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Noble gases are chemically inert, and it was therefore thought they would have little effect on biology. Paradoxically, it was found that they do exhibit a wide range of biological effects, many of which are target-specific and potentially useful and some of which have been demonstrated in vivo. The underlying mechanisms by which useful pharmacology, such as tissue and neuroprotection, anti-addiction effects, and analgesia, is elicited are relatively unexplored. Experiments to probe the interactions of noble gases with specific proteins are more difficult with gases than those with other chemicals. It is clearly impractical to conduct the large number of gas-protein experiments required to gain a complete picture of noble gas biology. Given the simplicity of atoms as ligands, in silico methods provide an opportunity to gain insight into which noble gas-protein interactions are worthy of further experimental or advanced computational investigation. Our previous validation studies showed that in silico methods can accurately predict experimentally determined noble gas binding sites in X-ray structures of proteins. Here, we summarize the largest reported in silico reverse docking study involving 127 854 protein structures and the five nonradioactive noble gases. We describe how these computational screening methods are implemented, summarize the main types of interactions that occur between noble gases and target proteins, describe how the massive data set that this study generated can be analyzed (freely available at group18.csiro.au), and provide the NDMA receptor as an example of how these data can be used to understand the molecular pharmacology underlying the biology of the noble gases. We encourage chemical biologists to access the data and use them to expand the knowledge base of noble gas pharmacology, and to use this information, together with more efficient delivery systems, to develop "atomic drugs" that can fully exploit their considerable and relatively unexplored potential in medicine.

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The commercial applications of nanoparticles are growing rapidly, but we know relatively little about how nanoparticles interact with biological systems. Their value--but also their risk--is related to their nanophase properties being markedly different to those of the same material in bulk. Experiments to determine how nanoparticles are taken up, distributed, modified, and elicit any adverse effects are essential. However, cost and time considerations mean that predictive models would also be extremely valuable, particularly assisting regulators to minimize health and environmental risks. We used novel sparse machine learning methods that employ Bayesian neural networks to model three nanoparticle data sets using both linear and nonlinear machine learning methods. The first data comprised iron oxide nanoparticles decorated with 108 different molecules tested against five cell lines, HUVEC, pancreatic cancer, and three macrophage or macrophage-like lines. The second data set comprised 52 nanoparticles with various core compositions, coatings, and surface attachments. The nanoparticles were characterized using four descriptors (size, relaxivities, and zeta potential), and their biological effects on four cells lines assessed using four biological assays per cell line and four concentrations per assay. The third data set involved the biological responses to gold nanoparticles functionalized by 80 different small molecules. Nonspecific binding and binding to AChE were the biological endpoints modelled. The biological effects of nanoparticles were modelled using molecular descriptors for the molecules that decorated the nanoparticle surface. Models with good statistical quality were constructed for most biological endpoints. These proof-of-concept models show that modelling biological effects of nanomaterials is possible using modern modelling methods.

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Pluripotency is a cellular state of multiple options. Here, we highlight the potential for self-organization to contribute to stem cell fate computation. A new way of considering regulatory circuitry is presented that describes the expression of each transcription factor (TF) as a branching process that propagates through time, interacting and competing with others. In a single cell, the interactions between multiple branching processes generate a collective process called 'critical-like self-organization'. We explain how this phenomenon provides a valid description of whole genome regulatory circuit dynamics. The hypothesis of exploratory stem cell decision-making proposes that critical-like self-organization (also called rapid self-organized criticality) provides the backbone for cell fate computation in regulative embryos and pluripotent stem cells. Unspecific amplification of TF expression is predicted to initiate this self-organizing circuitry, where cascades of gene expression propagate and may interact either synergistically or antagonistically. The emergent and highly dynamic circuitry is affected by various sources of selection pressure, such as the expression of TFs with disproportionate influence over other genes, and extrinsic biological and physical stimuli that differentially modulate particular gene expression cascades. Extrinsic conditions continuously trigger waves of transcription that ripple throughout regulatory networks on multiple spatiotemporal scales, providing the context within which circuitry self-organizes. In this framework, a distinction between instructive and selective mechanisms of fate determination is misleading because it is the 'interference pattern', rather than any single instructing or selecting factor, that is ultimately responsible for computing and directing cell fate. Using this framework, we consider whether the idea of a naïve ground state of pluripotency and that of a fluctuating transcriptome are compatible, and whether a ground state like that captured in vitro could exist in vivo.

