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- Muthas, Daniel, 1976-
(författare)
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Development and Application of Computational Methods in Antitubercular Drug Design : Identification of Novel Inhibitors of Ribonucleotide Reductase
- 2009
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Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- 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.
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| 2. |
- Sköld, Christian, 1976-
(författare)
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Computational Modeling of the AT2 Receptor and AT2 Receptor Ligands : Investigating Ligand Binding, Structure–Activity Relationships, and Receptor-Bound Models
- 2007
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Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- Rational conversion of biologically active peptides to nonpeptide compounds with retained activity is an appealing approach in drug development. One important objective of the work presented in this thesis was to use computational modeling to aid in such a conversion of the peptide angiotensin II (Ang II, Asp-Arg-Val-Tyr-Ile-His-Pro-Phe). An equally important objective was to gain an understanding of the requirements for ligand binding to the Ang II receptors, with a focus on interactions with the AT2 receptor. The bioactive conformation of a peptide can provide important guidance in peptidomimetic design. By designing and introducing well-defined secondary structure mimetics into Ang II the bioactive conformation can be addressed. In this work, both γ- and β-turn mimetic scaffolds have been designed and characterized for incorporation into Ang II. Using conformational analysis and the pharmacophore recognition method DISCO, a model was derived of the binding mode of the pseudopeptide Ang II analogues. This model indicated that the positioning of the Arg side chain was important for AT2 receptor binding, which was also supported when the structure–activity relationship of Ang II was investigated by performing a glycine scan. To further examine ligand binding, a 3D model of the AT2 receptor was constructed employing homology modeling. Using this receptor model in a docking study of the ligands, binding modes were identified that were in agreement with data from point-mutation studies of the AT2 receptor. By investigating truncated Ang II analogues, small pseudopeptides were developed that were structurally similar to nonpeptide AT2 receptor ligands. For further guidance in ligand design of nonpeptide compounds, three-dimensional quantitative structure–activity relationship models for AT1 and AT2 receptor affinity as well as selectivity were derived.
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| 3. |
- Peterson, Shane, 1978-
(författare)
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Improved CoMFA Modeling by Optimization of Settings : Toward the Design of Inhibitors of the HCV NS3 Protease
- 2007
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Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- The hepatitis C virus (HCV), with a global prevalence of roughly 2%, is among the most serious diseases today. Among the more promising HCV targets is the NS3 protease, for which several drug candidates have entered clinical trials. In this work, computational methods have been developed and applied to the design of inhibitors of the HCV NS3 protease. Comparative molecular field analysis (CoMFA) modeling and molecular docking are the two main computational tools used in this work. CoMFA is currently the most widely used 3D-QSAR method. Methodology for improving its predictive performance by evaluating 6120 combinations of non-default parameters has been developed. This methodology was tested on 9 data sets for various targets and found to consistently provide models of enhanced predictive accuracy. Validation was performed using q2, r2pred and response variable randomization. Molecular docking was used to develop SARs in two series of inhibitors of the HCV NS3 protease. In the first series, preliminary investigations indicated that replacement of P2 proline with phenylglycine would improve potency. Docking suggested that phenylglycine-based inhibitors may participate in two additional interactions but that the larger, more flexible phenylglycine group may result in worse ligand fit, explaining the loss in potency. In the second series, β-amino acids were explored as α-amino acid substitutes. Although β-amino acid substitution may reduce the negative attributes of peptide-like compounds, this study showed that β-amino acid substitution resulted in reduced potency. The P3 position was least sensitive to substitution and the study highlighted the importance of interactions in the oxyanion hole. Finally, docking was used to provide the conformations and alignment necessary for a CoMFA model. This CoMFA model, derived using default settings, had q2 = 0.31 and r2pred = 0.56. Application of the optimization methodology provided a more predictive model with q2 = 0.48 and r2pred = 0.68.
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