Lars Carlsson

Actin polymerizability is influenced by profilin, a low molecular weight protein in non-muscle cells

We have previously isolated and crystallized a complex from calf spleen, containing actin and a smaller protein which we call profilin. In this paper we describe some properties of this complex, and show that association with profilin is sufficient to explain the persistent monomeric state of some of the actin in spleen extracts; moreover, spleen profilin will recombine with skeletal muscle actin to form a non-polymerizable complex resembling that isolated from spleen. Profilin is not restricted to spleen, but is found in a variety of other tissues and tissue-cultured cell lines. We propose that reversible association of actin with profilin in the cell may provide a mechanism for storage of monomeric actin and controlled turnover of microfilaments.

Selective assay of monomeric and filamentous actin in cell extracts, using inhibition of deoxyribonuclease I.

A simple and selective assay for monomeric and filamentous actin is presented, based on the inhibition of DNAase I by actin. In mixtures of monomeric and filamentous actin, only the monomeric form is measured as DNAase inhibitor. The total amount of actin in a sample can be determined after depolymerization of F actin with guanidine hydrochloride. The assay is rapid enough to detect changes in the polymerization state of actin in vitro over time intervals as short as 3 min. Data characterizing unpolymerized and filamentous actin pools in extracts of human platelets, lymphocytes and HeLa cells are presented.

Depolymerization of F-actin by deoxyribonuclease I

Deoxyribonuclease I causes depolymerization of filamentous muscle actin to form a stable complex of 1 mole DNAase I:1 mole actin. The regulatory proteins tropomyosin and troponin bind to filamentous actin and slow down but do not prevent the depolymerization. In the absense of ATP, heavy meromyosin binds tightly to actin filaments and blocks completely the DNAase I: actin filament interaction. Addition of ATP releases heavy meromyosin; DNAase I is then rapidly inhibited and the actin filaments are depolymerized.

Crystallization of a non-muscle actin

Eukaryotic cells contain a protein which specifically inhibits DNAsae I. This inhibitor protein is closely related to muscle actin. As shown here the inhibitor purified from calf spleen consists of two polypeptides: one which closely resembles muscle actin and a second (15,000 to 20,000 in molecular weight) whose combination with actin has not been recognized before. Crystals of the complex were obtained in ammonium sulphate. They belong to the orthorhombic space group P212121, have unit cell dimensions of a = 187.4 A, b = 72.33 A and c = 38.19 A, and have one molecule of the spleen actin associated with one molecule of the low molecular weight protein per asymmetric unit.

STATE-OF-THE-ART TOOLS FOR COMPUTATIONAL SITE OF METABOLISM PREDICTIONS: COMPARATIVE ANALYSIS, MECHANISTICAL INSIGHTS, AND FUTURE APPLICATIONS

In drug design, it is crucial to have reliable information on how a chemical entity behaves in the presence of metabolizing enzymes. This requires substantial experimental efforts. Consequently, being able to predict the likely site/s of metabolism in any compound, synthesized or virtual, would be highly beneficial and time efficient. In this work, six different methodologies for predictions of the site of metabolism (SOM) have been compared and validated using structurally diverse data sets of drug-like molecules with well-established metabolic pattern in CYP3A4, CYP2C9, or both. Three of the methods predict the SOM based on the ligands chemical structure, two additional methods use structural information of the enzymes, and the sixth method combines structure and ligand similarity and reactivity. The SOM is correctly predicted in 50 to 90% of the cases, depending on method and enzyme, which is an encouraging rate. We also discuss the underlying mechanisms of cytochrome P450 metabolism in the light of the results from this comparison.

Interpretation of Nonlinear QSAR Models Applied to Ames Mutagenicity Data

A method for local interpretation of QSAR models is presented and applied to an Ames mutagenicity data set. In the work presented, local interpretation of Support Vector Machine and Random Forest models is achieved by retrieving the variable corresponding to the largest component of the decision-function gradient at any point in the model. This contribution to the model is the variable that is regarded as having the most importance at that particular point in the model. The method described has been verified using two sets of simulated data and Ames mutagenicity data. This work indicates that it is possible to interpret nonlinear machine-learning methods. Comparison to an interpretable linear method is also presented.

