Computational Studies of Macrocycles and Molecular Modeling of Hepatitis C Virus NS3 Protease Inhibitors

Computational tools are utilized in the drug discovery process to discover, design, and optimize new therapeutics. One important approach is structure-based drug design which relies on knowledge about the 3D structure of the biological target. The first part of this work focuses on applying structure-based drug design for binding mode prediction of HCV NS3 protease inhibitors. The NS3 protease is a challenging target from a computational perspective as it contains an extended binding site. Binding mode predictions were performed for various classes of new acyclic and macrocyclic HCV NS3 protease inhibitors and was used in the design of new inhibitors. None of the synthetized inhibitors have been co-crystallized yet, which has made the evaluation of the suggested binding mode predictions challenging. Macrocycles are an interesting compound class in drug discovery due to their unique structural architecture, which can enable access to new chemical space. Macrocycles can successfully modulate difficult therapeutic targets, as exemplified in the development of protease inhibitors. Furthermore they can improve drug-like properties, such as cell permeability and bioavailability. The second part of this thesis focuses on macrocycles from a computational point of view. A data set of 47 clinically relevant macrocycles was compiled and used in these studies. First, two different docking protocols rigid docking of pre-generated conformers and flexible docking in Glide were evaluated and compared. The results showed that flexible docking in Glide was sufficient for docking of macrocycles with respect to accuracy and speed. The aim of the second study was to evaluate and compare the performance of the more general conformational analysis tools, MCMM and MTLMOD, with the recently developed macrocycle-specialized conformational sampling tools, Prime-MCS and MMBS. In most cases, the general conformational analysis tools (with enhanced parameter settings) performed equally well as compared to the macrocycle-specialized conformational sampling techniques. However, MMBS was superior at locating the global energy minimum conformation. Finally, calculation of the conformational energy penalty of protein-bound macrocycles was performed. The macrocycle data set was complemented with linear analogues that are similar either with respect to physicochemical properties or 2D fingerprints. The conformational energy penalties of these linear analogues were calculated and compared to the conformational energy penalties of the macrocycles. The complete data set of macrocycles and non-macrocycles in this study differ from previously published work addressing conformational energy penalties, since it covers a more extended area of chemical space. Furthermore, there was a weak correlation between the calculated conformational energy penalties and the flexibility of the structures

Computational Studies of Macrocycles and Molecular Modeling of Hepatitis C Virus NS3 Protease Inhibitors

Computational tools are utilized in the drug discovery process to discover, design, and optimize new therapeutics. One important approach is structure-based drug design which relies on knowledge about the 3D structure of the biological target. The first part of this work focuses on applying structure-based drug design for binding mode prediction of HCV NS3 protease inhibitors. The NS3 protease is a challenging target from a computational perspective as it contains an extended binding site. Binding mode predictions were performed for various classes of new acyclic and macrocyclic HCV NS3 protease inhibitors and was used in the design of new inhibitors. None of the synthetized inhibitors have been co-crystallized yet, which has made the evaluation of the suggested binding mode predictions challenging. Macrocycles are an interesting compound class in drug discovery due to their unique structural architecture, which can enable access to new chemical space. Macrocycles can successfully modulate difficult therapeutic targets, as exemplified in the development of protease inhibitors. Furthermore they can improve drug-like properties, such as cell permeability and bioavailability. The second part of this thesis focuses on macrocycles from a computational point of view. A data set of 47 clinically relevant macrocycles was compiled and used in these studies. First, two different docking protocols rigid docking of pre-generated conformers and flexible docking in Glide were evaluated and compared. The results showed that flexible docking in Glide was sufficient for docking of macrocycles with respect to accuracy and speed. The aim of the second study was to evaluate and compare the performance of the more general conformational analysis tools, MCMM and MTLMOD, with the recently developed macrocycle-specialized conformational sampling tools, Prime-MCS and MMBS. In most cases, the general conformational analysis tools (with enhanced parameter settings) performed equally well as compared to the macrocycle-specialized conformational sampling techniques. However, MMBS was superior at locating the global energy minimum conformation. Finally, calculation of the conformational energy penalty of protein-bound macrocycles was performed. The macrocycle data set was complemented with linear analogues that are similar either with respect to physicochemical properties or 2D fingerprints. The conformational energy penalties of these linear analogues were calculated and compared to the conformational energy penalties of the macrocycles. The complete data set of macrocycles and non-macrocycles in this study differ from previously published work addressing conformational energy penalties, since it covers a more extended area of chemical space. Furthermore, there was a weak correlation between the calculated conformational energy penalties and the flexibility of the structures

