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The purpose of the article is to offer an overview of the latest release of the VEGA suite of programs. This software has been constantly developed and freely released during the last 20 years and has now reached a significant diffusion and technology level as confirmed by the about 22 500 registered users. While being primarily developed for drug design studies, the VEGA package includes cheminformatics and modeling features, which can be fruitfully utilized in various contexts of the computational chemistry. To offer a glimpse of the remarkable potentials of the software, some examples of the implemented features in the cheminformatics field and for structure-based studies are discussed. Finally, the flexible architecture of the VEGA program which can be expanded and customized by plug-in technology or scripting languages will be described focusing attention on the HyperDrive library including highly optimized functions. Availability and implementation The VEGA suite of programs and the source code of the VEGA command-line version are available free of charge for non-profit organizations at http://www.vegazz.net. [ABSTRACT FROM AUTHOR]

ABSTRACT (1 R)-Normetanephrine is the natural stereoisomeric substrate for sulfotransferase 1A3 (SULT1A3)-catalyzed sulfonation. Nothing appears known on the enantioselectivity of the reaction despite its potential significance in the metabolism of adrenergic amines and in clinical biochemistry. We confronted the kinetic parameters of the sulfoconjugation of synthetic (1 R)-normetanephrine and (1 S)-normetanephrine by recombinant human SULT1A3 to a docking model of each normetanephrine enantiomer with SULT1A3 and the 3′-phosphoadenosine-5′-phosphosulfate cofactor on the basis of molecular modeling and molecular dynamics simulations of the stability of the complexes. The KM, Vmax, and kcat values for the sulfonation of (1 R)-normetanephrine, (1 S)-normetanephrine, and racemic normetanephrine were similar. In silico models were consistent with these findings as they showed that the binding modes of the two enantiomers were almost identical. In conclusion, SULT1A3 is not substrate-enantioselective toward normetanephrine, an unexpected finding explainable by a mutual adaptability between the ligands and SULT1A3 through an 'induced-fit model' in the catalytic pocket. Chirality, 25:28-34, 2012.© 2012 Wiley Periodicals, Inc. [ABSTRACT FROM AUTHOR]

Abstract: Over the last years, considerable progress has been made for the identification and characterization of drug transporters, and several modeling studies have been undertaken to predict their effects on ADME profiling. Thus, this study was focused on the peptide transporter hPepT2, which influences the regional pharmacokinetics in brain, the reabsorption from renal tubular fluid and the pulmonary delivery. A reliable model for hPepT2 was generated by fragments based on the resolved structure of the homologue lactose permease LacY and the structure is made available as . The interaction capacities of such a model were explored by docking a set of 75 known ligands. Docking results underlined the predilection of hPepT2 for highly hydrophobic ligands and the key role of ionic interactions elicited by both charged termini. The docking results were further verified developing a pharmacophore model which clarified the key features required for an optimal hPepT2 affinity and confirmed the main factors governing the hPepT2/hPepT1 selectivity. The soundness of the docking results and the agreement with the pharmacophore mapping afford an encouraging validation for the proposed hPepT2 model and suggest that it can be conveniently exploited to design peptide-like molecules with an improved affinity for this transporter. [Copyright &y& Elsevier]

Abstract: Metabolic problems lead to numerous failures during clinical trials, and much effort is now devoted in developing in silico models predicting metabolic stability and metabolites. Such models are well known for cytochromes P450 and some transferases, whereas little has been done to predict the hydrolytic activity of human hydrolases. The present study was undertaken to develop a computational approach able to predict the hydrolysis of novel esters by human carboxylesterase hCES1. The study involves both docking analyses of known substrates to develop predictive models, and molecular dynamics (MD) simulations to reveal the in situ behavior of substrates and products, with particular attention being paid to the influence of their ionization state. The results emphasize some crucial properties of the hCES1 catalytic cavity, confirming that as a trend with several exceptions, hCES1 prefers substrates with relatively smaller and somewhat polar alkyl/aryl groups and larger hydrophobic acyl moieties. The docking results underline the usefulness of the hydrophobic interaction score proposed here, which allows a robust prediction of hCES1 catalysis, while the MD simulations show the different behavior of substrates and products in the enzyme cavity, suggesting in particular that basic substrates interact with the enzyme in their unprotonated form. [Copyright &y& Elsevier]

