Quantum molecular modeling of melatonin

Molecular modeling of melatonin was conducted using AM1 and ab initio quantum computations. Our study shows that four minimal energy conformations (noted I to IV) with a folded ethylamido side-chain are possible. These conformations are in correspondance (form I with form II, and form III with form IV) and are characterized by equivalent orbital frontiers and electrostatic properties. Conformations II and IV are in good agreement with pharmacophore models proposed by Jansen et al. [Bioorganic Med. Chem. 4 (1996) 1321] or by Sicsic et al. [J. Med. Chem. 40 (1997) 739].

The localization of molecular electrostatic potential and of the H and L onto the indole may explain a dominant role of this ring in the free radical scavenging mechanism.     

Variable selection and specification of robust QSAR models from multicollinear data: arylpiperazinyl derivatives with affinity and selectivity for α<Subscript>2</Subscript>-adrenoceptors

Two QSAR models have been identified that predict the affinity and selectivity of arylpiperazinyl derivatives for alpha1 and alpha2 adrenoceptors (ARs). The models have been specified and validated using 108 compounds whose structures and inhibition constants (Ki) are available in the literature [Barbaro et al., J. Med. Chem., 44 (2001) 2118; Betti et al., J. Med. Chem., 45 (2002) 3603; Barbaro et al., Bioorg. Med. Chem., 10 (2002) 361; Betti et al., J. Med. Chem., 46 (2003) 3555]. One hundred and forty-seven predictors have been calculated using the Cerius 2 software available from Accelrys. This set of variables exhibited redundancy and severe multicollinearity, which had to be identified and removed as appropriate in order to obtain robust regression models free of inflated errors for the beta estimates - so-called bouncing betas. Those predictors that contained information relevant to the alpha2 response were identified on the basis of their pairwise linear correlations with affinity (-log Ki) for alpha2 adrenoceptors; the remaining variables were discarded. Subsequent variable selection made use of Factor Analysis (FA) and Unsupervised Variable Selection (UzFS). The data was divided into test and training sets using cluster analysis. These two sets were characterised by similar and consistent distributions of compounds in a high dimensional, but relevant predictor space. Multiple regression was then used to determine a subset of predictors from which to determine QSAR models for affinity to alpha2-ARs. Two multivariate procedures, Continuum Regression (the Portsmouth formulation) and Canonical Correlation Analysis (CCA), have been used to specify models for affinity and selectivity, respectively. Reasonable predictions were obtained using these in silico screening tools.     

Construction of 4D-QSAR Models for Use in the Design of Novel p38-MAPK Inhibitors

The p38-mitogen-activated protein kinase (p38-MAPK) plays a key role in lipopolysaccharide-induced tumor necrosis factor-alpha (TNF-alpha) and interleukin-1 (IL-1) release during the inflammatory process, emerging as an attractive target for new anti-inflammatory agents. Four-dimensional quantitative structure-activity relationship (4D-QSAR) analysis [Hopfinger et al., J. Am. Chem. Soc., 119 (1997) 10509] was applied to a series of 33 (a training set of 28 and a test set of 5) pyridinyl-imidazole and pyrimidinyl-imidazole inhibitors of p38-MAPK, with IC50 ranging from 0.11 to 2100 nM [Liverton et al., J. Med. Chem., 42 (1999) 2180]. Five thousand conformations of each analogue were sampled from a molecular dynamics simulation (MDS) during 50 ps at a constant temperature of 303 K. Each conformation was placed in a 2 angstroms grid cell lattice for each of three trial alignments. 4D-QSAR models were constructed by genetic algorithm (GA) optimization and partial least squares (PLS) fitting, and evaluated by leave-one-out cross-validation technique. In the best models, with three to six terms, the adjusted cross-validated squared correlation coefficients, Q2adj, ranged from 0.67 to 0.85. Model D (Q2adj = 0.84) was identified as the most robust model from alignment 1, and it is representative of the other best models. This model encompasses new molecular regions as containing pharmacophore sites, such as the amino-benzyl moiety of pyrimidine analogs and the N1-substituent in the imidazole ring. These regions of the ligands should be further explored to identify better anti-inflammatory inhibitors of p38-MAPK.     

