M-Chem: a Modular Software Package for Molecular Simulation that Spans Scientific Domains.

We present a new software package called M-Chem that is designed from scratch in C++ and parallelized on shared-memory multi-core architectures to facilitate efficient molecular simulations. Currently, M-Chem is a fast molecular dynamics (MD) engine that supports the evaluation of energies and forces from two-body to many-body all-atom potentials, reactive force fields, coarse-grained models, combined quantum mechanics molecular mechanics (QM/MM) models, and external force drivers from machine learning, augmented by algorithms that are focused on gains in computational simulation times. M-Chem also includes a range of standard simulation capabilities including thermostats, barostats, multi-timestepping, and periodic cells, as well as newer methods such as fast extended Lagrangians and high quality electrostatic potential generation. At present M-Chem is a developer friendly environment in which we encourage new software contributors from diverse fields to build their algorithms, models, and methods in our modular framework. The long-term objective of M-Chem is to create an interdisciplinary platform for computational methods with applications ranging from biomolecular simulations, reactive chemistry, to materials research.

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.

Rationalization of the Membrane Permeability Differences in a Series of Analogue Cyclic Decapeptides.

Cyclization and selected backbone N-methylations are found to be often necessary but not sufficient conditions for peptidic drugs to have a good bioavailability. Thus, the design of cyclic peptides with good passive membrane permeability and good solubility remains a challenge. The backbone scaffold of a recently published series of cyclic decapeptides with six selected backbone N-methylations was designed to favor the adoption of a closed conformation with ß-turns and four transannular hydrogen bonds. Although this conformation was indeed adopted by the peptides as determined by NMR measurements, substantial differences in the membrane permeability were observed. In this work, we aim to rationalize the impact of discrete side chain modifications on membrane permeability for six of these cyclic decapeptides. The thermodynamic and kinetic properties were investigated using molecular dynamics simulations and Markov state modeling in water and chloroform. The study highlights the influence that side-chain modifications can have on the backbone conformation. Peptides with a d-proline in the ß-turns were more likely to adopt, even in water, the closed conformation with transannular hydrogen bonds, which facilitates transition through the membrane. The population of the closed conformation in water was found to correlate positively with PAMPA log Pe.

Interconversion Rates between Conformational States as Rationale for the Membrane Permeability of Cyclosporines.

Cyclic peptides have regained interest as potential inhibitors of challenging targets but have often a low bioavailability. The natural product cyclosporine A (CsA) is the textbook exception. Despite its size and polar backbone, it is able to passively cross membranes. This ability is hypothesized to be due to a conformational change from the low-energy conformation in water to a "congruent" conformation that is populated both in water and inside the membrane. Here, we use a combination of NMR measurements and kinetic models based on molecular dynamics simulations to rationalize the difference in the membrane permeability of cyclosporine E (CsE) and CsA. The structure of CsE differs only in a backbone methylation, but its membrane permeability is one order of magnitude lower. The most striking difference is found in the interconversion rates between the conformational states favored in water and in chloroform, which are up to one order of magnitude slower for CsE compared to CsA.

Cross-Linked Collagen Triple Helices by Oxime Ligation.

Covalent cross-links are crucial for the folding and stability of triple-helical collagen, the most abundant protein in nature. Cross-linking is also an attractive strategy for the development of synthetic collagen-based biocompatible materials. Nature uses interchain disulfide bridges to stabilize collagen trimers. However, their implementation into synthetic collagen is difficult and requires the replacement of the canonical amino acids (4R)-hydroxyproline and proline by cysteine or homocysteine, which reduces the preorganization and thereby stability of collagen triple helices. We therefore explored alternative covalent cross-links that allow for connecting triple-helical collagen via proline residues. Here, we present collagen model peptides that are cross-linked by oxime bonds between 4-aminooxyproline (Aop) and 4-oxoacetamidoproline placed in coplanar Xaa and Yaa positions of neighboring strands. The covalently connected strands folded into hyperstable collagen triple helices (Tm ≈ 80 °C). The design of the cross-links was guided by an analysis of the conformational properties of Aop, studies on the stability and functionalization of Aop-containing collagen triple helices, and molecular dynamics simulations. The studies also show that the aminooxy group exerts a stereoelectronic effect comparable to fluorine and introduce oxime ligation as a tool for the functionalization of synthetic collagen.

