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Nowadays, computational-aided investigations become an essential part in the chemical, biochemical or pharmaceutical research. With increasing computing power, the calculation of larger biological systems becomes feasible. In this work molecular mechanical (MM) and quantum mechanical approaches (QM) and the combination of both (QM/MM) have been applied to study several questions which arose from different working groups. Thus, this work comprises eight different subjects which deals with chemical reactions or proton transfer in enzymes, conformational changes of ligands or proteins and verification of experimental data.
This work firstly deals with reaction mechanisms of aromatic inhibitors of cysteine proteases which can be found in many organisms. These enzymes are responsible for various cancer or diseases as for example Human African Trypanosomiasis (HAT) or the Chagas disease. Aromatic SNAr-type electrophiles might offer a new possibility to covalently modify these proteases. Quantum mechanical calculations have been performed to gain insights into the energetics and possible mechanisms.
The next chapter also deals with Trypanosomiasis but the focus was set on a different enzyme. The particularity of Trypanosomiasis is the thiol metabolism which can also be modified by covalent inhibitors. In this context, the wild type and point mutations of the enzyme tryparedoxin have been investigated via molecular dynamic (MD) simulations to examine the influence of specific amino acids in regard to the inhibitor. Experimental data showed that a dimerization of the enzyme occurs if the inhibitor is present. Simulations revealed that the stability of the dimer decreases in absence of the inhibitor and thus confirms these experiments.
Further investigations concerning cysteine proteases such as cruzain and rhodesain have been conducted with respect to experimental kinetic data of covalent vinylsulfone inhibitors. Several approaches such as QM or QM/MM calculations and docking, MD or MMPBSA/MMGBSA simulations have been applied to reproduce these data. The utilization of force field approaches resulted in a qualitatively accurate prediction.
The kinase AKT is involved in a range of diseases and plays an important role in the formation of cancer. Novel covalent-allosteric inhibitors have been developed and crystallized in complex with AKT. It was shown that depending on the inhibitor a different cysteine residue is modified. To investigate these differences in covalent modification computational simulations have been applied.
Enoyl-(acyl carrier) (ENR) proteins are essential in the last step of the fatty acid biosynthesis II (FAS) and represent a good target for inhibition. The diphenylether inhibitor SKTS1 which was originally designed to target the ENR’s of Staphylococcus aureus was also crystallized in InhA, the ENR of Mycobacterium tuberculosis (TB). Crystal structures indicate a change of the inhibitor's tautomeric form. This subject was investigated via MD simulations. Results of these simulations confirmed the tautomerization of the inhibitor.
This work also deals with the development of a covalent inhibitor originating from a non-covalent ligand. The target FadA5 is an essential enzyme for the degradation of steroids in TB and is responsible for chronic tuberculosis. This enzyme was crystallized in complex with a non-covalent ligand which served as starting point for this study. Computations on QM or QM/MM level and docking and MD simulations have been applied to evaluate potential candidates.
The next chapter focuses on the modification of the product spectrum of Bacillus megaterium levansucrase, a polymerase which catalyzes the biosynthesis of fructans. The covalent modification of the wild type or mutants of the enzyme lead to an accumulation of oligosaccharides but also to polymers with higher polymerization degree. To understand these changes in product spectra MD simulations have been performed.
Finally, the proton transfer in catalytic cysteine histidine dyads was investigated. The focus was set on the influence of the relaxation of the protein environment to the reaction. Calculations of the enzymes FadA5 and rhodesain revealed that the preferred protonation state of the dyade depends on the protein environment and has an impact on the reaction barrier. Furthermore, the adaptation of the environment to a fixed protonation state was analyzed via MD simulations.
\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.