Polycation comb-type copolymer reduces counterion condensation effect to stabilize DNA duplex and triplex formation

We have previously demonstrated that the polycation comb-type copolymer having abundant grafts of hydrophilic polymer chains significantly stabilizes DNA duplexes and triplexes [Maruyama et al., Bioconjugate Chem., 8 (1997) 3, Ferdous et al., Nucleic Acids Res., 26 (1998) 39]. This study was designed to estimate the mechanisms involved in the copolymer-mediated stabilization of DNA duplexes and triplexes. The melting temperatures, T_m, of DNA duplex and triplex increased with increasing salt concentration, as well documented by the Poisson–Boltzmann and counterion condensation theories that were originally proposed by Manning [J. Chem. Phys., 51 (1969) 924] and further elaborated by Manning [Biopolymers 11 (1972) 937, Biopolymers. 15 (1976) 2385] and Record [Biopolymers, 14 (1975) 2137–2158, Biopolymers, 15 (1976) 893]. In the presence of the copolymer, however, the T_m values of DNA duplexes and triplexes did not show significant change with salt concentration. It was concluded that the copolymer is capable of reducing the counterion condensation effects to stabilize DNA duplexes and triplexes. Strong but exchangeable interaction between the copolymer and DNA is seemingly involved in the stabilization behavior.     

A pseudoreceptor modelling study of the varicella-zoster virus and human thymidine kinase binding sites

Summary A representative range of pyrimidine nucleoside analogues that are known to inhibit herpes simplex virus (HSV) replication have been used to construct receptor binding site models for the varicella-zoster virus (VZV), thymidine kinase (TK) and human TK1. Given a set of interacting ligands, superimposed in such a manner as to define a pharmacophore, the pseudoreceptor modelling technique Yak provides a means of building binding site models of macromolecules for which no three-dimensional experimental structures are available. Once the models have been evaluated by their ability to reproduce experimental binding data [Vedani et al., J. Am. Chem. Soc., 117 (1995) 4987], they can be used for predictive purposes. Calculated and experimental values of relative binding affinity are compared. Our models suggest that the substitution of one residue may be sufficient to determine ligand subtype affinity.     

On the efficiency of VBSCF algorithms,a comment on “An efficient algorithm for energy gradients and orbital optimization in valence bond theory”

We comment on the paper [Song et al., J. Comput. Chem. 2009, 30, 399]. and discuss the efficiency of the orbital optimization and gradient evaluation in the Valence Bond Self Consistent Field (VBSCF) method. We note that Song et al. neglect to properly reference Broer et al., who published an algorithm [Broer and Nieuwpoort, Theor. Chim. Acta 1988, 73, 405] to use a Fock matrix to compute a matrix element between two different determinants, which can be used for an orbital optimization. Further, Song et al. publish a misleading comparison with our VBSCF algorithm [Dijkstra and van Lenthe, J. Chem. Phys. 2000, 113, 2100; van Lenthe et al., Mol. Phys. 1991, 73, 1159] to enable them to favorably compare their algorithm with ours. We give detail timings in terms of different orbital types in the calculation and actual timings for the example cases. © 2012 Wiley Periodicals, Inc.     

Constrained search of conformational hyperspace

Summary We introduce a new method for determining pharmacophore or active site geometries by analysis of the structures of a series of active compounds. The method, constrained search, and the key concepts on which it is based, is described and illustrated by its application to 28 potent inhibitors of angiotensin-converting enzyme (ACE). The data set is one utilized by Mayer et al. [J. Comput.-Aided Mol. Design, 1 (1987) 3–16] to determine a unique geometry for the active site. Our experiment validated the previously reported results, obtained by a systematic search, while reducing the computer time requirement by more than two orders of magnitude. The experiment also identified a previously unrecognized alternative active site geometry for the ACE series.     