Abstract: The aim of this study was to generate a reliable model for the homotetrameric structure of the human TRPM8 cation channel, a temperature sensor involved in innocuous cold perceptions. The described model was generated using a fragmental strategy and its interaction capacities were explored by docking a representative set of ligands. The analysis of the quaternary structure suggests that the N-terminus possesses a solenoidal topology which could be involved in tetramerization due to its electrostatic characteristics. Again, the tetramer model unveils a precise fitting between the segments of neighbouring monomers affording attractive suggestions for the multifaceted mechanism of channel gating. Docking results are in convincing agreement with mutational analyses and confirm that S4 and S4–S5 linker play a key role in channel activation. Overall, the proposed model could find fertile applications to further investigate the gating mechanism and to design truly selective ligands able to clarify the pathophysiological roles of the TRPM8 channel. [Copyright &y& Elsevier]

Abstract: Recent biochemical and clinical evidences unveiled that DHCR24 enzyme (3-β-hydoxysterol-Δ24-reductase, also named seladin-1), which catalyzes the last step of the cholesterol biosynthesis, is implicated in relevant neuroprotective processes by modulating the level of cholesterol in membrane. The present study was undertaken with a view to model the DHCR24 enzyme and its catalytic site, analyzing the substrate recognition at an atomic level. A homology model of the enzyme was obtained based on plant Cytokinin Dehydrogenase, and its active site was found to bind the desmosterol plus a set of post-squalenic intermediates of the cholesterol biosynthesis in a binding mode conducive to catalysis, even if the docking results suggested that the enzyme has a clear preference for the last intermediates of such biosynthetic pathway. Since DHCR24 possesses a putative transmembrane segment, the enzyme was, then, inserted in a suitable membrane model and the membrane-anchored structure in complex with desmosterol and cholesterol underwent 10ns MD simulations. Such simulations evidenced a clearly different behavior between substrate and product since the product only completely leaves the catalytic cavity whereas desmosterol firmly conserves its pivotal interactions during all simulation time. This is one of the first reports documenting the enzymatic product egress using simple MD simulations in which all atoms are free to move. [Copyright &y& Elsevier]

Abstract: The three-dimensional structure of full-length structure of the M1 muscarinic receptor was obtained through the fragmental homology modeling procedure. A 10-ns molecular dynamics (MD) simulation of the protein imbedded in a lipid slab and surrounded by water molecules was further used to relax the model. It was found that the homology model corresponded to the conformation in the ground state, since no significant motions of the backbone of transmembrane domains were observed. Furthermore, the reliability of the model was validated by analyzing key inter-helical contacts, sidechain–sidechain interactions, the formation of stable aromatic microdomains (clusters) and the docking of acetylcholine to its binding site. Moreover, a few conserved interactions observed in the X-ray structure of rhodopsin, such as inter-helical sidechain–sidechain hydrogen bonds were accurately reproduced in the MD simulation. The coupling of ACh to its binding site was found to be dominated by π-cation and salt bridge interactions, while its conformational space was restrained through van der Waals and hydrogen bond interactions. In general, such features were in very good agreement with the available experimental as well as with theoretical data. Considering the above, the structural information obtained in this study can be used a starting point to investigate the activation mechanism of the receptor and the ability to develop selective agonists and allosteric modulators which could be used for the treatment of Alzheimer’s disease. [Copyright &y& Elsevier]

The recent availability of crystal structure of bovine rhodopsin offers new opportunities in order to approach the construction of G protein coupled receptors. This study focuses the attention on the modeling of full-length α1a adrenergic receptor (α1a-AR) due to its biological role and significant implications in pharmacological treatment of benign prostate hyperplasia. This work could be considered made up by two main steps: (a) the construction of full structure of α1a-AR, through homology modeling methods; (b) the automated docking of an endogenous agonist, norepinephrine, and of an antagonist, WB-4101, using BioDock program. The obtained results highlight the key residues involved in binding sites of both agonists and antagonists, confirming the mutagenesis data and giving new suggestions for the rational design of selective ligands. [Copyright &y& Elsevier]

