Vicinity analysis: a methodology for the identification of similar protein active sites.

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.

QSAR studies using the parashift system.

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.

Selecting compounds for focused screening using linear discriminant analysis and artificial neural networks.

Linear discriminant analysis and a committee of neural networks have been applied to recognise compounds that act at biological targets belonging to a specific gene family, protein kinases. The MDDR database was used to provide compounds targeted against this family and sets of randomly selected molecules. BCUT parameters were employed as input descriptors that encode structural properties and information relevant to ligand-receptor interactions. The technique was applied to purchasing compounds from external suppliers. These compounds achieved hit rates on a par with those achieved using known actives for related targets when tested for the ability to inhibit kinases at a single concentration. This approach is intended as one of a series of filters in the selection of screening candidates, compound purchases and the application of synthetic priorities to combinatorial libraries.

Variable selection and specification of robust QSAR models from multicollinear data: arylpiperazinyl derivatives with affinity and selectivity for alpha2-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.

Simultaneous prediction of aqueous solubility and octanol/water partition coefficient based on descriptors derived from molecular structure.

It has been shown that water solubility and octanol/water partition coefficient for a large diverse set of compounds can be predicted simultaneously using molecular descriptors derived solely from a two dimensional representation of molecular structure. These properties have been modelled using multiple linear regression, artificial neural networks and a statistical method known as canonical correlation analysis. The neural networks give slightly better models both in terms of fitting and prediction presumably due to the fact that they include non-linear terms. The statistical methods, on the other hand, provide information concerning the explanation of variance and allow easy interrogation of the models. Models were fitted using a training set of 552 compounds, a validation set and test set each containing 68 molecules and two separate literature test sets for solubility and partition.

Leaf surfaces and the bioavailability of pesticide residues.

Laboratory bioassays were carried out to determine the toxicity to Folsomia candida Willem (Collembola: Isotomidae) of residues of a pyrethroid insecticide, deltamethrin, and an organophosphorus insecticide, dimethoate, on different leaf surfaces. The test leaves included a range of species and leaves of different ages. Dose-response relationships were estimated for F candida walking over the various treated leaf substrates. Probit analysis was used to estimate the means and standard deviations of the associated tolerance distributions expressed as gAIha-1. Parallelism tests were undertaken to compare the susceptibilities of F candida to the two compounds applied to the different leaf surfaces. On deltamethrin-treated leaf surfaces, the LD50 values for F candida varied from 6.36 to 77.14 gAIha-1. F candida was least susceptible to deltamethrin residues when applied to leaves of dwarf bean (Phaseolus vulgarus L) and the highest susceptibility was observed following application to leaves of seedlings of barley (Hordeum vulgare L). In contrast, the LD50 values observed for dimethoate treatments did not differ significantly between leaf types, ranging from 1.35 to 8.69 gAIha-1. The laboratory data on susceptibility of F candida on different leaf types for different pesticides can be used to investigate the role of leaf surface properties in modifying the toxicity of applied pesticides to exposed invertebrates.

Simultaneous binding of PtdIns(4,5)P2 and clathrin by AP180 in the nucleation of clathrin lattices on membranes.

Adaptor protein 180 (AP180) and its homolog, clathrin assembly lymphoid myeloid leukemia protein (CALM), are closely related proteins that play important roles in clathrin-mediated endocytosis. Here, we present the structure of the NH2-terminal domain of CALM bound to phosphatidylinositol-4,5- bisphosphate [PtdIns(4,5)P2] via a lysine-rich motif. This motif is found in other proteins predicted to have domains of similar structure (for example, Huntingtin interacting protein 1). The structure is in part similar to the epsin NH2-terminal (ENTH) domain, but epsin lacks the PtdIns(4,5)P2-binding site. Because AP180 could bind to PtdIns(4,5)P2 and clathrin simultaneously, it may serve to tether clathrin to the membrane. This was shown by using purified components and a budding assay on preformed lipid monolayers. In the presence of AP180, clathrin lattices formed on the monolayer. When AP2 was also present, coated pits were formed.

Unsupervised forward selection: a method for eliminating redundant variables.

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.

Use of automatic relevance determination in QSAR studies using Bayesian neural networks.

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.

Binding constants of neuraminidase inhibitors: An investigation of the linear interaction energy method.