Effect of the overall rotation on the cis-trans isomerization of HONO induced by an external field

Rovibrational eigenenergies of HONO are computed and compared to experimental energies available in the literature. For their computation, we use a previously developed potential energy surface (PES) and a newly derived exact kinetic energy operator (KEO) including the overall rotation for a tetra-atomic molecule in non-orthogonal coordinates. In addition, we use the Heidelberg Multi-Configuration Time-Dependent Hartree (MCTDH) package. We compare the experimental rovibrational eigenvalues of HONO available in the literature with those obtained with MCTDH and a previously developed potential energy surface (PES) [F. Richter et al., J. Chem. Phys., 2004, 120, 1306.] for the cis geometry. The effect of the overall rotation on the process studied in our previous work on HONO [F. Richter et al., J. Chem. Phys., 2007, 127, 164315.] leading to the cis→trans isomerization of HONO is investigated. This effect on this process is found to be weak.     

Modeling aqueous electrolyte solutions: Part 1. Fully dissociated electrolytes

Liquid densities (pvT), vapor pressures (VLE), and mean ionic activity coefficients (MIAC) at 25 °C of 115 single-salt electrolyte solutions containing univalent up to trivalent ions are modeled with the ePC-SAFT equation of state proposed by Cameretti et al. [L.F. Cameretti, G. Sadowski, J.M. Mollerup, Ind. Eng. Chem. Res. 44 (2005) 3355–3362; ibid., 8944]. For each ion, only two model parameters were adjusted to experimental density and MIAC data. Without using any additional binary parameters, ePC-SAFT is able to reproduce experimental data of the respective salt solutions up to high electrolyte molalities. Moreover, it is even able to describe the reversed MIAC series for alkali hydroxides and fluorides.     

Application of GC-PPC-SAFT EoS to amine mixtures with a predictive approach

The GC-PPC-SAFT equation of state (EoS) is a combination of a group contribution method [S. Tamouza et al., Fluid Phase Equilib. 222-223 (2004) 67-76; S. Tamouza et al., Fluid Phase Equilib. 228-229 (2005) 409-419] and the PC-SAFT EoS [J. Gross, G. Sadowski, Ind. Eng. Chem. Res. 40 (2001) 1244-1260] which was adapted to the polar molecules [D. Nguyen-Huynh et al., Fluid Phase Equilib. 264 (2008) 62-75]. It is here applied to the vapour pressure and liquid molar volume of primary, secondary and tertiary amines and their mixtures with n-alkanes, primary and secondary alcohols, using previously published group parameters. The mixing enthalpy is also evaluated for the binary systems. Binary interaction parameters k_ij are computed using a group-contribution pseudo-ionization energy, as proposed by Nguyen-Huynh [D. Nguyen-Huynh et al., Ind. Eng. Chem. Res. 47 (2008) 8847-8858]. A unique corrective parameter for the cross-association energy between amines and alcohols is used.The agreement with experimental data in correlation and prediction were found rather encouraging. The mean absolute average deviation (AAD) on bubble pressure is about 3.5% for pure amines. The mean AAD on the vapour-liquid equilibria (VLE) are respectively 2.2% and 5.5% for the amine mixtures with n-alkanes and alcohols. The AADs on saturated liquid volume are about 0.7% for the pure compounds and 0.9% for the mixtures. Prediction results are qualitatively and quantitatively accurate and they are comparable to those obtained with GC-PPC-SAFT on previously investigated systems.     

An approach to reaction path branching using valley–ridge inflection points of potential-energy surfaces

Valley–ridge inflection (VRI) points of a potential-energy surface (PES) may have a strong relation to the occurrence of bifurcations along reaction pathways of molecular rearrangements. We discuss two different definitions of VRI points in the literature. The calculation of symmetric VRI points has already been reported [W. Quapp et al. (1998) Theor. Chem. Acc. 100: 285–299]. Here, we in addition calculate special asymmetric VRI points which are placed on gradient extremals (GE). Following a GE opens the possibility to find the VRI point on it. An application is presented to search for asymmetric VRI points near the isomerization valley of the PES of the HCN molecule. A new method for GE-following is based on a mathematical connection between the following of a reduced gradient and the calculation of GEs. The tangent search method to follow a GE to the smallest eigenvalue [W. Quapp et al. (2000) Theor. Chem. Acc. 105: 145–155] is extended to follow also GEs to higher eigenvalues in order to find a VRI point. The new method needs gradient and second derivatives of the PES only.     