The updated author contribution is as follows: Conceptualization, A.A., G.B. and G.A.; Data curation, A.A., G.B., C.B. and G.A.; Formal analysis, A.A., G.B., M.B. (Marta Balbinot), A.M.F., B.Z. and M.B. (Maura Brioschi); Funding acquisition, M.C., C.B. and G.A.; Investigation, A.A., G.B. and A.P.; Methodology, A.A., G.B., M.B. (Marta Balbinot), A.M.F. and M.B. (Maura Brioschi); Project administration, C.B. and G.A.; Resources, M.C., C.B. and G.A.; Supervision, A.A., G.B., C.B. and G.A.; Validation, A.A., G.B. and M.B. (Maura Brioschi); Writing - original draft, A.A., C.B. and G.A.; Writing - review and editing, A.A., M.C., P.A., A.P., C.B. and G.A. All authors have read and agreed to the published version of the manuscript. In-Depth AGE and ALE Profiling of Human Albumin in Heart Failure: Ex Vivo Studies. Reference 1 Altomare A., Baron G., Balbinot M., Martínez Fernández A., Pedretti A., Zoanni B., Brioschi M., Agostoni P., Carini M., Banfi C. In-Depth AGE and ALE Profiling of Human Albumin in Heart Failure: Ex Vivo Studies. [Extracted from the article]

(1) Background: Machine learning algorithms are finding fruitful applications in predicting the ADME profile of new molecules, with a particular focus on metabolism predictions. However, the development of comprehensive metabolism predictors is hampered by the lack of highly accurate metabolic resources. Hence, we recently proposed a manually curated metabolic database (MetaQSAR), the level of accuracy of which is well suited to the development of predictive models. (2) Methods: MetaQSAR was used to extract datasets to predict the metabolic reactions subdivided into major classes, classes and subclasses. The collected datasets comprised a total of 3788 first-generation metabolic reactions. Predictive models were developed by using standard random forest algorithms and sets of physicochemical, stereo-electronic and constitutional descriptors. (3) Results: The developed models showed satisfactory performance, especially for hydrolyses and conjugations, while redox reactions were predicted with greater difficulty, which was reasonable as they depend on many complex features that are not properly encoded by the included descriptors. (4) Conclusions: The generated models allowed a precise comparison of the propensity of each metabolic reaction to be predicted and the factors affecting their predictability were discussed in detail. Overall, the study led to the development of a freely downloadable global predictor, MetaClass, which correctly predicts 80% of the reported reactions, as assessed by an explorative validation analysis on an external dataset, with an overall MCC = 0.44. [ABSTRACT FROM AUTHOR]

The 3CL-Protease appears to be a very promising medicinal target to develop anti-SARS-CoV-2 agents. The availability of resolved structures allows structure-based computational approaches to be carried out even though the lack of known inhibitors prevents a proper validation of the performed simulations. The innovative idea of the study is to exploit known inhibitors of SARS-CoV 3CL-Pro as a training set to perform and validate multiple virtual screening campaigns. Docking simulations using four different programs (Fred, Glide, LiGen, and PLANTS) were performed investigating the role of both multiple binding modes (by binding space) and multiple isomers/states (by developing the corresponding isomeric space). The computed docking scores were used to develop consensus models, which allow an in-depth comparison of the resulting performances. On average, the reached performances revealed the different sensitivity to isomeric differences and multiple binding modes between the four docking engines. In detail, Glide and LiGen are the tools that best benefit from isomeric and binding space, respectively, while Fred is the most insensitive program. The obtained results emphasize the fruitful role of combining various docking tools to optimize the predictive performances. Taken together, the performed simulations allowed the rational development of highly performing virtual screening workflows, which could be further optimized by considering different 3CL-Pro structures and, more importantly, by including true SARS-CoV-2 3CL-Pro inhibitors (as learning set) when available. [ABSTRACT FROM AUTHOR]