Multiple conformational states in retrospective virtual screening : homology models vs. crystal structures : beta-2 adrenergic receptor case study

Background: Distinguishing active from inactive compounds is one of the crucial problems of molecular docking, especially in the context of virtual screening experiments. The randomization of poses and the natural flexibility of the protein make this discrimination even harder. Some of the recent approaches to post-docking analysis use an ensemble of receptor models to mimic this naturally occurring conformational diversity. However, the optimal number of receptor conformations is yet to be determined. In this study, we compare the results of a retrospective screening of beta-2 adrenergic receptor ligands performed on both the ensemble of receptor conformations extracted from ten available crystal structures and an equal number of homology models. Additional analysis was also performed for homology models with up to 20 receptor conformations considered. Results: The docking results were encoded into the Structural Interaction Fingerprints and were automatically analyzed by support vector machine. The use of homology models in such virtual screening application was proved to be superior in comparison to crystal structures. Additionally, increasing the number of receptor conformational states led to enhanced effectiveness of active vs. inactive compounds discrimination. Conclusions: For virtual screening purposes, the use of homology models was found to be most beneficial, even in the presence of crystallographic data regarding the conformational space of the receptor. The results also showed that increasing the number of receptors considered improves the effectiveness of identifying active compounds by machine learning method

Imidazolidine-4-one derivatives in the search for novel chemosensitizers of Staphylococcus aureus MRSA : synthesis, biological evaluation and molecular modeling studies

A series of amine derivatives of 5-aromatic imidazolidine-4-ones (7–19), representing three subgroups: piperazine derivatives of 5-arylideneimidazolones (7–13), piperazine derivatives of 5-arylideneimidazolidine-2,4-dione (14–16) and primary amines of 5-naphthyl-5-methylimidazolidine-2,4-diones (17–19), was evaluated for their ability to improve antibiotics effectiveness in two strains of Gram-positive Staphylococcus aureus: ATCC 25923 (a reference strain) and MRSA (methicillin resistant S. aureus) HEMSA 5 (a resistant clinical isolate). The latter compounds (17–19) were obtained by 4-step synthesis using Bucherer-Bergs condensation, two-phase bromoalkylation and Gabriel reactions. The naphthalen derivative: (Z)-5-(naphthalen-2-ylmethylene)-2-(piperazin-1-yl)-3H-imidazol-4(5H)-one (10) was the most potent in combination with β-lactam antibiotics and ciprofloxacin against the resistant strain. The high potency to increase efficacy of oxacillin was noted for (Z)-5-(anthracen-10-ylmethylene)-2-(piperazin-1-yl)-3H-imidazol-4(5H)one (12) too. In order to explain the mechanism of action of the compounds 10 and 12, docking studies with the use of crystal structures of a penicillin binding protein (PBP2a) and MecR1 were carried out. Their outcomes suggested that the most probable mechanism of action of the active compounds is the interaction with MecR1. Molecular dynamic experiments performed for the active compounds and compound 13 (structurally similar to 12) supported this hypothesis and provided possible explanation of activity dependencies of the tested compounds in terms of the restoration of antibiotic efficacy in S. aureus MRSA HEMSA 5