Three-dimensional quantitative structure-activity relationships of steroid aromatase inhibitors

Summary Inhibition of aromatase, a cytochrome P450 that converts androgens to estrogens, is relevant in the therapeutic control of breast cancer. We investigate this inhibition using a three-dimensional quantitative structure-activity relationship (3D QSAR) method known as Comparative Molecular Field Analysis, CoMFA [Cramer III, R.D. et al., J. Am. Chem. Soc., 110 (1988) 5959]. We analyzed the data for 50 steroid inhibitors [Numazawa, M. et al., J. Med. Chem., 37 (1994) 2198, and references cited therein] assayed against androstenedione on human placental microsomes. An initial CoMFA resulted in a three-component model for log(1/K_i), with an explained variance r² of 0.885, and a cross-validated q² of 0.673. Chemometric studies were performed using GOLPE [Baroni, M. et al., Quant. Struct.-Act. Relatsh., 12 (1993) 9]. The CoMFA/GOLPE model is discussed in terms of robustness, predictivity, explanatory power and simplicity. After randomized exclusion of 25 or 10 compounds (repeated 25 times), the q² for one component was 0.62 and 0.61, respectively, while r² was 0.674. We demonstrate that the predictive r² based on the mean activity (Y_m) of the training set is misleading, while the test set Y_m-based predictive r² index gives a more accurate estimate of external predictivity. Using CoMFA, the observed differences in aromatase inhibition among C6-substituted steroids are rationalized at the atomic level. The CoMFA fields are consistent with known, potent inhibitors of aromatase, not included in the model. When positioned in the same alignment, these compounds have distinct features that overlap with the steric and electrostatic fields obtained in the CoMFA model. The presence of two hydrophobic binding pockets near the aromatase active site is discussed: a steric bulk tolerant one, common for C4, C6-alpha and C7-alpha substitutents, and a smaller one at the C6-beta region.     

Local response dispersion method. II. Generalized multicenter interactions

Recently introduced local response dispersion method [T. Sato and H. Nakai, J. Chem. Phys. 131, 224104 (2009)], which is a first-principles alternative to empirical dispersion corrections in density functional theory, is implemented with generalized multicenter interactions involving both atomic and atomic pair polarizabilities. The generalization improves the asymptote of intermolecular interactions, reducing the mean absolute percentage error from about 30% to 6% in the molecular C(6) coefficients of more than 1000 dimers, compared to experimental values. The method is also applied to calculations of potential energy curves of molecules in the S22 database [P. Jure?ka et al., Phys. Chem. Chem. Phys. 8, 1985 (2006)]. The calculated potential energy curves are in a good agreement with reliable benchmarks recently published by Molnar et al. [J. Chem. Phys. 131, 065102 (2009)]. These improvements are achieved at the price of increasing complexity in the implementation, but without losing the computational efficiency of the previous two-center (atom-atom) formulation. A set of different truncations of two-center and three- or four-center interactions is shown to be optimal in the cost-performance balance.     