The linear interaction energy (LIE) method has been applied to the calculation of the binding free energies of 15 inhibitors of the enzyme neuraminidase. This is a particularly challenging system for this methodology since the protein conformation and the number of tightly bound water molecules in the active site are known to change for different inhibitors. It is not clear that the basic LIE method will calculate the contributions to the binding free energies arising from these effects correctly. Application of the basic LIE equation yielded an rms error with respect to experiment of 1.51 kcal mol(-1) for the free energies of binding. To determine whether it is appropriate to include extra terms in the LIE equation, a detailed statistical analysis was undertaken. Multiple linear regression (MLR) is often used to determine the significance of terms in a fitting equation; this method is inappropriate for the current system owing to the highly correlated nature of the descriptor variables. Use of MLR in other applications of the LIE equation is therefore not recommended without a correlation analysis being performed first. Here factor analysis was used to determine the number of useful dimensions contained within the data and, hence, the maximum number of variables to be considered when specifying a model or equation. Biased fitting methods using orthogonalized components were then used to generate the most predictive model. The final model gave a q(2) of 0.74 and contained van der Waals and electrostatic energy terms. This result was obtained without recourse to prior knowledge and was based solely on the information content of the data.

EGF receptor activation stimulates endogenous gastrin gene expression in canine G cells and human gastric cell cultures.

Gastrin release from the antral gastrin-expressing cell (G cell) is regulated by bombesin and luminal factors. Yet, these same extracellular regulators do not stimulate expression of the gene. Since the gastric mucosa expresses large quantities of EGF receptor ligands such as TGFalpha, we examined whether EGF receptor ligands stimulate gastrin gene expression in gastrin-expressing cell cultures. EGF receptor activation of primary cultures stimulated gastrin gene expression about twofold; whereas bombesin treatment of antral G cell cultures stimulated gastrin release but not gene expression. EGF and TGFalpha were weak stimulants of gastrin release. EGF receptor activation of AGS human gastric adenocarcinoma cell line stimulated gastrin gene expression nearly fourfold; and gastrin reporter constructs transfected into AGS cells were stimulated more than fourfold by EGF. EGF induction was conferred by the previously defined GC-rich gastrin EGF response element (gERE) element located at -68 to -53 bp upstream from the cap site since a mutation of the gERE element abolished both basal and EGF induction. Moreover, EGF treatment of AGS cells stimulated binding of the transcription factor Sp1 to this element. Collectively, these results demonstrate that gastrin gene expression and gastrin release are regulated by different signaling pathways: gene expression by EGF receptor activation and gastrin secretion by neuropeptides and luminal factors.

Structure-activity relationships of pyrethroid insecticides. Part 2. The use of molecular dynamics for conformation searching and average parameter calculation.

Molecular dynamics simulations have been performed on a number of conformationally flexible pyrethroid insecticides. The results indicate that molecular dynamics is a suitable tool for conformational searching of small molecules given suitable simulation parameters. The structures derived from the simulations are compared with the static conformation used in a previous study. Various physicochemical parameters have been calculated for a set of conformations selected from the simulations using multivariate analysis. The averaged values of the parameters over the selected set (and the factors derived from them) are compared with the single conformation values used in the previous study.

The computer as an aid in clinical management.

Clinical dietitians' time and expertise have been allocated much more efficiently at Fairview General Hospital through computer analysis of information that is readily available in existing mainframe programs. Other health care institutions may be able to achieve similar results by examining information about patient populations and diet orders stored in their computers.

A new approach to the analysis of complex dose-response relationships: a multivariate study of octopamine action on the leech Leydig neurone.

Octopamine (OA) hyperpolarizes leech Leydig neurones but response amplitudes and thresholds vary. Associations between response, OA dose and other variables [initial resting potential, action potential (AP) frequency and amplitude, temperature, microelectrode resistance, position of ganglion along nerve cord, animal, treatment order] were examined using canonical correlation. A complex response was expressed by summing changes in membrane potential and AP frequency over 2 min of OA treatment. Only dose and ganglion position influenced this response. One significant canonical variate related dose to immediate changes in membrane potential and summed changes in AP frequency throughout the OA effect. Response to OA was greater in cells in posterior ganglia.

The psychiatrist's double bind: the right to refuse medication.

The assertion of a patient's right to refuse medication places a psychiatrist in a double bind because he or she knows that medication will often greatly relieve mental disturbance. Delaying medication until the patient is formally judged incompetent and a guardian appointed causes discomfort for the patient, the physician, staff, and other patients. On the other hand, forcing medication on a patient undermines the latter's sense of autonomy and may interfere with his or her constitutional rights, as a federal judge has ruled in the famous Boston State Hospital case. The right to refuse medication presents a uniquely intriguing case study of a need for accommodation between abstract constitutional concepts and practical realities and has opened a profound legal and ethical debate about the nature of "true freedom."

Dietitians improve patient care with computerized selective menu.

A computer-based system provides a selective menu that meets the dietary prescriptions of each patient; reduces food, printing, and personnel costs; and frees clinical dietitians to spend more time in rendering direct patient care through nutrition assessment and education and in auditing their department's operations.