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Electronic structural signatures of the guanine-7H and guanine-9H tautomers have been investigated on an orbital by orbital basis using dual space analysis. A combination of density functional theory (B3LYP/TZVP), the statistical average of model orbital potentials (SAOP/TZ2P) method and outer valence Green's function theory (OVGF/TZVP) has been used to generate optimal tautomer geometries and accurate ionization energy spectra for the guanine tautomer pair. The present work found that the non-planar form for both of the guanine keto pair possesses lower energies than their corresponding planar counterparts, and that the canonical form of the guanine-7H tautomer has slightly lower total energy than guanine-9H. This latter result is in agreement with previous experimental and theoretical findings. In the planar guanine pair the geometric parameters and anisotropic molecular properties are compared, focusing on changes caused by the mobile proton transfer. It is demonstrated that the mobile proton only causes limited disturbance to isotropic properties, such as geometry and the energetics, of the guanine keto tautomer pair. The exception to this general statement is for related local changes such as the N((7))-C((8)) and C((8))-N((9)) bond length resonance between the single and double bonds, reflecting the nitrogen atom being bonded with the mobile proton in the tautomers. The mobile proton distorts the electron distribution of the tautomers, which leads to significant changes in the molecular anisotropic properties. The dipole moment of guanine-7H is altered by about a factor of three, from 2.23 to 7.05 D (guanine-9H), and the molecular electrostatic potentials also reflect significant electron charge distortion. The outer valence orbital momentum distributions, which were obtained using the plane wave impulse approximation (PWIA), have demonstrated quantitatively that the outer valence orbitals of the tautomer pair can be divided into three groups. That is orbitals 1a''-7a'' and 18a', which do not have visible alternations in the tautomeric process (which consist of either pi orbitals or are close to the inner valence shell); a second group comprising orbitals 19a'-22a', 25a', 26a', 28a', 29a' and 31a', which show small perturbations as a result of the mobile hydrogen locations; and group three, orbitals 23a', 24a', 27a', 30a' and 32a', which demonstrate significant changes due to the mobile proton transfer and are therefore considered as signature orbitals of the G-7H/G-9H keto tautomeric process.

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Results of a study of the valence electronic structure of norbornene (C(7)H(10)), up to binding energies of 30 eV, are reported. Experimental electron momentum spectroscopy (EMS) and theoretical Green's function and density functional theory approaches were utilized in this investigation. A stringent comparison between the electron momentum spectroscopy and theoretical orbital momentum distributions found that, among the tested models, the combination of the Becke-Perdew functional and a polarized valence basis set of triple-zeta quality provides the best representation of the electron momentum distributions for all 19 valence orbitals of norbornene. This experimentally validated model was then used to extract other molecular properties of norbornene (geometry, infrared spectrum). When these calculated properties are compared to corresponding results from independent measurements, reasonable agreement is typically found. Due to the improved energy resolution, EMS is now at a stage to very finely image the effective topology of molecular orbitals at varying distances from the molecular center, and the way the individual atomic components interact with each other, often in excellent agreement with theory. This will be demonstrated here. Green's Function calculations employing the third-order algebraic diagrammatic construction scheme indicate that the orbital picture of ionization breaks down at binding energies larger than about 22 eV. Despite this complication, they enable insights within 0.2 eV accuracy into the available ultraviolet emission and newly presented (e,2e) ionization spectra. Finally, limitations inherent to calculations of momentum distributions based on Kohn-Sham orbitals and employing the vertical depiction of ionization processes are emphasized, in a formal discussion of EMS cross sections employing Dyson orbitals.