Molecular dynamics simulations of spontaneous bile salt aggregation

Bile salts are surfactants in bile that facilitate digestion, adsorption and excretion of various compounds. They have planar hydrophobic and hydrophilic faces and therefore exhibit some unusual properties; including the shape and size of the micelles that they form. Molecular dynamics simulations of the spontaneous aggregation of six bile salts (cholate (CHD), glycocholate (GCH), taurocholate (TCH), glycochenodeoxycholate (GCD), glycodeoxycholate (GDX) and glycolithocholate (GLC)) were performed in an aqueous phase to gain insight into their micellar structure. The aggregates that formed spontaneously from a random distribution of molecules ranged in size from 8 to 17 molecules. The structures are highly dynamic in nature and are on average oblate, but can vary from oblate, to spherical or prolate. Intermolecular hydrogen bonding within the micelles was found to be an important factor in determining the micelle size, structure and dynamics. The molecular arrangement within the micelles maximises the hydration of the hydrophilic chains and some favourable orientations for adjacent molecules were acquired. The dynamics of the micelles were investigated using the hydrogen-bond lifetime autocorrelation function correlation time, which exhibited a relationship with the degree of hydroxylation. Comparison of the proposed model to the three literature models showed some features of the disk shaped models of Cary and Small [M.C. Cary, D.M. Small, Arch. Intern. Med. 130 (1972) 506–527] and Kawamura et al. [H. Kawamura, Y. Murata, T. Yamaguchi, H. Igimi, M. Tanaka, G. Sugihara, J.P. Kratohvil, J. Phys. Chem. 93 (1989) 3321–3326], whereas the third, inverted helix model of Giglio et al. [E. Giglio, S. Loreti, N.V. Pavel, J. Phys. Chem. 92 (1988) 2858–2862] can be discounted. The proposed model is better than the existing models, which assumed a rigid and structured molecular arrangement.     

QSID Tool: a new three-dimensional QSAR environmental tool

QSID Tool (Quantitative structure–activity relationship tool for Innovative Discovery) was developed to provide an easy-to-use, robust and high quality environmental tool for 3D QSAR. Predictive models developed with QSID Tool can accelerate the discovery of lead compounds by enabling researchers to formulate and test hypotheses for optimizing efficacy and increasing drug safety and bioavailability early in the process of drug discovery. QSID Tool was evaluated by comparison with SYBYL^® using two different datasets derived from the inhibitors of Trypsin (Böhm et al., J Med Chem 42:458, 1999) and p38-MAPK (Liverton et al., J Med Chem 42:2180, 1999; Romeiro et al., J Comput Aided Mol Des 19:385, 2005; Romeiro et al., J Mol Model 12:855, 2006). The results suggest that QSID Tool is a useful model for the prediction of new analogue activities.     

Variable selection and specification of robust QSAR models from multicollinear data: arylpiperazinyl derivatives with affinity and selectivity for α2-adrenoceptors

Two QSAR models have been identified that predict the affinity and selectivity of arylpiperazinyl derivatives for ₁ and ₂ adrenoceptors (ARs). The models have been specified and validated using 108 compounds whose structures and inhibition constants (K _i) are available in the literature [Barbaro etal., J. Med. Chem., 44 (2001) 2118; Betti etal., J. Med. Chem., 45 (2002) 3603; Barbaro etal., Bioorg. Med. Chem., 10 (2002) 361; Betti etal., J. Med. Chem., 46 (2003) 3555]. One hundred and forty-seven predictors have been calculated using the Cerius 2 software available from Accelrys. This set of variables exhibited redundancy and severe multicollinearity, which had to be identified and removed as appropriate in order to obtain robust regression models free of inflated errors for the estimates – so-called bouncing s. Those predictors that contained information relevant to the ₂ response were identified on the basis of their pairwise linear correlations with affinity (–log K _i) for ₂ adrenoceptors; the remaining variables were discarded. Subsequent variable selection made use of Factor Analysis (FA) and Unsupervised Variable Selection (UzFS). The data was divided into test and training sets using cluster analysis. These two sets were characterised by similar and consistent distributions of compounds in a high dimensional, but relevant predictor space. Multiple regression was then used to determine a subset of predictors from which to determine QSAR models for affinity to ₂-ARs. Two multivariate procedures, Continuum Regression (the Portsmouth formulation) and Canonical Correlation Analysis (CCA), have been used to specify models for affinity and selectivity, respectively. Reasonable predictions were obtained using these in silico screening tools.     

