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International audience; 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

Understanding the sorption of pharmaceuticals to sewage sludge during waste water treatment processes is important for understanding their environmental fate and in risk assessments. The degree of sorption is defined by the sludge/water partition coefficient (Kd). Experimental Kd values (n = 297) for active pharmaceutical ingredients (n = 148) in primary and activated sludge were collected from literature. The compounds were classified by their charge at pH 7.4 (44 uncharged, 60 positively and 28 negatively charged, and 16 zwitterions). Univariate models relating log Kd to log Kow for each charge class showed weak correlations (maximum R2 = 0.51 for positively charged) with no overall correlation for the combined dataset (R2 = 0.04). Weaker correlations were found when relating log Kd to log Dow. Three sets of molecular descriptors (Molecular Operating Environment, VolSurf and ParaSurf) encoding a range of physico-chemical properties were used to derive multivariate models using stepwise regression, partial least squares and Bayesian artificial neural networks (ANN). The best predictive performance was obtained with ANN, with R2 = 0.62–0.69 for these descriptors using the complete dataset. Use of more complex Vsurf and ParaSurf descriptors showed little improvement over Molecular Operating Environment descriptors. The most influential descriptors in the ANN models, identified by automatic relevance determination, highlighted the importance of hydrophobicity, charge and molecular shape effects in these sorbate-sorbent interactions. The heterogeneous nature of the different sewage sludges used to measure Kd limited the predictability of sorption from physico-chemical properties of the pharmaceuticals alone. Standardization of test materials for the measurement of Kd would improve comparability of data from different studies, in the long-term leading to better quality environmental risk assessments., Graphical abstract Image 1, Highlights • Understanding sorption of pharmaceuticals to sludge is important in risk assessment. • Predicting sorption on molecular properties limited by heterogeneous nature of sludge • Models based only on the partition coefficient gave poor predictive models. • Non-linear artificial neural network models improve predictability. • Descriptors that influenced sorbate-sorbent interactions were identified.

Arginine vasopressin (AVP) has been suggested by molecular-dynamics (MD) simulations to exist as a mixture of conformations in solution. The 1H and 13C 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 1H chemical shifts are most useful for assigning equilibrium concentrations of the conformations in this case. 13C 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 1H chemical shifts can be reproduced with a standard error of less than 0.24 ppm (13C). 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.

Assessment of the fate of pharmaceutical residues in the environment involves the measurement or prediction of their sewage sludge partition coefficient (Kd). Sewage sludge can be classified into four types: primary, activated, secondary and digested, each one with different physical and chemical properties. Published studies have measured Kd for pharmaceuticals in a variety of sludge types. This paper discusses the variability of reported Kd values of pharmaceuticals in different types of sewage sludge, using a dataset generated from the literature. Using a meta-analysis approach, it was shown that the measured Kd values depend on the type of sludge used in the test. Recommendations are given for the type of sludge to be used when studying the partitioning behaviour of pharmaceuticals in waste water treatment plants. Activated sludge is preferred due to its more homogenous nature and the ease of collection of consistent samples at a plant. Weak statistical relationships were found between Kd values for activated and secondary sludge, and for activated and digested sludge. Pooling of Kd values for these sludge types is not recommended for preliminary fate and risk assessments. In contrast, statistical analyses found stronger similarities between Kd values reported for the same pharmaceutical in primary and activated sludges. This allows the pooling of experimental values for these two sludge types to obtain a larger dataset for modelling purposes.