Investigation of the Conformational Behavior of Small Molecules and Peptides

In this thesis, we investigated the conformational behavior of small molecules and peptides, and the influence it has on their properties. An overview of the computational methods which can be used to study thermodynamic and kinetic properties of biomolecular systems, and of the issues which arise when comparing calculated and experimental data, is presented in Chapter 1. The time-dependent evolution of a system can be studied on an atomic level by molecular dynamics (MD) simulations. In order to properly describe the conformational behavior of a molecule in an MD simulation, an accurate sampling of the potential energy surface is required. For this, multiple simulations can be performed starting from diverse structures, which were generated using e.g. enhanced-sampling methods. The kinetic information from multiple unconnected MD trajectories can then be retrieved by Markov state models (MSMs). In MSMs, the conformational space of a system is represented by kinetically metastable sets, and the interconversion rates between these sets are described by implied timescales. In Chapter 2, MD simulations and MSMs in water and chloroform were used to investigate the membrane permeability of cyclosporine A (CsA). CsA is a well known example of a cyclic peptide, which can passively diffuse through membranes, and is used as an anti-inflammatory drug. We sampled the transitions between “open” and “closed” conformations of CsA to understand the interconversion processes that facilitate membrane permeability. The state-of-the-art membrane permeability hypothesis states that only a low-dielectric (“closed”) conformation of a cyclic peptide can cross the apolar interior of a membrane. The MSMs in water and chloroform revealed the existence of two different kinds of “congruent” conformational states, i.e. metastable sets present in both environments. This finding led to an extended hypothesis of membrane permeability, with potentially more than one congruent conformation accessible in water, which can facilitate passive diffusion through a membrane. As the interconversions between the conformations of CsA present in polar and apolar environments proved to be essential for its membrane permeability, in Chapter 3 we investigated if the use of experimental structural information in MD simulations can induce interconversions between “open” and “closed” conformations of CsA. For this, nuclear Overhauser enhancement (NOE) distance restraints were derived from NMR measurements in chloroform. Although the structures present in the ensembles obtained in the restrained simulations resulted in favorable agreement with experimental data, the simulations starting from an “open” conformation in chloroform did not lead to interconversion to the “closed” conformation observed experimentally in chloroform. In Chapter 4, we investigated kinetic properties of cyclosporine E (CsE), a synthetic derivative of CsA. CsA and CsE are an example of a “permeability cliff”, where a small difference in structure (missing methylation of one backbone amide) leads to a significant change in membrane permeability, as the permeability of CsE is one order of magnitude lower than of CsA. The most striking difference between the MSMs of CsA and CsE were the interconversion timescales between the metastable sets. This indicates that the lower membrane permeability of CsE is a result of slower interconversion rates between the open and “congruent” conformations in water. In Chapter 5, we studied a series of six cyclic decapeptides with a constant backbone methylation pattern, but differently substituted side chains. Analogously to the methodology applied in Chapters 2 and 4, MD simulations were performed in water and chloroform. MSMs were constructed using core-set models, which led to a more reliable representation of the kinetically stable conformations. The membrane permeability was found to correlate with the population of “congruent” conformations in the MSMs in water. Chapter 6 describes the investigation of the stability of covalently cross-linked collagen triple helices. The conformational behavior of linker and wild-type collagens was investigated with MD simulations at 300 K and 400 K. Our results indicate that cross-linking increases the conformational stability of triple helices compared to the non-linked variants. The stereochemistry of the linked residues as well as the location of linkers proved to be important factors affecting the stability of the triple helical conformation. Chapter 7 addresses the problem of a proper partial charge estimation for small molecules. All MD simulations of peptides described in previous Chapters were performed using a force field developed for biomolecules. Small molecules, however, require individual parameterization strategies. Bonded and van der Waals parameters are usually taken from biomolecular force fields using a matching strategy, while partial charges are derived from quantum-mechanical (QM) calculations. In Chapter 7, we investigated the impact of the specific structures used to derive the partial charges. The effect of the partial charges obtained from these structures was quantified by comparing calculated and experimental hydration free energies. The results show that the choice of the input geometry for which the charges are derived has a significant impact on the obtained values and the resulting properties of the molecule. Chapter 8 discusses possible directions of research to extend and validate studies presented in this thesis, especially to understand membrane permeability of cyclic peptides in order to facilitate their rational design. Additionally, alternative approaches to obtain starting conformations, which sample the conformational space more efficiently, are proposed

Application of interaction profiles and machine learning in virtual screening

Badania prowadzone w ramach wirtualnego screeningu dostarczają dużej ilości informacji na temat oddziaływania potencjalnych ligandów z ich receptorami. W celu wyeliminowania trudności związanych z analizą danych stosuje się metody wspomagane komputerowo. Jedną z nich stanowi Fingerprint Oddziaływań Strukturalnych (ang. Strcuctural Interaction Fingerprint – SIFt) będący poręcznym narzędziem służącym do analizy wyników dokowania. Fingerprint składa się z dziewięciobitowych binarnych fragmentów, kodujących informacje na temat reszt aminokwasowych zaangażowanych w oddziaływanie określonego typu. Uśrednienie fingerprintów otrzymanych na podstawie kilku kompleksów ligand – białko, prowadzi do utworzenia profilu oddziaływań konkretnego związku chemicznego.Celem pracy jest zastosowanie profili SIFt dla rozróżniania między ligandami aktywnymi a nieaktywnymi dla danego białka docelowego. Profile oddziaływań uzyskane na podstawie SIFt-ów, osobno dla aktywnych i nieaktywnych związków, zostały poddane procesowi ewaluacji poprzez algorytmy uczenia maszynowego. Na tej podstawie oceniono, czy możliwe jest odróżnienie związków o różnych aktywnościach na bazie profili SIFt.Dodatkowo, opisana metodologia została porównana z zastosowaniem fingerprintów molekularnych (MACCS), opisujących strukturę liganda, ale nie oddziaływania w jakie jest zaangażowany.Wyniki uzyskane z zastosowaniem uczenia maszynowego udowadniają, że możliwe jest skuteczne odróżnienie związków aktywnych od nieaktywnych na bazie profili SIFt.Virtual screening provides a vast amount of information about possible binding modes of potential ligands to their biological targets. Structural Interaction Fingerprint (SIFt) constitutes a handy and extremely supportive tool for post-docking analysis. It can be utilized to store, organize and mine vast amount of data obtained in a result of docking studies. SIFt fingerprint consists of 9-bit binary fragments, providing information about protein residues involved in interaction, and moreover, the interaction type. A collection of such fingerprints can be merged into an averaged string, showing frequencies of each interaction. In presented work, we propose a possibility of profile construction, basing on SIFt strings selected from multiple binding complexes, but for one ligand at the time. SIFt profiles were utilized to distinguish between active and inactive compounds. SIFt-based interaction profiles, for active and inactive ligands were evaluated by machine learning algorithms, that determined whether compounds bearing distinct activities could be discriminated. Finally, SIFt profiles performance in virtual screening was compared with molecular fingerprints (MACCS), that describe structure of compound, but not interactions it is involved in.Interaction profiles built on the basis of structural interaction fingerprints proved to be a viable tool for discrimination between active and inactive compounds

