<p>Molecular interactions lie at the heart of myriad biological processes. Knowledge of molecular recognition processes and the ability to model and predict interactions of any biological molecule to any chemical compound are the key for better understanding of cell functions and discovery of more efficacious medicines.</p><p>This thesis presents contributions to the development of a novel chemo-bioinformatics approach called proteochemometrics; a general method for interaction space analysis of biological macromolecules and their ligands. In this work we explore proteochemometrics-based interaction models over broad groups of protein families, evaluate their validity and scope, and compare proteochemometrics to traditional modeling approaches.</p><p>Through the proteochemometric analysis of large interaction data sets of multiple retroviral proteases from various viral species we investigate complex mechanisms of drug resistance in HIV-1 and discover general physicochemical determinants of substrate cleavage efficiency and binding in retroviral proteases. We further demonstrate how global proteochemometric models can be used for design of protease inhibitors with broad activity on drug-resistant viral mutants, for monitoring drug resistance mechanisms in the physicochemical sense and prediction of potential HIV-1 evolution trajectories. We provide novel insights into the complexity of HIV-1 protease specificity by constructing a generalized IF-THEN rule model based on bioinformatics analysis of the largest set of HIV-1 protease substrates and non-substrates.</p><p>We discuss how proteochemometrics can be used to map recognition sites of entire protein families in great detail and demonstrate how it can incorporate target variability into drug discovery process. Finally, we assess the utility of the proteochemometric approach in evaluation of ADMET properties of drug candidates with a special focus on inhibition of cytochrome P450 enzymes and investigate application of the approach in the pharmacogenomics field.</p>

Viruses are a major threat to humans due to their unique adaptability, evolvability and  capability to control their hosts as parasites and genetic elements. HIV/AIDS is the third largest cause of death by infectious diseases in the world, and drug resistance due to the viral mutations is still the leading cause of treatment failure. The flaviviruses, such as Dengue virus (DEN) and Japanese encephalitis virus (JEV), represent other major cause of morbidity and mortality, and the areas where these viruses are endemic are spreading rapidly. No curative therapy for any flavivirus could be made available as yet. The first part of this thesis focuses on the HIV-1 drug resistance caused by mutations in a major HIV drug target, the HIV-1 reverse transcriptase (RT) as a response to the largest class of clinically used anti-retrovirals, the NRTIs. A robust proteochemometric model was created to analyse the complex mutation patterns in RT drug resistance. The model identified more than ten frequently-occurring mutations, each conferring at least two-fold decrease in susceptibility for one or several NRTIs. Using our prediction server (hivdrc.org), the model can be applied to propose optimum combination therapy for patients harbouring mutated HIV variants. The second part of the thesis encompasses studies on a promising drug target, the NS2B(H)-NS3pro, in two flaviviruses, namely the dengue virus (DEN) and Japanese encephalitis virus (JEV). Functional determinants of DEN NS2B(H)-NS3pro were identified by site-directed mutagenesis. Further, peptide inhibitors were designed using proteochemometrics (PCM) and statistical molecular design (SMD), synthesized and assayed on DEN proteases, which resulted in some novel peptides with low micromolar or sub-micromolar inhibitor activity. The very poorly characterised JEV NS2B(H)-NS3pro  was cloned, purified and the kinetic parameters of this attractive drug target were determined for a series of model substrates and inhibitor. The results identified the role in target-ligand interaction of different residues on specific positions in the target (NS2B(H)-NS3pro) and ligands (substrates/inhibitors). Overall, the findings in this thesis contribute to rational antiviral drug discovery and therapy.

Molecular interactions lie at the heart of myriad biological processes. Knowledge of molecular recognition processes and the ability to model and predict interactions of any biological molecule to any chemical compound are the key for better understanding of cell functions and discovery of more efficacious medicines. This thesis presents contributions to the development of a novel chemo-bioinformatics approach called proteochemometrics; a general method for interaction space analysis of biological macromolecules and their ligands. In this work we explore proteochemometrics-based interaction models over broad groups of protein families, evaluate their validity and scope, and compare proteochemometrics to traditional modeling approaches. Through the proteochemometric analysis of large interaction data sets of multiple retroviral proteases from various viral species we investigate complex mechanisms of drug resistance in HIV-1 and discover general physicochemical determinants of substrate cleavage efficiency and binding in retroviral proteases. We further demonstrate how global proteochemometric models can be used for design of protease inhibitors with broad activity on drug-resistant viral mutants, for monitoring drug resistance mechanisms in the physicochemical sense and prediction of potential HIV-1 evolution trajectories. We provide novel insights into the complexity of HIV-1 protease specificity by constructing a generalized IF-THEN rule model based on bioinformatics analysis of the largest set of HIV-1 protease substrates and non-substrates. We discuss how proteochemometrics can be used to map recognition sites of entire protein families in great detail and demonstrate how it can incorporate target variability into drug discovery process. Finally, we assess the utility of the proteochemometric approach in evaluation of ADMET properties of drug candidates with a special focus on inhibition of cytochrome P450 enzymes and investigate application of the approach in the pharmacogenomics field.