Molecular dynamics simulations of surface-initiated melting of nitromethane

The melting of nitromethane initiated at solid-vacuum interfaces has been investigated using molecular dynamics nvt simulations with a realistic force field [D. C. Sorescu et al., J. Phys. Chem. B 104, 8406 (2000)]. The calculated melting point (251+/-5 K) is in good agreement with experiment (244.73 K) and values obtained previously (approximately 255.5 and 266.5+/-8 K) using other simulation methods [P. M. Agrawal et al., J. Chem. Phys. 119, 9617 (2003)]. Analyses of the molecular orientations and diffusion during the simulations as functions of the distance from the exposed surfaces show that the melting is a direct crystal-to-liquid transition, in which the molecules first gain rotational freedom, then mobility. There is a slight dependence of the melting temperature on the exposed crystallographic face.     

Crystallization of alkane melts induced by carbon nanotubes and graphene nanosheets: a molecular dynamics simulation study

The crystallization of alkane melts on carbon nanotubes (CNT) and the surface of graphene nanosheets (GNS) is investigated using molecular dynamics (MD) simulations. The crystallization process of the alkane melts is analyzed in terms of the bond-orientational order parameter, atomic radial distribution for the CNT/alkane, atomic longitudinal distribution for the GNS/alkane, and diffusion properties. The dimensional effects of the different carbon-based nanostructures on the crystallization of alkane melts are shown. It is found that one-dimensional CNT has a stronger ability to induce the crystallization of the polymer than that of two-dimensional GNS, which provides a support at molecular level for the experimental observation [Li et al., J. Am. Chem. Soc., 2006, 128, 1692 and Xu et al., Macromolecules, 2010, 43, 5000]. From the MD simulations, we also find that the crystallization of alkane molecules has been completed with the highly cooperative processes of adsorption and orientation.     

Systematic search in conformational analysis

The coupling of conformation to activity and reactivity is a widely accepted concept, and as such has driven the development of tools which execute conformational searches in rapid and robust fashion [T.F. Havel, Prog. Biophys. Molec. Biol., 56 (1991) 43–78; A.R. Leach, In Rev. Comput. Chem.; K.B. Lipkowitz and D.B. Boyd, Ed.; VCH Publishers, Inc.: New York, N.Y., 1991, Vol. II, pp. 1–55]. Among the aims of these methods are the determination of a complete set of local minima from which the global energy minimum can be identified, or the generation of conformations consistent with constraints derived from SAR or structural studies. Most methods fall into two broad categories: those which are random or stochastic, and those which are systematic. Yet another group consists of those which are based on heuristics and artificial intelligence [A.R. Leach, K. Prout, D.P. Dolata, J. Comput. Chem. 11 (1990) 680–693]. The first category is typified by molecular dynamics [W.F. van Gunsteren and H.J.C. Berendsen, Angew. Chem. Int. Ed. Eng., 29 (1990) 992–1023], Monte Carlo [M.P. Alien and D.J. Tildesley, Computer Simulation of Liquids, Oxford Science Publications, 1989], distance geometry [J.M. Blaney and J.S. Dixon, in K.B. Lipkowitz and D.B. Boyd (Eds.), Reviews in Computational Chemistry, VCH, New York, Vol. 5, pp. 299–335, 1994], and other approaches [M. Saunders, J. Comput. Chem., 10 (1989) 203–208] in which the path by which conformational space is examined is ideally completely random, but bounded by the geometries of covalent bond lengths and angles. In traditional systematic searches, the variable to be examined, e.g. torsion angles, is divided into a regular grid. Each and every grid point is evaluated in a systematic fashion to determine its validity. The path through the grid points is regular and defined. In principle, systematic search can, within the resolution of the grid, identify all sterically allowed conformations of a molecule. Consequently, systematic search is an ideal tool for conformational analysis because it is not path dependent and cannot become entrapped in local minima. In this article we review some of the basics of systematic search, algorithmic improvements that have enhanced its speed, and new developments that have increased its accuracy by moving away from the limitations of a fixed torsional grid.