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Molecular dynamics simulations of acetylcholine, in terms of solvent-influenced solute molecules motion, reveal seven clusters of conformers. Data indicate a progressive loss of conformational freedom and a decrease in angular velocity and angular acceleration with increasing molecular weight of the solvents.

The study describes the MetaQSAR tool, a new database engine specifically tailored to collect and analyze metabolic data. This is a plug-in embedded in the VEGA suite of programs (freely downloadable at www.vegazz.net) and takes advantage from all cheminformatics features implemented in the software with additional tools aimed to perform statistical analyses, similarity searches, and physicochemical profiling of the stored molecules. MetaQSAR also implements a novel metabolism classification, which groups the metabolic reactions in 101 classes and can find numerous applications in metabolic analyses. The potentials of MetaQSAR are here assessed by using it to store and analyze an extended database focused on metabolism of xenobiotics, which was collected by manually curated meta-analysis of the recent literature. The database includes 1890 substrates taken from about 1500 original papers in the years 2004-2015. The database was utilized in both physicochemical analyses and similarity searches, so evidencing the notable potentialities of MetaQSAR, which can find particularly fruitful applications in developing targeted predictive approaches. [ABSTRACT FROM AUTHOR]

Leishmaniasis is a chronic disease caused by protozoa of the distinct Leishmania genus transmitted by sandflies of the genus Phlebotomus (old world) and Lutzomyia (new world). Among the molecular factors that contribute to the virulence and pathogenesis of Leishmania are metalloproteases, e.g., glycoprotein 63 (gp63), also known as leishmanolysin or major surface protease (MSP). This protease is a zinc-dependent metalloprotease that is found on the surface of the parasite, abundant in Leishmania promastigote and amastigote. This study describes the prediction of three-dimensional (3D) structures of leishmanolysin (UniProt ID A0A088RJX7) of Leishmania panamensis employing a homology modeling approach. The 3D structure prediction was performed using the SWISS-MODEL web server. The tools PROCHECK, Molprobyty, and Verify3D were used to check the quality of the model, indicating that they are reliable. Best docking configurations were identified applying AutoDock Vina in PyRx 0.8 to obtain a potential antileishmanial activity. Biflavonoids such as lanaroflavone, podocarpusflavone A, amentoflavone, and podocarpusflavone B showed good scores among these molecules. Lanaroflavone appears to be the most suitable compound from binding affinity calculations., Competing Interests: The authors declare no competing financial interest., (Copyright © 2020 American Chemical Society.)

In this work we present the third generation of FAst MEtabolizer (FAME 3), a collection of extra trees classifiers for the prediction of sites of metabolism (SoMs) in small molecules such as drugs, druglike compounds, natural products, agrochemicals, and cosmetics. FAME 3 was derived from the MetaQSAR database ( Pedretti et al. J. Med. Chem. 2018 , 61 , 1019 ), a recently published data resource on xenobiotic metabolism that contains more than 2100 substrates annotated with more than 6300 experimentally confirmed SoMs related to redox reactions, hydrolysis and other nonredox reactions, and conjugation reactions. In tests with holdout data, FAME 3 models reached competitive performance, with Matthews correlation coefficients (MCCs) ranging from 0.50 for a global model covering phase 1 and phase 2 metabolism, to 0.75 for a focused model for phase 2 metabolism. A model focused on cytochrome P450 metabolism yielded an MCC of 0.57. Results from case studies with several synthetic compounds, natural products, and natural product derivatives demonstrate the agreement between model predictions and literature data even for molecules with structural patterns clearly distinct from those present in the training data. The applicability domains of the individual models were estimated by a new, atom-based distance measure (FAMEscore) that is based on a nearest-neighbor search in the space of atom environments. FAME 3 is available via a public web service at https://nerdd.zbh.uni-hamburg.de/ and as a self-contained Java software package, free for academic and noncommercial research.

Recent biochemical and clinical evidence implicates human serum carnosinase in a variety of pathological conditions, such as neurological disorders and diabetic nephropathy, suggesting that this enzyme is of potential interest as a novel medicinal target. The present study was undertaken with a view to model the serum carnosinase and its catalytic site and to unravel the molecular mechanism by which citrate ions increase the catalytic efficiency of serum carnosinase. A homology model of the enzyme was obtained on the basis of beta-alanine synthetase, and its active center was found to bind known substrates carnosine, homocarnosine, and anserine in a binding mode conducive to catalysis. Citrate ions were shown to bind at only three well-defined sites involving both ion pairs and hydrogen bonds. Molecular dynamics simulations evidenced that citrate binding had a remarkable conformational influence on the 3D structure of carnosinase, increasing the binding affinity (i.e., binding score) of carnosine to the catalytic site. This is one of the first reports documenting the molecular mechanism of an allosteric enzyme activator using MD simulations.