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We report on the results of an exhaustive study of the valence electronic structure of norbornane (C(7)H(12)), up to binding energies of 29 eV. Experimental electron momentum spectroscopy and theoretical Green's function and density functional theory approaches were all utilized in this investigation. A stringent comparison between the electron momentum spectroscopy and theoretical orbital momentum distributions found that, among all the tested models, the combination of the Becke-Perdew functional and a polarized valence basis set of triple-zeta quality provides the best representation of the electron momentum distributions for all of the 20 valence orbitals of norbornane. This experimentally validated quantum chemistry model was then used to extract some chemically important properties of norbornane. When these calculated properties are compared to corresponding results from other independent measurements, generally good agreement is found. Green's function calculations with the aid of the third-order algebraic diagrammatic construction scheme indicate that the orbital picture of ionization breaks down at binding energies larger than 22.5 eV. Despite this complication, they enable insights within 0.2 eV accuracy into the available ultraviolet photoemission and newly presented (e,2e) ionization spectra, except for the band associated with the 1a(2) (-1) one-hole state, which is probably subject to rather significant vibronic coupling effects, and a band at approximately 25 eV characterized by a momentum distribution of "s-type" symmetry, which Green's function calculations fail to reproduce. We note the vicinity of the vertical double ionization threshold at approximately 26 eV.

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Rice Blast Disease, caused by the fungus Pyricularia oryzae, is one of the most important diseases of rice. Several enzymes in the melanin biosynthetic pathway have proven to be valuable targets for development of rice blast fungicides. In particular, inhibitors of trihydroxynaphthalene reductase (3HNR), which catalyzes the conversion of trihydroxynaphthalene to vermelone, have yielded commercially useful rice fungicides. The X-ray structure of 3HNR has been published recently, presenting an opportunity to use this information in the de novo design of novel 3HNR inhibitors that may exhibit useful rice blast activity. We used the LeapFrog program to develop a docking model for interaction of ligands with the active site of THNR. The final model gave a good correlation between calculated binding energy and log Ki and was used to design novel ligands and score compounds for synthesis. Using this as a tool, we synthesized inhibitors in the nanomolar range and also developed several inhibitors that did not conform to the properties of the THNR active site. Leapfrog was able to locate a previously unrecognized binding pocket that could accommodate these otherwise anomalous regions of structure.

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Core molecular orbital contribution to the electronic structure of N2O isomers has been studied using quantum mechanical density functional theory combined with a plane wave impulse approximation method. Momentum distributions of wave functions for inner shell molecular orbitals of the linear NNO, cyclic and linear NON isomers of N2O are calculated through the (e, 2e) differential cross sections in momentum space. This is possible because this momentum distribution is directly proportional to the modulus squared of the momentum space wave function for the molecular orbital in question. While the momentum distributions of the NNO and cyclic N2O isomers demonstrate strong atomic orbital characteristics in their core space, the outer core molecular orbitals of the linear NON isomer exhibit configuration interactions between them and the valence molecular orbitals. It is suggested that the frozen core approximation breaks down in the prediction of the electronic structure of such an isomer. Core molecular orbital contributions to the electronic structure can alter the order of total energies of the isomers and lead to incorrect conclusions of the stability among the isomers. As a result, full electron calculations should be employed in the study of N2O isomerization.