Conformational Analysis of Macrocycles : Comparing General and Specialized Methods

Macrocycles represent an important class of medicinally relevant small molecules due to their interesting biological properties. Therefore, a firm understanding of their conformational preferences is important for drug design. Given the importance of macrocycle-protein modelling in drug discovery, we envisaged that a systematic study of both classical and recent specialized methods would provide guidance for other practitioners within the field. In this study we compare the performance of the general, well established conformational analysis methods Monte Carlo Multiple Minimum (MCMM) and Mixed Torsional/Low-Mode sampling (MTLMOD) with two more recent and specialized macrocycle sampling techniques: MacroModel macrocycle Baseline Search (MD/LLMOD) and Prime macrocycle conformational sampling (PRIME-MCS). Using macrocycles extracted from 44 macrocycle-protein X-ray crystallography complexes, we evaluated each method based on their ability to (i) generate unique conformers, (ii) generate unique macrocycle ring conformations, (iii) identify the global energy minimum, (iv) identify conformers similar to the X-ray ligand conformation after Protein Preparation Wizard treatment (X-ray(ppw)), and (v) to the X-ray(ppw) ring conformation. Computational speed was also considered. In addition, conformational coverage, as defined by the number of conformations identified, was studied. In order to study the relative energies of the bioactive conformations, the energy differences between the global energy minima and the energy minimized X-ray(ppw) structures and, the global energy minima and the MCMM-Exhaustive (1,000,000 search steps) generated conformers closest to the X-ray(ppw) structure, were calculated and analysed. All searches were performed using relatively short run times (10,000 steps for MCMM, MTLMOD and MD/LLMOD). To assess the performance of the methods, they were compared to an exhaustive MCMM search using 1,000,000 search steps for each of the 44 macrocycles (requiring ca 200 times more CPU time). Prior to our analysis, we also investigated if the general search methods MCMM and MTLMOD could also be optimized for macrocycle conformational sampling. Taken together, our work concludes that the more general methods can be optimized for macrocycle modelling by slightly adjusting the settings around the ring closure bond. In most cases, MCMM and MTLMOD with either standard or enhanced settings performed well in comparison to the more specialized macrocycle sampling methods MD/LLMOD and PRIME-MCS. When using enhanced settings for MCMM and MTLMOD, the X-ray(ppw) conformation was regenerated with the greatest accuracy. The, MD/LLMOD emerged as the most efficient method for generating the global energy minima. Graphic abstrac

Docking of Macrocycles: Comparing Rigid and Flexible Docking in Glide

In recent years, there has been an increased interest in using macrocyclic compounds for drug discovery and development. For docking of these commonly large and flexible compounds to be addressed, a screening and a validation set were assembled from the PDB consisting of 16 and 31 macrocycle-containing protein complexes, respectively. The macrocycles were docked in Glide by rigid docking of pregenerated conformational ensembles produced by the macrocycle conformational sampling method (MCS) in Schrödinger Release 2015-3 or by direct Glide flexible docking after performing ring-templating. The two protocols were compared to rigid docking of pregenerated conformational ensembles produced by an exhaustive Monte Carlo multiple minimum (MCMM) conformational search and a shorter MCMM conformational search (MCMM-short). The docking accuracy was evaluated and expressed as the RMSD between the heavy atoms of the ligand as found in the X-ray structure after refinement and the poses obtained by the docking protocols. The median RMSD values for top-scored poses of the screening set were 0.83, 0.80, 0.88, and 0.58 Å for MCMM, MCMM-short, MCS, and Glide flexible docking, respectively. There was no statistically significant difference in the performance between rigid docking of pregenerated conformations produced by the MCS and direct docking using Glide flexible docking. However, the flexible docking protocol was 2-times faster in docking the screening set compared to that of the MCS protocol. In a final study, the new Prime-MCS method was evaluated in Schrödinger Release 2016-3. This method is faster compared that of to MCS; however, the conformations generated were found to be suboptimal for rigid docking. Therefore, on the basis of timing, accuracy, and ease of set up, standard Glide flexible docking with prior ring-templating is recommended over current gold standard protocols using rigid docking of pregenerated conformational ensembles