Author(s): Fechner, Uli; de Graaf, Chris; Torda, Andrew E; Gussregen, Stefan; Evers, Andreas; Matter, Hans; Hessler, Gerhard; Richmond, Nicola J; Schmidtke, Peter; Segler, Marwin HS; Waller, Mark P; Pleik, Stefanie; Shea, Joan-Emma; Levine, Zachary; Mullen, Ryan; van den Broek, Karina; Epple, Matthias; Kuhn, Hubert; Truszkowski, Andreas; Zielesny, Achim; Fraaije, Johannes Hans; Gracia, Ruben Serral; Kast, Stefan M; Bulusu, Krishna C; Bender, Andreas; Yosipof, Abraham; Nahum, Oren; Senderowitz, Hanoch; Krotzky, Timo; Schulz, Robert; Wolber, Gerhard; Bietz, Stefan; Rarey, Matthias; Zimmermann, Markus O; Lange, Andreas; Ruff, Manuel; Heidrich, Johannes; Onlia, Ionut; Exner, Thomas E; Boeckler, Frank M; Bermudez, Marcel; Firaha, Dzmitry S; Holloczki, Oldamur; Kirchner, Barbara; Tautermann, Christofer S; Volkamer, Andrea; Eid, Sameh; Turk, Samo; Rippmann, Friedrich; Fulle, Simone; Saleh, Noureldin; Saladino, Giorgio; Gervasio, Francesco L; Haensele, Elke; Banting, Lee; Whitley, David C; Oliveira Santos, Jana Sopkova-de; Bureau, Ronan; Clark, Timothy; Sandmann, Achim; Lanig, Harald; Kibies, Patrick; Heil, Jochen; Hoffgaard, Franziska; Frach, Roland; Engel, Julian; Smith, Steven; Basu, Debjit; Rauh, Daniel; Kohlbacher, Oliver; Boeckler, Frank M; Essex, Jonathan W; Bodnarchuk, Michael S; Ross, Gregory A; Finkelmann, Arndt R; Goller, Andreas H; Schneider, Gisbert; Husch, Tamara; Schutter, Christoph; Balducci, Andrea; Korth, Martin; Ntie-Kang, Fidele; Gunther, Stefan; Sippl, Wolfgang; Mbaze, Luc Meva'a

A summary of the practical and theoretical (molecular modelling) aspects of conducting polymer research led by the late Dr Sheelagh Campbell at the University of Portsmouth is presented. The wide-range of interest encompasses tailored monomer design, density functional theory calculations, electrodeposition on various substrates, and investigations of polypyrrole as a corrosion inhibitor and as a matrix for release of therapeutic agents.

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.

A novel way of describing molecules in terms of their surfaces and local properties at the surfaces is described. The use of these surfaces and properties to explain chemical reactivity and model simple molecular properties has already been demonstrated. This study reports an examination of the use of these descriptions of molecules to model a simple chemical interaction (complex formation) and a diverse set of mutagens. Both of these systems have been modelled successfully and the results are discussed.

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.

In the past, neural networks have been viewed as classification and regression systems whose internal representations were extremely difficult to interpret. It is now becoming apparent that algorithms can be designed which extract understandable representations from trained neural networks, enabling them to be used for data mining, i.e. the discovery and explanation of previously unknown relationships present in data. This paper reviews existing algorithms for extracting comprehensible representations from neural networks and describes research to generalize and extend the capabilities of one of these algorithms. The algorithm has been generalized for application to bioinformatics datasets, including the prediction of splice site junctions in Human DNA sequences. Results generated on this datasets are compared with those generated by a conventional data mining technique (C5) and conclusions drawn.

A 4.1μs molecular dynamics simulation of the NR4A1 (hNur77) apo-protein has been undertaken and a previously undetected druggable pocket has become apparent that is located remotely from the ‘traditional’ nuclear receptor ligand-binding site. A NR4A1/bis-indole ligand complex at this novel site has been found to be stable over 1 μs of simulation and to result in an interesting conformational transmission to a remote loop that has the capacity to communicate with a NBRE within a RXR-α/NR4A1 heterodimer. Several features of the simulations undertaken indicate how NR4A1 can be affected by alternate-site modulators., PLoS ONE, 10 (8), ISSN:1932-6203

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 β-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 van der Waals surface graph is the graph defined on a van der Waals surface by the intersections of the atomic van der Waals spheres. A van der Waals shape graph has a vertex for each atom with a visible face on the van der Waals surface, and edges between vertices representing atoms with adjacent faces on the van der Waals surface. These are discrete invariants of three‐dimensional molecular shape. Some basic properties of van der Waals surface graphs are studied, including their relationship with the Voronoi diagram of the atom centres, and a class of molecular embeddings is identified for which the dual of the van der Waals surface graph coincides with the van der Waals shape graph.