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We have used a new, robust structure-activity mapping technique, a Bayesian-regularized neural network, to develop a quantitative structure-activity relationships (QSAR) model for the toxicity of 278 substituted benzenes toward Tetrahymena pyriformis. The independent variables used in the modeling were derived solely from the molecular structure, and the model was tested on 20% of the data set selected from the whole set by cluster analysis and which had not been used in training the network. The results show that the method is robust and reliable and give results for mixed class compounds which are comparable to earlier QSAR work on single-chemical class subsets of the 278 compounds and which employed measured physicochemical parameters as independent variables. Comparisons of Bayesian neural net models with those derived by classical PLS analysis showed the superiority of our method. The method appears to be able to model more diverse chemical classes and more than one mechanism of toxicity.

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We describe the use of Bayesian regularized artificial neural networks (BRANNs) coupled with automatic relevance determination (ARD) in the development of quantitative structure-activity relationship (QSAR) models. These BRANN-ARD networks have the potential to solve a number of problems which arise in QSAR modeling such as the following: choice of model; robustness of model; choice of validation set; size of validation effort; and optimization of network architecture. The ARD method ensures that irrelevant or highly correlated indices used in the modeling are neglected as well as showing which are the most important variables in modeling the activity data. The application of the methods to QSAR of compounds active at the benzodiazepine and muscarinic receptors as well as some toxicological data of the effect of substituted benzenes on Tetetrahymena pyriformis is illustrated.

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We describe the use of Bayesian regularized artificial neural networks (BRANNs) in the development of QSAR models. These networks have the potential to solve a number of problems which arise in QSAR modeling such as: choice of model; robustness of model; choice of validation set; size of validation effort; and optimization of network architecture. The application of the methods to QSAR of compounds active at the benzodiazepine and muscarinic receptors is illustrated.

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The importance of inhibitors of glycosidases as therapeutic agents for viral, proliferative, and metabolic diseases is being increasingly recognised. Several years ago we reported that the activities of mannosidase inhibitors may be explained in terms of their similarity to the mannosyl cation intermediate postulated to form during the enzyme-catalyzed processing of oligosaccharide substrates. Recently, the validity of this model has been called in to question by some authors. We report recent molecular modeling studies undertaken to clarify this apparent contradiction. Mannostatin can indeed bind in a fashion which bears a close similarity to the mannosyl cation. Moreover we have shown that (-)-mannostatin is not able to adopt a similar binding mode to that of the mannosyl cation. As additional proof, Farr et al. have synthesized a trihydroxycyclopentylamine as a direct mimic of our mannosyl cation model. Satisfyingly, this compound shows potent inhibition of Jack Bean alpha-mannosidase, as predicted by the model. The inactivity of aminotrihydroxyhexahydro-1H-azepine against mannosidases can be explained in terms of the relative energies of the axial versus equatorial conformations of the critical hexahydroazepine ring substituents.

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A molecular orbital and molecular graphics study of 12 substrates, inhibitors, reaction intermediates, and substrate analogues of alpha-mannosidase was undertaken. The results indicated that potent inhibitors must be good topographical analogues of the mannopyranosyl cation, an intermediate in the reaction catalyzed by the enzyme. Enzyme recognition and strong binding by the inhibitors requires that they contain, as part of their structure, electronegative atoms which are the topographical equivalent of the mannosyl cation C2 and C3 hydroxyl groups and ring heteroatom. The absence of a topographical analogue of the C4 hydroxyl group of the cation appeared to have little effect on the binding and activity of inhibitors. These results have been utilized in the design of potential anti-HIV drugs whose synthesis is now under consideration.

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Our molecular modeling software package, MORPHEUS, allows the study of the interactions between biologically active molecules and their receptors. The package is capable of exploring the multidimensional conformational space accessible to each molecule of the data set under study. By specifying distance constraints or hypothetical receptor binding points, the package is able to filter the biologically accessible conformations of each active compound and deduce a three-dimensional model of the binding sites consistent with the properties of the agonists (or antagonists) under scrutiny. The electrostatic potentials in the environment of a putative binding site can also be investigated using the MORPHEUS package. The molecular modeling module CRYS-X, which is written in FORTRAN 77 for IBM PC machines, is capable of building, displaying and manipulating molecules.

