@phdthesis{Merget2015,
  author    = {Merget, Benjamin},
  title     = {Computational methods for assessing drug-target residence times in bacterial enoyl-ACP reductases and predicting small-molecule permeability for the \(Mycobacterium\) \(tuberculosis\) cell wall},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-127386},
  school      = {Universit{\"a}t W{\"u}rzburg},
  year      = {2015},
  abstract  = {\textbf{Molecular Determinants of Drug-Target Residence Times of Bacterial Enoyl-ACP Reductases.} Whereas optimization processes of early drug discovery campaigns are often affinity-driven, the drug-target residence time \$t_R\$ should also be considered due to an often strong correlation with \textit{in vivo} efficacy of compounds. However, rational optimization of \$t_R\$ is not straightforward and generally hampered by the lack of structural information about the transition states of ligand association and dissociation. The enoyl-ACP reductase FabI of the fatty acid synthesis (FAS) type II is an important drug-target in antibiotic research. InhA is the FabI enzyme of \textit{Mycobacterium tuberculosis}, which is known to be inhibited by various compound classes. Slow-onset inhibition of InhA is assumed to be associated with the ordering of the most flexible protein region, the substrate binding loop (SBL). Diphenylethers are one class of InhA inhibitors that can promote such SBL ordering, resulting in long drug-target residence times. Although these inhibitors are energetically and kinetically well characterized, it is still unclear how the structural features of a ligand affect \$t_R\$. Using classical molecular dynamics (MD) simulations, recurring conformational families of InhA protein-ligand complexes were detected and structural determinants of drug-target residence time of diphenyl\-ethers with different kinetic profiles were described. This information was used to deduce guidelines for efficacy improvement of InhA inhibitors, including 5'-substitution on the diphenylether B-ring. The validity of this suggestion was then analyzed by means of MD simulations. Moreover, Steered MD (SMD) simulations were employed to analyze ligand dissociation of diphenylethers from the FabI enzyme of \textit{Staphylococcus aureus}. This approach resulted in a very accurate and quantitative linear regression model of the experimental \$ln(t_R)\$ of these inhibitors as a function of the calculated maximum free energy change of induced ligand extraction. This model can be used to predict the residence times of new potential inhibitors from crystal structures or valid docking poses. Since correct structural characterization of the intermediate enzyme-inhibitor state (EI) and the final state (EI*) of two-step slow-onset inhibition is crucial for rational residence time optimization, the current view of the EI and EI* states of InhA was revisited by means of crystal structure analysis, MD and SMD simulations. Overall, the analyses affirmed that the EI* state is a conformation resembling the 2X23 crystal structure (with slow-onset inhibitor \textbf{PT70}), whereas a twist of residues Ile202 and Val203 with a further opened helix \$\alpha 6\$ corresponds to the EI state. Furthermore, MD simulations emphasized the influence of close contacts to symmetry mates in the SBL region on SBL stability, underlined by the observation that an MD simulation of \textbf{PT155} chain A with chain B' of a symmetry mate in close proximity of the SBL region showed significantly more stable loops, than a simulation of the tetrameric assembly. Closing Part I, SMD simulations were employed which allow the delimitation of slow-onset InhA inhibitors from rapid reversible ligands. \textbf{Prediction of \textit{Mycobacterium tuberculosis} Cell Wall Permeability.} The cell wall of \textit{M. tuberculosis} hampers antimycobacterial drug design due to its unique composition, providing intrinsic antibiotic resistance against lipophilic and hydrophilic compounds. To assess the druggability space of this pathogen, a large-scale data mining endeavor was conducted, based on multivariate statistical analysis of differences in the physico-chemical composition of a normally distributed drug-like chemical space and a database of antimycobacterial--and thus very likely permeable--compounds. The approach resulted in the logistic regression model MycPermCheck, which is able to predict the permeability probability of small organic molecules based on their physico-chemical properties. Evaluation of MycPermCheck suggests a high predictive power. The model was implemented as a freely accessible online service and as a local stand-alone command-line version. Methodologies and findings from both parts of this thesis were combined to conduct a virtual screening for antimycobacterial substances. MycPermCheck was employed to screen the chemical permeability space of \textit{M. tuberculosis} from the entire ZINC12 drug-like database. After subsequent filtering steps regarding ADMET properties, InhA was chosen as an exemplary target. Docking to InhA led to a principal hit compound, which was further optimized. The quality of the interaction of selected derivatives with InhA was subsequently evaluated using MD and SMD simulations in terms of protein and ligand stability, as well as maximum free energy change of induced ligand egress. The results of the presented computational experiments suggest that compounds with an indole-3-acethydrazide scaffold might constitute a novel class of InhA inhibitors, worthwhile of further investigation.},
  subject      = {Computational chemistry},
  language  = {en}
}