The objective of this study was to investigate the reliability of a fragmental approach to build a full-length model of the human ghrelin receptor (hGHS-R1a) in its open state. The soundness of the model was verified by docking the tetrapeptide Gly-Ser-Ser(n-octanoyl)-Phe-NH2, which represents the ghrelin active core, and a dataset of 35 peptidomimetic GH secretagogues taken from literature. Docking results confirm the relevance of two distinct subpockets: a polar cavity bearing the key residues involved in receptor activation and an aromatic/apolar subpocket, which plays a crucial role in determining the high constitutive activity of hGHS-R1a. The docking scores of both subpockets are in remarkable agreement with biological data, emphasizing that the model can be used to predict the activity of novel ligands. Moreover, the subpocket selectivity of peptidomimetic GHSs suggests a cooperative role of the aromatic/apolar subpocket. Taken globally, the results highlight the potential of the fragmental approach to build improved models for any GPCR.

The objective of this study was to investigate the conformational and property spaces of acetylcholine in hydrated octanol and in a membrane model. Molecular dynamics simulations of long duration (15 ns) were carried out, yielding 3000 conformers. For each, we calculated N(+)-C8 distance, solvent-accessible surface area (SAS), polar surface area (PSA), dipole moment, and lipophilicity (virtual logP). Their variations as a function of the dihedral angles tau(2) and tau(3) remained unexpectedly broad and comparable to those seen previously in a vacuum, in water, and in chloroform.(12) Thus, each of the seven conformational clusters was able to access a marked proportion of the lipophilicity space accessible to acetylcholine (0.40 in the logP scale). Histograms of logP distributions revealed two overlapping populations, namely more lipophilic and more hydrophilic. Their deconvolution into two Gaussian curves demonstrated solvent-mediated constraints on the lipophilicity space of acetylcholine, clearly showing how a polar medium favors polar conformers, whereas the opposite is true for media of low polarity.

In this paper, we report the first study aimed at correlating pharmacological properties with molecular parameters derived from the physicochemical property space of bioactive molecules. A dataset of 36 ligands of the alpha(1a)-, alpha(1b)-, and alpha(1d)-adrenoceptors as published by Bremner et al. (Bioorg. Med. Chem. 2000, 8, 201-214) was used. One thousand conformers were generated for each ligand by Monte Carlo conformational analysis, and four 3D-dependent physicochemical properties were computed for each conformer of each ligand, namely virtual lipophilicity (log P), dipole moment, polar surface area (PSA), and solvent-accessible surface area (SAS). Thus, a space of four physicochemical properties was obtained for each ligand. These spaces were assessed by two descriptors, namely their range and their sensitivity (i.e., the variation amplitude of a given physicochemical property for a given variation in molecular geometric properties). Little or no correlation was found to exist between the physicochemical properties and their range or sensitivity, indicating that the latter descriptors do not encode the same molecular information as the former properties. As expected, neither the range nor the sensitivity of any of the four physicochemical properties correlated with receptor affinities. In contrast, range and sensitivity showed promising correlations with deltapK(a-b) (i.e., the alpha(1a)/alpha(1b) selectivity) for the complete dataset. The correlations were lower for deltapK(a-d) (i.e., the alpha(1a)/alpha(1d) selectivity), whereas there was no correlation at all with deltapK(b-d). These results are consistent with the results of Bremner et al., which indicate that the alpha(1a)-AR ligands bind in an extended geometry, whereas the alpha(1b)-AR and alpha(1d)-AR ligands assume more folded conformations. Since the property space descriptors presented here take structural variability into account, their correlation with deltapK(a-b) and deltapK(a-d) indicates that these selectivities are indeed driven by differences in conformational behavior and hence in property spaces.

The objective of this study was, first, to examine the property space of a test molecule and, second, to assess solvent constraints. Acetylcholine was chosen as the object of study given its interesting molecular structure and major biological significance. Molecular dynamics simulations of long duration (30 ns) were carried out with acetylcholine in a vacuum or in a box of solvent (chloroform, water, water plus one chloride counterion). For each of the 6000 conformers stored during each run, various geometric and physicochemical properties were calculated, namely, N(+)-C8 distance, solvent-accessible surface area (SAS), polar surface area (PSA), dipole moment, and lipophilicity (virtual log P). The variations of these properties as a function of the dihedral angles tau(2) and tau(3) were unexpectedly broad for such a small molecule. Dipole moment and virtual log P were well correlated, and they varied in a complex manner with the dihedral angles. For example, each of the seven conformational clusters was able to access much of the lipophilicity space of acetylcholine. Solvent constraints on the property space clearly indicate that a polar medium tends to favor polar conformers, whereas the opposite is true for a solvent of low polarity.

Several natural inhibitors of farnesyl transferase have been reported in the literature: some compounds are competitive with farnesyl pyrophosphate (FPP), whereas other ones are competitive with Ras proteins, even though it is usually hard to highlight their inhibition mechanism, which is still unknown for several natural compounds. The aim of this work is to show that the molecular docking analysis can be successfully used to underline the inhibition mechanism of these natural compounds. First, the selected compounds were subjected to a detailed docking analysis, by means of BioDock, a program able to reveal the most likely binding mode for each ligand. By comparing these results with the binding sites for the natural substrates, earlier determined, it was possible to highlight the site specificity and the inhibition mechanism of the selected compounds. In addition, it is possible to relate the binding mode of these molecules with their lipole values, which is appreciably less for peptidomimetics than for FPP mimetic and reveals a straightforward method to predict and to understand the inhibition mechanism of these natural derivatives.