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In this respect, DTC is an ideal test case to investigate the interplay of dispersive and electrostatic interactions, as it lacks strong hydrogen bonds and does not undergo structural phase transitions in the T range here explored. Lowering T implies just a reduction of the intermolecular centre of mass distances, and does not significantly affect conformations and mutual orientations of neighboring molecules.
Accordingly, most intermolecular close contacts are similar in the two polymorphs. This rotation is kinetically hindered in the solid state, as the system is monotropic in the K — RT range, with the phase B as the metastable one. A hypothetical conformational change in the asymmetric unit is never compatible with the pristine crystal lattice, as it produces unfavorable intermolecular interactions that must be relieved at the expense of the lattice cohesive energy.
In both polymorphs, the T -driven reduction of the cell volume increases the strength of electrostatic interactions and, in particular, decreases the atomic dipolar polarization terms and cause the total molecular dipole moment to increase. At the same time, dispersive interactions become less effective, as repulsive terms are enhanced by closer proximity of neighbouring molecules at lower temperatures.
These results do not depend on the specific computational method employed to decompose the crystal cohesive energy into individual NCI terms. This conformity of views provides in our opinion solid grounds to our interpretation of crystal field effects of DTC as dominated by electrostatics, and in particular by permanent molecular dipole moments. The presence of strong hydrogen bond patterns, as well as more than one molecule in the asymmetric unit, would likely complicate the picture.
Structural and energetic investigations as a function of T on other suitable test cases will provide further insights on this topic. In conclusion, we showed that multi- T single-crystal X-ray diffraction experiments, combined with quantum mechanical and semiempirical calculations, allow to go a step further with respect to the static view provided by usual computational polymorph screening, which focuses on the most stable structures at 0 K and does not explicitly take into account finite- T effects.
In addition, the present study provides accurate experimental molecular geometries and thermal expansion coefficient estimates in a couple of subtly different polymorphs, and might be useful as a reference test bench for predictive calculations of thermal properties and crystal structures.
The Authors thank prof. Clerici for provision of the title compound. Thanks are also due to prof. Gavezzotti for fruitful discussions. Colombo of the Italian CNR is also to be warmly thanked for technical assistance. CCDC — RSC Adv. Published online Nov Ahmed M. Author information Article notes Copyright and License information Disclaimer. Corresponding author. Received Sep 28; Accepted Nov 7. Introduction Different polymorphs have different intensive physical properties.
Open in a separate window. Scheme 1. Methods 2. Rigid body analysis and hydrogen atoms treatment The experimental atomic coordinates were corrected for the rigid-body libration of the asymmetric unit, 27 as computed through the Schomaker and Trueblood TLS decomposition procedure 28 of the experimental Debye—Waller tensors.
Solid-state quantum simulations Following commonly accepted procedures, 33—36 single-point periodic DFT calculations were carried out on the accurate X-ray structures of DTC polymorphs Section 2. In vacuo quantum simulations The Gaussian09 program 49 was employed throughout. Quality assessment and reproducibility CCDC — contain the full supplementary crystallographic data for this work.
Results and discussion 3. Molecular conformation The title compound is chiral and its known A and B polymorphs are both centrosymmetric racemates. Inset: best least-squares superposition of the two asymmetric units, overlaid onto the thiazete group, with the corresponding root mean square deviation. Crystal packing Fig. The plot shows the frequency of intermolecular pairs of internal d i and external d e distances describing the closest atomic contacts with the in-crystal molecular Hirshfeld surface.
Light blue points mean increasing frequency. Conformational interconversion paths In the gas phase, the two DTC conformers have essentially the same energy after full coordinate optimization. Virtual polymorph screening To gain insights into the relative interplay of molecular conformation and crystal packing, either conformer was fully optimized within the lattice of the other one, 19 comparing the virtual structures so generated with the actual ones.
Standard convergence criteria 37 on energy and its gradient were employed. Lattice energies take into account the conformational relaxation of the asymmetric unit and are expressed in kcal per mol per molecule.
Lattice cohesive energies and energy ranking Fig. Lattice cohesive energies, E coh , of the A blue rhombi and B red triangles DTC polymorphs per molecule, as evaluated by periodic calculations at the M06 pob-TZVP theory level, from the experimental structures as a function of T. Energy decomposition To gain insights into the relative weight of different NCI in determining the cohesive energy of the two crystal forms, it is desirable to decompose E coh into terms that can be associated with specific physical-chemical classes of intermolecular interactions.