Actin filament formation in pancreatic β-cells during glucose stimulation of insulin secretion

Many of the motile processes performed by eucaryotic cells have been attributed to the functional properties of the actin-based microfilament system [l-3]. In addition to the presence of actin in microfilaments, this protein is also found as unpolymerized monomers in homogenates of various tissues and cells [3]. Monomeric actin can be isolated from a variety of sources in a 1: 1 complex with profilin, a small basic protein affecting the polymerizability of actin [4-71. A rapid biochemical assay for the quantification of actin has been described [8] which can discriminate unpolymerized and filamentous actin in mixtures of purified components and cell homogenates. Using this technique, it was shown that the thrombin-induced transformation of human platelets from smooth discs to spiky spheres, is concomitant with a reorganization of actin from an unpolymerized to a filamentous form [9,10]. The motile behaviour of insulin secreting p-cells from the pancreas is similar in several respects to that observed with other cells [ 1 l-181. The P-cells have also been shown to contain actin [ 191 and myosin [20], and insulin-storage granules interact in vitro with purified fdamentous actin [21]. Here we present evidence to support the idea that microfilaments are directly involved in the secretion of insulin since exposure of the p-cells to glucose, the major physiological stimulator, was accompanied by an increase in the fraction of filamentous actin. * Present address: Department of Medicine, Charing Cross Hospital, Medical School, Fulham Palace Road, London, W6 8RF, England

Introducing Conformal Prediction in Predictive Modeling. A Transparent and Flexible Alternative to Applicability Domain Determination

Conformal prediction is introduced as an alternative approach to domain applicability estimation. The advantages of using conformal prediction are as follows: First, the approach is based on a consistent and well-defined mathematical framework. Second, the understanding of the confidence level concept in conformal predictions is straightforward, e.g. a confidence level of 0.8 means that the conformal predictor will commit, at most, 20% errors (i.e., true values outside the assigned prediction range). Third, the confidence level can be varied depending on the situation where the model is to be applied and the consequences of such changes are readily understandable, i.e. prediction ranges are increased or decreased, and the changes can immediately be inspected. We demonstrate the usefulness of conformal prediction by applying it to 10 publicly available data sets.

Use of historic metabolic biotransformation data as a means of anticipating metabolic sites using MetaPrint2D and Bioclipse

BackgroundPredicting metabolic sites is important in the drug discovery process to aid in rapid compound optimisation. No interactive tool exists and most of the useful tools are quite expensive.ResultsHere a fast and reliable method to analyse ligands and visualise potential metabolic sites is presented which is based on annotated metabolic data, described by circular fingerprints. The method is available via the graphical workbench Bioclipse, which is equipped with advanced features in cheminformatics.ConclusionsDue to the speed of predictions (less than 50 ms per molecule), scientists can get real time decision support when editing chemical structures. Bioclipse is a rich client, which means that all calculations are performed on the local computer and do not require network connection. Bioclipse and MetaPrint2D are free for all users, released under open source licenses, and available from http://www.bioclipse.net.

Stereo Signature Molecular Descriptor

We present an algorithm to compute molecular graph descriptors considering the stereochemistry of the molecular structure based on our previously introduced signature molecular descriptor. The algorithm can generate two types of descriptors, one which is compliant with the Cahn-Ingold-Prelog priority rules, including complex stereochemistry structures such as fullerenes, and a computationally efficient one based on our previous definition of a directed acyclic graph that is augmented to a chiral molecular graph. The performance of the algorithm in terms of speed as a canonicalizer as well as in modeling and predicting bioactivity is evaluated, showing an overall better performance than other molecular descriptors, which is particularly relevant in modeling stereoselective biochemical reactions. The complete source code of the stereo signature molecular descriptor is available for download under an open-source license at http://molsig.sourceforge.net.