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Major groups of modern mammals have their origins in the Mesozoic Era, yet the mammalian fossil record is generally poor for that time interval. Fundamental morphological changes that led to modern mammals are often represented by small samples of isolated teeth. Fortunately, functional wear facets on teeth allow prediction of the morphology of occluding teeth that may be unrepresented by fossils. A major step in mammalian evolution occurred in the Early Cretaceous with the evolution of tribosphenic molars, which characterize marsupials and placentals, the two most abundant and diverse extant groups of mammals. A tooth from the Early Cretaceous (110 million years before present) of Texas tests previous predictions (based on lower molars) of the morphology of upper molars in early tribosphenic dentitions. The lingual cusp (protocone) is primitively without shear facets, as expected, but the cheek side of the tooth is derived (advanced) in having distinctive cusps along the margin. The tooth, although distressingly inadequate to define many features of the organism, demonstrates unexpected morphological diversity at a strategic stage of mammalian evolution and falsifies previous claims of the earliest occurrence of true marsupials.

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Molecular orbital calculations and 13C and 15N NMR experiments have been performed on the O-methyl oximes of two types of 2-acyl cyclic 1,3-diones. One (III; X = CH2) was based on cyclohexane, and the other (X = O) on pyran. The data indicate that both the cyclohexane and pyran compounds prefer to exist as the oxime, rather than the enamine isomer. Two equivalent, interconverting, keto-enol tautomers exist in the cyclohexane compound. The pyran compound exists as the lactone-enol tautomer. Our results have implications in the design of herbicidal compounds and drugs containing similar tautomeric systems.

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Semiempirical molecular orbital calculations have been performed on a series of hydrazone uncouplers of mitochondrial oxidative phosphorylation which show insecticidal activity. Regression analysis yielded significant correlations between uncoupling activity, insecticidal potency and such physicochemical or theoretically-derived parameters as lipophilicity, pKa and atom charges.

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Classical potential energy calculations are reported for a series of 11 structurally diverse substrates, products, and inhibitors of dihydrofolate reductase. In almost every case, the calculations reveal a range of potential biologically active conformations accessible to the molecule, and geometry optimization with molecular mechanics and molecular orbital calculations further expands the range of accessible conformations. The energy calculations are supplemented with electrostatic potential energy surfaces for the heterocyclic components of each molecule. These data are used in conjunction with the energy calculations and the crystallographically determined enzyme structures to compare two alternative proposed binding modes of folates known to bind with their pteridine rings inverted relative to that of methotrexate. It is shown that the conformational flexibility of the connecting chain between the benzoyl glutamate and pteridine moieties in the folates actually allows the pteridine ring to shift between these alternative binding modes, a combination of which may offer the best explanation for the observed activity. The electrostatic potentials and conformational energy data are also used in an attempt to account for the species specificity of inhibitors of mammalian, bacterial, and protozoal dihydrofolate reductases. The results show that while these techniques can be used to explain many of the observed results, others require recourse to the observed crystal structures to provide a satisfactory explanation.

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A computer-graphic-based pattern-recognition study of two series of 5-ethyl-5-substituted barbiturates has been undertaken in an attempt to find a correlation between molecular conformation and convulsant and anticonvulsant activity. Studies of a first (trial) set of barbiturates related to pentobarbital revealed a region of space in which at least one low-energy conformation of the hydrocarbon side chain of each of the anticonvulsant barbiturates resides. Another region was occupied by a low-energy conformation of each of the convulsant barbiturates. These regions of space are, thus, possible pharmacophores for convulsant and anticonvulsant activity. Analysis of a second (test) set of barbiturates related to phenobarbital has shown that the activities and structures of these molecules are consistent with the above model. These pharmacophores thus provide a basis for the design of rigid, new analogues with potent convulsant or anticonvulsant activities.

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