Development and Application of Computational Methods in Antitubercular Drug Design : Identification of Novel Inhibitors of Ribonucleotide Reductase

Sammanfattning: Tuberculosis kills approximately 1.7 million people each year around the world making it one of the most lethal infectious diseases. This thesis concerns the development of two computational tools that can support the early stages of drug discovery, and their use in an anti-tubercular drug discovery program.One of the tools developed is a statistical molecular design (SMD) approach that generates information-rich libraries biased towards a lead structure. The other metod is a post-filtering technique to increase the success of virtual screening, has also been developed. Both methods have been validated using literature data.Ribonucleotide reductase (RNR) has been identified as a potential anti-tubercular target, and our focus has been to develop small-molecule inhibitors of this target. The enzyme consists of two subunits (a large R1 and a small R2 subunit) that have to associate in order to generate a bioactive complex. It had previously been shown that a heptapeptide corresponding to the small R2 subunits C-terminal inhibited the enzyme. In order to investigate the requirements for inhibitory effect of the peptide a library was designed using the developed SMD approach. The designed library was synthesized and evaluated for biological activity and an OPLS-DA model was derived to understand which positions were most important for activity.In order to identify small-molecule inhibitors of RNR a combined shape- and structure-based virtual screen was performed, employing ROCS, GlideXP and the developed post-filtering technique. Starting from a library of 1.5 million compounds 24 was acquired and evaluated for enzymatic activity. The best compounds were almost as potent as the starting peptide, but considerably more drug-like.