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 ß-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 µs 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.

Rational design in search for 5-phenylhydantoin selective 5-HT7R antagonists. Molecular modeling, synthesis and biological evaluation.

A series of novel arylpiperazine 5-(4-fluorophenyl)-5-methylhydantoins with 2-hydroxypropyl linker (2-15) was synthesized and evaluated on their affinity towards serotonin 5-HT7 receptor (5-HT7R) in comparison to other closely related GPCRs: serotonin 5-HT1A, and dopamine D2 receptors. The functional activity studied through the measurement of 5-HT7R-mediated cyclic AMP production in Human Embryonic Kidney 293 cells (HEK293) stably expressing human 5-HT7 proved their antagonistic properties. The lead structure was also examined in the preliminary metabolic stability study using human liver microsomes (HMLs). The process of selection of candidates for synthesis was supported by a special molecular modeling workflow including combinatorial library generation, docking, and machine learning-based assessment. Additionally, in silico predictions of selectivity over 5-HT1AR and D2R, as well as functional activity were carried out. The newly synthesized compounds were proved to possess a potent affinity for 5-HT7R, similar to that of the lead structure of 5-(4-fluorophenyl)-3-(3-(4-(2-methoxyphenyl)piperazin-1-yl)-2-hydroxypropyl)-5-methylimidazolidine-2,4-dione (1). For several derivatives, significant selectivity both over 5-HT1AR and D2R was found.

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 ß-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.

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 methods. Graphical abstractComparison of machine learning results obtained for various number of beta-2 AR homology models and crystal structures.

Multi-Step Protocol for Automatic Evaluation of Docking Results Based on Machine Learning Methods--A Case Study of Serotonin Receptors 5-HT(6) and 5-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.

Towards novel 5-HT7versus 5-HT1A receptor ligands among LCAPs with cyclic amino acid amide fragments: design, synthesis, and antidepressant properties. Part II.

A 26-membered library of novel long-chain arylpiperazines, which contained primary and tertiary amides of cyclic amino acids (proline and 1,2,3,4-tetrahydroisoquinoline-3-carboxamide) in the terminal fragment was synthesized and biologically evaluated for binding affinity for 5-HT7 and 5-HT1A receptors. Docking studies confirmed advantages of Tic-amide over Pro-amide fragment for interaction with 5-HT7 receptors. Selected compounds 32 and 28, which behaved as 5-HT7Rs antagonist and 5-HT1A partial agonist, respectively, produced antidepressant-like effects in the forced swim test in mice after acute treatment in doses of 10 mg/kg (32) and 1.25 mg/kg (28). Compound 32 reduced immobility in a manner similar to the selective 5-HT7 antagonist SB-269970.

Impact of template choice on homology model efficiency in virtual screening.

Homology modeling is a reliable method of predicting the three-dimensional structures of proteins that lack NMR or X-ray crystallographic data. It employs the assumption that a structural resemblance exists between closely related proteins. Despite the availability of many crystal structures of possible templates, only the closest ones are chosen for homology modeling purposes. To validate the aforementioned approach, we performed homology modeling of four serotonin receptors (5-HT1AR, 5-HT2AR, 5-HT6R, 5-HT7R) for virtual screening purposes, using 10 available G-Protein Coupled Receptors (GPCR) templates with diverse evolutionary distances to the targets, with various approaches to alignment construction and model building. The resulting models were further validated in two steps by means of ligand docking and enrichment calculation, using Glide software. The final quality of the models was determined in virtual screening-like experiments by the AUROC score of the resulting ROC curves. The outcome of this research showed that no correlation between sequence identity and model quality was found, leading to the conclusion that the closest phylogenetic relative is not always the best template for homology modeling.

An application of machine learning methods to structural interaction fingerprints--a case study of kinase inhibitors.

In this Letter, we present a novel methodology of searching for biologically active compounds, which is based on the combination of docking experiments and analysis of the results by machine learning methods. The study was performed for 5 different protein kinases, and several sets of compounds (active, inactive and assumed inactives) were docked into their targets. The resulting ligand-protein complexes were represented by the means of structural interaction fingerprints profiles (SIFts profiles) that constituted an input for ML methods. The developed protocol was found to be superior to the combination of classification algorithms with the standard fingerprint MACCSFP.