Conformation and Dynamics of Human Urotensin II and Urotensin Related Peptide in Aqueous Solution.

Conformation and dynamics of the vasoconstrictive peptides human urotensin II (UII) and urotensin related peptide (URP) have been investigated by both unrestrained and enhanced-sampling molecular-dynamics (MD) simulations and NMR spectroscopy. These peptides are natural ligands of the G-protein coupled urotensin II receptor (UTR) and have been linked to mammalian pathophysiology. UII and URP cannot be characterized by a single structure but exist as an equilibrium of two main classes of ring conformations, open and folded, with rapidly interchanging subtypes. The open states are characterized by turns of various types centered at K8Y9 or F6W7 predominantly with no or only sparsely populated transannular hydrogen bonds. The folded conformations show multiple turns stabilized by highly populated transannular hydrogen bonds comprising centers F6W7K8 or W7K8Y9. Some of these conformations have not been characterized previously. The equilibrium populations that are experimentally difficult to access were estimated by replica-exchange MD simulations and validated by comparison of experimental NMR data with chemical shifts calculated with density-functional theory. UII exhibits approximately 72% open:28% folded conformations in aqueous solution. URP shows very similar ring conformations as UII but differs in an open:folded equilibrium shifted further toward open conformations (86:14) possibly arising from the absence of folded N-terminal tail-ring interaction. The results suggest that the different biological effects of UII and URP are not caused by differences in ring conformations but rather by different interactions with UTR.

Can Simulations and Modeling Decipher NMR Data for Conformational Equilibria? Arginine-Vasopressin.

Arginine vasopressin (AVP) has been suggested by molecular-dynamics (MD) simulations to exist as a mixture of conformations in solution. The (1)H and (13)C NMR chemical shifts of AVP in solution have been calculated for this conformational ensemble of ring conformations (identified from a 23 µs molecular-dynamics simulation). The relative free energies of these conformations were calculated using classical metadynamics simulations in explicit water. Chemical shifts for representative conformations were calculated using density-functional theory. Comparison with experiment and analysis of the results suggests that the (1)H chemical shifts are most useful for assigning equilibrium concentrations of the conformations in this case. (13)C chemical shifts distinguish less clearly between conformations, and the distances calculated from the nuclear Overhauser effect do not allow the conformations to be assigned clearly. The (1)H chemical shifts can be reproduced with a standard error of less than 0.24 ppm (<2.2 ppm for (13)C). The combined experimental and theoretical results suggest that AVP exists in an equilibrium of approximately 70% saddlelike and 30% clinched open conformations. Both newly introduced statistical metrics designed to judge the significance of the results and Smith and Goodman's DP4 probabilities are presented.

A Three-Site Mechanism for Agonist/Antagonist Selective Binding to Vasopressin Receptors.

Molecular-dynamics simulations with metadynamics enhanced sampling reveal three distinct binding sites for arginine vasopressin (AVP) within its V2 -receptor (V2 R). Two of these, the vestibule and intermediate sites, block (antagonize) the receptor, and the third is the orthosteric activation (agonist) site. The contacts found for the orthosteric site satisfy all the requirements deduced from mutagenesis experiments. Metadynamics simulations for V2 R and its V1a R-analog give an excellent correlation with experimental binding free energies by assuming that the most stable binding site in the simulations corresponds to the experimental binding free energy in each case. The resulting three-site mechanism separates agonists from antagonists and explains subtype selectivity.

Conformation and dynamics of 8-Arg-vasopressin in solution.

Arginine-vasopressin was subjected to a long (11 µs) molecular dynamics simulation in aqueous solution. Analysis of the results by DASH and principal components analyses revealed four main ring conformations that move essentially independently of the faster-moving tail region. Two of these conformations (labeled "saddle") feature well-defined ß-turns in the ring and conserved transannular hydrogen bonds, whereas the other two ("open") feature neither. The conformations have been identified and defined and are all of sufficient stability to be considered candidates for biological conformations in their cognate receptors.

A solid-phase extraction method for rapidly determining the adsorption coefficient of pharmaceuticals in sewage sludge.

The partitioning of pharmaceuticals in the environment can be assessed by measuring their adsorption coefficients (Kd) between aqueous and solid phases. Measuring this coefficient in sewage sludge gives an indication of their partitioning behaviour in a wastewater treatment plant and hence contributes to an understanding of their subsequent fate. The regulatory approved method for measuring Kd in sewage sludge is the US Environmental Protection Agency's Office of Prevention, Pesticides and Toxic Substances (OPPTS) guideline 835.1110, which is labour intensive and time consuming. We describe an alternative method for measuring the Kd of pharmaceuticals in sewage sludge using a modified solid-phase extraction (SPE) technique. SPE cartridges were packed at different sludge/PTFE ratios (0.4, 6.0, 24.0 and 40.0% w/w sludge) and eluted with phosphate buffer at pH 7.4. The approach was tested initially using three pharmaceuticals (clofibric acid, diclofenac and oxytetracycline) that covered a range of Kd values. Subsequently, the sorption behaviour of ten further pharmaceuticals with varying physico-chemical properties was evaluated. Results from the SPE method were comparable to those of the OPPTS test, with a correlation coefficient of 0.93 between the two approaches. SPE cartridges packed with sludge and PTFE were stable for up to one year; use within one month reduced variability in measurements (to a maximum of 0.6 log units). The SPE method is low-cost, easy to use and enables the rapid measurement of Kd values for a large number of chemicals. It can be used as an alternative to the more laborious full OPPTS test in environmental fate studies and risk assessments.

