral mutations leading to drug resistance and harmful side-effects of the current medicines have identified the need for new drugs to combat HIV.This study presents computational efforts to understand the interaction of inhibitors to HIV protease. The first part of this study has used molecular modelling and Comparative Molecular Field Analysis (CoMFA) to help explain the structure-active relationship of a novel series of protease inhibitors. The inhibitors are sulfamide derivatives structurally similar to the cyclic urea candidate drug mozenavir (DMP-450). The central ring of the sulfamides twists to adopt a nonsymmetrical binding mode distinct from that of the cyclic ureas. The energetics of this twist has been studied with ab initio calculations to develop improved empirical force field parameters for use in molecular modelling.The second part of this study has focused on an analysis of the association and dissociation kinetics of a broad collection of HIV protease inhibitors. Quantitative models have been derived using CoMFA which relate the dissociation rate back to the chemical structures. Efforts have also been made to improve the models by systematically varying the parameters used to generate them.

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