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Sigma-1 receptor (S1R) is involved in a large array of biological functions due to its ability to interact with various proteins and ion channels. Crystal structures of human S1R revealed the trimeric organization for which each protomer comprises the ligand binding pocket. This study applied a multistep computational procedure to develop a pharmacophore model obtained from molecular dynamics simulations of available cocrystal structures of well-known S1R ligands. Apart from the well-established positive ionizable and hydrophobic features, the obtained model included an additional specific hydrophobic feature and different excluded volumes, thus increasing the selectivity of the model as well as a more detailed determination of the distance between two essential features. The obtained pharmacophore model passed the validation test by receiver operating characteristic (ROC) curve analysis of active and inactive S1R ligands. Finally, the pharmacophoric performance was experimentally investigated through the synthesis and binding assay of new 4-phenylpiperazine-based compounds. The most active new ligand 2-(3-methyl-1-piperidyl)-1-(4-phenylpiperazin-1-yl)ethanone ( 3 ) showed an S1R affinity close to the reference compound haloperidol ( K i values of 4.8 and 2.6 nM, respectively). The proposed pharmacophore model can represent a useful tool to design and discover new potent S1R ligands.

The importance of taking into account protein flexibility in drug design and virtual ligand screening (VS) has been widely debated in the literature, and molecular dynamics (MD) has been recognized as one of the most powerful tools for investigating intrinsic protein dynamics. Nevertheless, deciphering the amount of information hidden in MD simulations and recognizing a significant minimal set of states to be used in virtual screening experiments can be quite complicated. Here we present an integrated MD-FLAP (molecular dynamics-fingerprints for ligand and proteins) approach, comprising a pipeline of molecular dynamics, clustering and linear discriminant analysis, for enhancing accuracy and efficacy in VS campaigns. We first extracted a limited number of representative structures from tens of nanoseconds of MD trajectories by means of the k-medoids clustering algorithm as implemented in the BiKi Life Science Suite ( http://www.bikitech.com [accessed July 21, 2015]). Then, instead of applying arbitrary selection criteria, that is, RMSD, pharmacophore properties, or enrichment performances, we allowed the linear discriminant analysis algorithm implemented in FLAP ( http://www.moldiscovery.com [accessed July 21, 2015]) to automatically choose the best performing conformational states among medoids and X-ray structures. Retrospective virtual screenings confirmed that ensemble receptor protocols outperform single rigid receptor approaches, proved that computationally generated conformations comprise the same quantity/quality of information included in X-ray structures, and pointed to the MD-FLAP approach as a valuable tool for improving VS performances.

The serum/glucocorticoid-inducible kinase 1 (Sgk1) has demonstrated antiapoptotic function and the capability to regulate cell survival, proliferation, and differentiation. A pivotal role of Sgk1 in carcinogenesis and in resistance to anticancer therapy has been suggested. With the aim of identifying new Sgk1 modulators, 322 pyrazolo-pyrimidine derivatives have been virtually screened with respect to a crystallographic model of Sgk1. The top five ranked compounds have been evaluated demonstrating Sgk1 inhibition in vitro and selectivity compared to RAC-alpha serine/threonine-protein kinase (Akt1).

Oxovitisin is a natural antioxidant present in aged wine and comes from the chemical transformation undergone by anthocyanins and pyranoanthocyanins. Its antioxidant radical scavenging capacity was theoretically explored by density functional theory (DFT)/B3LYP methods. The O-H bond dissociation energy (BDE), the ionization potential (IP), the proton affinity (PA), and the metal-oxovitisin binding energy (BE) parameters were computed in the gas-phase and in water and benzene solutions. Results provided molecular insight into factors that influence radical scavenging potential of this new class of anthocyanins.

To date, published pharmacophore elucidation approaches typically use a handful of data sets for validation: here, we have assembled a data set for 81 targets, containing 960 ligands aligned using their cocrystallized protein targets, to provide the experimental "gold standard". The two-dimensional structures are also assembled to remove conformational bias; an ideal method would be able to take these structures as input, find the common features, and reproduce the bioactive conformations and their alignments to correspond with the X-ray-determined gold standard alignments. Here we present this data set and describe three objective measures to evaluate performance: the ability to identify the bioactive conformation, the ability to identify and correctly align this conformation for 50% of the molecules in each data set, and the pharmacophoric field similarity. We have applied this validation methodology to our pharmacophore elucidation method FLAPpharm, that is published in the first paper of this series and discuss the limitations of the data set and objective success criteria. Starting from two-dimensional structures and producing unbiased models, FLAPpharm was able to identify the bioactive conformations for 67% of the ligands and also to produce successful models according to the second metric for 67% of the Pharmbench data sets. Inspection of the unsuccessful models highlighted the limitation of this root mean square (rms)-derived metric, since many were found to be pharmacophorically reasonable, increasing the overall success rate to 83%. The PharmBench data set is available at http://www.moldiscovery.com/PharmBench , along with a web service to enable users to score model alignments coming from external methods in the same way that we have presented here and, therefore, establishes a pharmacophore elucidation benchmark data set available to be used by the community.

A computer-aided design of novel asymmetric pyrazoles with improved enantioselective properties was performed by docking experiments starting from a model of Chiralcel OJ chiral in the stationary phase. Synthesis and HPLC experiments confirmed the theoretical prediction and led to a detailed investigation of the enantioselective recognition process. For the first time, looking at the time spent by each enantiomer in contact with the CSP during long molecular dynamic simulations, the experimental analytical trend has been reproduced., (© 2012 American Chemical Society)

The current strategy to improve the quality of life of Human Immunodeficiency Virus (HIV) infected individuals through suppressing viral replication and maintaining the virus at low to undetectable levels is based on highly active antiretroviral therapy (HAART). Protease inhibitors are essential components of most HAART protocols and are often used as the first line of treatment. However, a considerable percentage of new HIV-1 infections are caused by viruses carrying antiretroviral drug-resistant mutations. In this paper molecular dynamics, docking simulations, and free energy analysis of mutated HIV protease complexes were used to estimate the influence of different drug resistance-associated mutations in lopinavir, amprenavir, saquinavir, and atazanavir protease recognition. In agreement with virological and clinical data, the structural analysis showed that the single mutations V82A, I84V, and M46I are associated with higher energetic values for all analyzed complexes with respect to wild-type, indicating their decreased stability. Interestingly, in atazanavir complexes, in the presence of the L76V substitution, the drug revealed a more productive binding affinity, in agreement with hypersusceptibility data.