Measure units are Kelvin, Debye and degrees. Values normalized to RT estimates are provided. The lines are guides for the eye. Conflicts of interest There are no conflicts of interest to declare. Acknowledgments The Authors thank prof.
References Jones A. Chattopadhyay B. Geerts Y. Resel R. Weber, Springer-Verlag: Berlin-Heidelberg, , vol. Bernstein J. Process Res. Acta Crystallogr. B: Struct. Filippini G. Trask A. Motherwell W. Jones W. Fox, M. Labes and A. Weissberger, Interscience Publishers, New York, ; vol.
Cooper R. Adjiman C. Bhattacharya S. Boese A. Brandenburg J. Bygrave P. Bylsma R. Campbell J. Car R. Leusen F. Kendrick J. Reutzel-Edens S. Drug Discovery Today. Soave R. Destro R. Sartirana E. Loconte L. Colombo P. To each SMILES, a random endpoint value was associated in order for the software to compare this value with the predicted one. The following regression was used for predicting the endpoints:.
All of the optimized structures were imported into the software Forge v The negative, positive, shape, and hydrophobic field points of each molecule were generated using the extended electron distribution XED force field in Forge.
The molecules were then aligned with the training set of the 3D-QSAR model by a maximum common substructure algorithm using a customized set-up. The maximum number of conformations that was generated for each molecule was set to , as suggested by the software. The gradient cutoff for conformer minimization was set to 0. The energy window was set to 2. Conformers with a minimized energy outside the energy window were discarded.
In the end, an optimized hybrid model was built through iteratively replacing bad regions in the top scoring model with the corresponding fragments from the other models. The simulation was set up automatically by first scanning the protein for exposed transmembrane helices [i. The major axis vectors of these helices i. The temporary protein scaling, which was needed to avoid the deletion of too many lipids around the protein, was then slowly removed during a short simulation at K in vacuum.
The protein with all of the atoms kept fixed was scaled by 1. As soon as the protein had reached its original size again, the protein side-chain p K a s were predicted, protonation states were assigned according to pH 7.
The main simulation was then run with PME, and an 8. The source code of this simulation protocol and visualizations of the individual steps can be found at www.
Docking experiments were effectuated, employing the AutoDock 4. The maps were generated by the program AutoGrid 4. All of the parameters were inserted at their default settings. In vitro and biological screenings of the most promising compounds are in due course.
Free academic licenses from Cresset and ChemAxon for their suites of programs are gratefully acknowledged. Conceptualization, G. Mar Drugs. Published online Oct Find articles by Carla Barbaraci. Find articles by Davide Gentile.
Find articles by Rita Turnaturi. Find articles by Agostino Marrazzo. Find articles by Antonio Rescifina. Author information Article notes Copyright and License information Disclaimer.
Received Oct 1; Accepted Oct Associated Data Supplementary Materials marinedrugss Abstract Sigma receptors are a fascinating receptor protein class whose ligands are actually under clinical evaluation for the modulation of opioid analgesia and their use as positron emission tomography radiotracers. Keywords: virtual screening, database marine products, sigma-2 receptor, sigma-2 receptor ligands. Results and Discussion 2. Open in a separate window. Homology Model and Molecular Docking To further reinforce the results obtained through the 2D and 3D filters, we decided to add a third filter based on molecular docking.
Figure 1. Table 2 Structure and calculated p K i values of the 15 most potent marine products resulted from docking. Figure 2. Table 3 Structure, calculated mean p K i , and corresponding K i nM values of the 15 most potent marine products resulted from the mean of the three combined filters. Materials and Methods 3. Figure 3. Click here for additional data file. Author Contributions Conceptualization, G. Funding This research received no external funding. Conflicts of Interest The authors declare no conflict of interest.
References 1. Martin W. The effects of morphine- and nalorphine-like drugs in the nondependent and morphine-dependent chronic spinal dog. Hellewell S. Rat liver and kidney contain high densities of sigma 1 and sigma 2 receptors: Characterization by ligand binding and photoaffinity labeling. The sigma-1 receptor chaperone as an inter-organelle signaling modulator. Trends Pharmacol. Hashimoto K. Sigma-1 receptor chaperone and brain-derived neurotrophic factor: Emerging links between cardiovascular disease and depression.
Hanner M. Purification, molecular cloning, and expression of the mammalian sigma1-binding site. Schmidt H. Crystal structure of the human sigma1 receptor.
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