Improving Conformer Generation for Small Rings and Macrocycles Based on Distance Geometry and Experimental Torsional-Angle Preferences

The conformer generator ETKDG is a stochastic search method that utilizes distance geometry together with knowledge derived from experimental crystal structures. It has been shown to generate good conformers for acyclic, flexible molecules. This work builds on ETKDG to improve conformer generation of molecules containing small or large aliphatic (i.e., non-aromatic) rings. For one, we devise additional torsional-angle potentials to describe small aliphatic rings and adapt the previously developed potentials for acyclic bonds to facilitate the sampling of macrocycles. However, due to the larger number of degrees of freedom of macrocycles, the conformational space to sample is much broader than for small molecules, creating a challenge for conformer generators. We therefore introduce different heuristics to restrict the search space of macrocycles and bias the sampling toward more experimentally relevant structures. Specifically, we show the usage of elliptical geometry and customizable Coulombic interactions as heuristics. The performance of the improved ETKDG is demonstrated on test sets of diverse macrocycles and cyclic peptides. The code developed here will be incorporated into the 2020.03 release of the open-source cheminformatics library RDKit.ISSN:1549-9596ISSN:0095-2338ISSN:1520-514

Cross-Linked Collagen Triple Helices by Oxime Ligation

ISSN:0002-7863ISSN:1520-512

Kinetic Models of Cyclosporin A in Polar and Apolar Environments Reveal Multiple Congruent Conformational States

The membrane permeability of cyclic peptides and peptidomimetics, which are generally larger and more complex than typical drug molecules, is likely strongly influenced by the conformational behavior of these compounds in polar and apolar environments. The size and complexity of peptides often limit their bioavailability, but there are known examples of peptide natural products such as cyclosporin A (CsA) that can cross cell membranes by passive diffusion. CsA is an undecapeptide with seven methylated backbone amides. Its crystal structure shows a "closed" twisted beta-pleated sheet conformation with four intramolecular hydrogen bonds that is also observed in NMR measurements of CsA in chloroform. When binding to its target cyclophilin, on the other hand, CsA adopts an "open" conformation without intramolecular hydrogen bonds. In this study, we attempted to sample the complete conformational space of CsA in chloroform and in water by molecular dynamics simulations in order to better understand its conformational behavior in these two environments and to rationalize the good membrane permeability of CsA observed experimentally. From 10 mus molecular dynamics simulations in each solvent, Markov state models were constructed to characterize the metastable conformational states. The model in chloroform is compared to nuclear Overhauser effect NMR spectroscopy data reported in this study and taken from the literature. The conformational landscapes in the two solvents show significant overlap but also clearly distinct features

Multi-step protocol for automatic evaluation of docking results based on machine learning methods : a case study of serotonin receptors 5−HT65-HT_6 and 5−HT75-HT_7

Molecular docking, despite its undeniable usefulness in computer-aided drug design protocols and the increasing sophistication of tools used in the prediction of ligand–protein interaction energies, is still connected with a problem of effective results analysis. In this study, a novel protocol for the automatic evaluation of numerous docking results is presented, being a combination of Structural Interaction Fingerprints and Spectrophores descriptors, machine-learning techniques, and multi-step results analysis. Such an approach takes into consideration the performance of a particular learning algorithm (five machine learning methods were applied), the performance of the docking algorithm itself, the variety of conformations returned from the docking experiment, and the receptor structure (homology models were constructed on five different templates). Evaluation using compounds active toward 5-HT₆ and 5-HT₇ receptors, as well as additional analysis carried out for beta-2 adrenergic receptor ligands, proved that the methodology is a viable tool for supporting virtual screening protocols, enabling proper discrimination between active and inactive compounds