In ligand–receptor binding it is generally accepted that molecular recognition takes place in a region near the molecular surface and involves both the three-dimensional shape of the surface and the distributions of certain properties on the surface. Consequently, molecular surfaces and surface properties have found extensive application in many aspects of in silico drug design, including virtual screening and the development of quantitative structure–activity relationships, and often play a central role in questions related to molecular similarity. This chapter reviews the various ways in which molecular surfaces are modelled, the surface properties that have been studied and the methods that have been developed for their analysis and application. Several of these methods involve the local extrema of surface properties and a systematic procedure for analysing these through the Morse-Smale complex of the gradient vector field of the property is outlined.

A van der Waals surface graph is the graph defined on the van der Waals surface of a molecule by the intersections of the atomic van der Waals spheres. This is a discrete invariant of three-dimensional molecular shape. Two applications of these graphs to the study of small ring molecules are described: the basic shapes of rings up to order six are classified, and the results of a substructure search are analyzed, indicating how substructures with a specified three-dimensional shape may be identified.

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.

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.

Vicinity analysis (VA) is a new methodology developed to identify similarities between protein binding sites based on their three-dimensional structure and the chemical similarity of matching residues. The major objective is to enable searching of the Protein Data Bank (PDB) for similar sub-pockets, especially in proteins from different structural and biochemical series. Inspection of the ligands bound in these pockets should allow ligand functionality to be identified, thus suggesting novel monomers for use in library synthesis. VA has been developed initially using the ATP binding site in kinases, an important class of protein targets involved in cell signalling and growth regulation. This paper defines the VA procedure and describes matches to the phosphate binding sub-pocket of cyclin-dependent protein kinase 2 that were found by searching a small test database that has also been used to parameterise the methodology.

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.

TREPAN is an algorithm for the extraction of comprehensible rules from trained neural networks. The method has been applied successfully to biological sequence (bioinformatics) problems. It has now been extended to handle chemoinformatics (QSAR) datasets. The method has been shown to have advantages over traditional symbolic rule induction methods such as C5. Results obtained for bioinformatics and chemoinformatics problems using the TREPAN algorithm are presented., TREPAN je algoritam za izlučivanje razumljivih pravila iz neuronskih mreža nakon provedenoga postupka učenja. Metoda je uspješno primjenjivana na probleme u bioinformatici, za analizu bioloških sekvencija. Primjena TREPAN metode sada se proširuje i na analizu skupova podataka u kemoinformatici (QSAR). Pokazano je da metoda ima prednosti u odnosu na uobičajene postupke koji se rabe za indukciju simboličkih pravila poput metode C5. Prikazani su rezultati koji su dobiveni u analizi bioinformatičkih i kemoinformatičkih problema s pomo}u algoritma TREPAN.

In the past, neural networks have been viewed as classification and regression systems whose internal representations were incomprehensible. It is now becoming apparent that algorithms can be designed which extract comprehensible representations from trained neural networks, enabling them to be used for data mining, i.e. the discovery and explanation of previously unknown relationships present in data. This paper reviews existing algorithms for extracting comprehensible representations from neural networks and describes research to generalize and extend the capabilities of one of these algorithms. The algorithm has been generalized for application to bioinformatics datasets, including the prediction of splice site junctions in human DNA sequences. Results generated on this dataset are compared with those generated by a conventional data mining technique (C5), and conclusions are drawn regarding the application of the neural network based technique to other fields of interest.

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.

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.

An unsupervised learning method is proposed for variable selection and its performance assessed using three typical QSAR data sets. The aims of this procedure are to generate a subset of descriptors from any given data set in which the resultant variables are relevant, redundancy is eliminated, and multicollinearity is reduced. Continuum regression, an algorithm encompassing ordinary least squares regression, regression on principal components, and partial least squares regression, was used to construct models from the selected variables. The variable selection routine is shown to produce simple, robust, and easily interpreted models for the chosen data sets.

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.