Pan-NS3 protease inhibitors of hepatitis C virus based on an R-elongated pyrazinone scaffold

Herein, we present the design and synthesis of 2(1H)-pyrazinone based HCV NS3 protease inhibitors and show that elongated Rurea substituents were associated with increased inhibitory potencies over several NS3 protein variants. The inhibitors are believed to rely on β-sheet mimicking hydrogen bonds which are similar over different genotypes and current drug resistant variants and correspond to the β-sheet interactions of the natural peptide substrate. Inhibitor 36, for example, with a urea substituent including a cyclic imide showed balanced nanomolar inhibitory potencies against genotype 1a, both wild-type (K = 30 nM) and R155K (K = 2 nM), and genotype 3a (K = 5 nM).status: publishe

Predicting target profiles with confidence as a service using docking scores

Background: Identifying and assessing ligand-target binding is a core component in early drug discovery as one or more unwanted interactions may be associated with safety issues. Contributions: We present an open-source, extendable web service for predicting target profiles with confidence using machine learning for a panel of 7 targets, where models are trained on molecular docking scores from a large virtual library. The method uses conformal prediction to produce valid measures of prediction efficiency for a particular confidence level. The service also offers the possibility to dock chemical structures to the panel of targets with QuickVina on individual compound basis. Results: The docking procedure and resulting models were validated by docking well-known inhibitors for each of the 7 targets using QuickVina. The model predictions showed comparable performance to molecular docking scores against an external validation set. The implementation as publicly available microservices on Kubernetes ensures resilience, scalability, and extensibility

Achiral Pyrazinone-Based Inhibitors of the Hepatitis C Virus NS3 Protease and Drug-Resistant Variants with Elongated Substituents Directed Toward the S2 Pocket

Herein we describe the design, synthesis, inhibitory potency, and pharmacokinetic properties of a novel class of achiral peptidomimetic HCV NS3 protease inhibitors. The compounds are based on a dipeptidomimetic pyrazinone glycine P3P2 building block in combination with an aromatic acyl sulfonamide in the P1P1′ position. Structure–activity relationship data and molecular modeling support occupancy of the S2 pocket from elongated R⁶ substituents on the 2­(1<i>H</i>)-pyrazinone core and several inhibitors with improved inhibitory potency down to <i>K</i>_i = 0.11 μM were identified. A major goal with the design was to produce inhibitors structurally dissimilar to the di- and tripeptide-based HCV protease inhibitors in advanced stages of development for which cross-resistance might be an issue. Therefore, the retained and improved inhibitory potency against the drug-resistant variants A156T, D168V, and R155K further strengthen the potential of this class of inhibitors. A number of the inhibitors were tested in in vitro preclinical profiling assays to evaluate their apparent pharmacokinetic properties. The various R⁶ substituents were found to have a major influence on solubility, metabolic stability, and cell permeability

Novel Peptidomimetic Hepatitis C Virus NS3/4A Protease Inhibitors Spanning the P2–P1′ Region

Herein, novel hepatitis C virus NS3/4A protease inhibitors based on a P2 pyrimidinyloxyphenylglycine in combination with various regioisomers of an aryl acyl sulfonamide functionality in P1 are presented. The P1′ 4-(trifluoromethyl)­phenyl side chain was shown to be particularly beneficial in terms of inhibitory potency. Several inhibitors with <i>K</i>_i-values in the nanomolar range were developed and included identification of promising P3-truncated inhibitors spanning from P2–P1′. Of several different P2 capping groups that were evaluated, a preference for the sterically congested Boc group was revealed. The inhibitors were found to retain inhibitory potencies for A156T, D168V, and R155K variants of the protease. Furthermore, in vitro pharmacokinetic profiling showed several beneficial effects on metabolic stability as well as on apparent intestinal permeability from both P3 truncation and the use of the P1′ 4-(trifluoromethyl)­phenyl side chain