APTE: identification of indirect read-out A-DNA promoter elements in genomes.

BACKGROUND: Transcriptional regulation is normally based on the recognition by a transcription factor of a defined base sequence in a process of direct read-out. However, the nucleic acid secondary and tertiary structure can also act as a recognition site for the transcription factor in a process known as indirect read-out, although this is much less understood. We have previously identified such a transcriptional control mechanism in early Xenopus development where the interaction of the transcription factor ilf3 and the gata2 promoter requires the presence of both an unusual A-form DNA structure and a CCAAT sequence. Rapid identification of such promoters elsewhere in the Xenopus and other genomes would provide insight into a less studied area of gene regulation, although currently there are few tools to analyse genomes in such ways. RESULTS: In this paper we report the implementation of a novel bioinformatics approach that has identified 86 such putative promoters in the Xenopus genome. We have shown that five of these sites are A-form in solution, bind to transcription factors and fully validated one of these newly identified promoters as interacting with the ilf3 containing complex CBTF. This interaction regulates the transcription of a previously uncharacterised downstream gene that is active in early development. CONCLUSIONS: A Perl program (APTE) has located a number of potential A-form DNA promotor elements in the Xenopus genome, five of these putative targets have been experimentally validated as A-form and as targets for specific DNA binding proteins; one has also been shown to interact with the A-form binding transcription factor ilf3. APTE is available from http://www.port.ac.uk/research/cmd/software/ under the terms of the GNU General Public License.

Sharpening the toolbox of computational chemistry: a new approximation of critical f-values for multiple linear regression.

Multiple linear regression is a major tool in computational chemistry. Although it has been used for more than 30 years, it has only recently been noted within the cheminformatics community that the standard F-values used to assess the significance of the resulting models are inappropriate in situations where the variables included in a model are chosen from a large pool of descriptors, due to an effect known in the statistical literature as selection bias. We have used Monte Carlo simulations to estimate the critical F-values for many combinations of sample size (n), model size (p), and descriptor pool size (k), using stepwise regression, one of the methods most commonly used to derive linear models from large sets of molecular descriptors. The values of n, p, and k represent cases appropriate to contemporary cheminformatics data sets. A formula for general n, p, and k values has been developed from the numerical estimates that approximates the critical stepwise F-values at 90%, 95%, and 99% significance levels. This approximation reproduces both the original simulated values and an interpolation test set (within the range of the training values) with an R2 value greater than 0.995. For an extrapolation test set of cases outside the range of the training set, the approximation produced an R2 above 0.93.

The extraction of information and knowledge from trained neural networks.

In the past, neural networks were viewed as classification and regression systems whose internal representations were incomprehensible. It is now becoming apparent that algorithms can be designed that extract comprehensible representations from trained neural networks, enabling them to be used for data mining and knowledge discovery, that is, the discovery and explanation of previously unknown relationships present in data. This chapter reviews existing algorithms for extracting comprehensible representations from neural networks and outlines research to generalize and extend the capabilities of one of these algorithms, TREPAN. This algorithm has been generalized for application to bioinformatics data sets, including the prediction of splice junctions in human DNA sequences, and cheminformatics. The results generated on these data sets are compared with those generated by a conventional data mining technique (C5) and appropriate conclusions are drawn.

Pattern recognition based on color-coded quantum mechanical surfaces for molecular alignment.

A pattern recognition algorithm for the alignment of drug-like molecules has been implemented. The method is based on the calculation of quantum mechanical derived local properties defined on a molecular surface. This approach has been shown to be very useful in attempting to derive generalized, non-atom based representations of molecular structure. The visualization of these surfaces is described together with details of the methodology developed for their use in molecular overlay and similarity calculations. In addition, this paper also introduces an additional local property, the local curvature (C (L)), which can be used together with the quantum mechanical properties to describe the local shape. The method is exemplified using some problems representing common tasks encountered in molecular similarity.

A consensus neural network-based technique for discriminating soluble and poorly soluble compounds.

BCUT [Burden, CAS, and University of Texas] descriptors, defined as eigenvalues of modified connectivity matrices, have traditionally been applied to drug design tasks such as defining receptor relevant subspaces to assist in compound selections. In this paper we present studies of consensus neural networks trained on BCUTs to discriminate compounds with poor aqueous solubility from those with reasonable solubility. This level was set at 0.1 mg/mL on advice from drug formulation and drug discovery scientists. By applying strict criteria to the insolubility predictions, approximately 95% of compounds are classified correctly. For compounds whose predictions have a lower level of confidence, further parameters are examined in order to flag those considered to possess unsuitable biopharmaceutical and physicochemical properties. This approach is not designed to be applied in isolation but is intended to be used as a filter in the selection of screening candidates, compound purchases, and the application of synthetic priorities to combinatorial libraries.

Selecting screening candidates for kinase and G protein-coupled receptor targets using neural networks.

A series of neural networks has been trained, using consensus methods, to recognize compounds that act at biological targets belonging to specific gene families. The MDDR database was used to provide compounds targeted against gene families and sets of randomly selected molecules. BCUT parameters were employed as input descriptors that encode structural properties and information relevant to ligand-receptor interactions. In each case, the networks identified over 80% of the compounds targeting a gene family. The technique was applied to purchasing compounds from external suppliers, and results from screening against one gene family demonstrated impressive abilities to predict the activity of the majority of known hit compounds.