Mycolactone is the exotoxin produced by Mycobacterium ulcerans and is the virulence factor behind the neglected tropical disease Buruli ulcer. The toxin has a broad spectrum of biological effects within the host organism, stemming from its interaction with at least two molecular targets and the inhibition of protein uptake into the endoplasmic reticulum. Although it has been shown that the toxin can passively permeate into host cells, it is clearly lipophilic. Association with lipid carriers would have substantial implications for the toxin's distribution within a host organism, delivery to cellular targets, diagnostic susceptibility, and mechanisms of pathogenicity. Yet the toxin's interactions with, and distribution in, lipids are unknown. Herein we have used coarse-grained molecular dynamics simulations, guided by all-atom simulations, to study the interaction of mycolactone with pure and mixed lipid membranes. Using established techniques, we calculated the toxin's preferential localization, membrane translocation, and impact on membrane physical and dynamical properties. The computed water-octanol partition coefficient indicates that mycolactone prefers to be in an organic phase rather than in an aqueous environment. Our results show that in a solvated membrane environment the exotoxin mainly localizes in the water-membrane interface, with a preference for the glycerol moiety of lipids, consistent with the reported studies that found it in lipid extracts of the cell. The calculated association constant to the model membrane is similar to the reported association constant for Wiskott-Aldrich syndrome protein. Mycolactone is shown to modify the physical properties of membranes, lowering the transition temperature, compressibility modulus, and critical line tension at which pores can be stabilized. It also shows a tendency to behave as a linactant, a molecule that localizes at the boundary between different fluid lipid domains in membranes and promotes inter-mixing of domains. This property has implications for the toxin's cellular access, T-cell immunosuppression, and therapeutic potential.

MOTIVATION: Our knowledge of the metabolites in cells and their reactions is far from complete as revealed by metabolomic measurements that detect many more small molecules than are documented in metabolic databases. Here, we develop an approach for predicting the reactivity of small-molecule metabolites in enzyme-catalyzed reactions that combines expert knowledge, computational chemistry and machine learning. RESULTS: We classified 4843 reactions documented in the KEGG database, from all six Enzyme Commission classes (EC 1-6), into 80 reaction classes, each of which is marked by a characteristic functional group transformation. Reaction centers and surrounding local structures in substrates and products of these reactions were represented using SMARTS. We found that each of the SMARTS-defined chemical substructures is widely distributed among metabolites, but only a fraction of the functional groups in these substructures are reactive. Using atomic properties of atoms in a putative reaction center and molecular properties as features, we trained support vector machine (SVM) classifiers to discriminate between functional groups that are reactive and non-reactive. Classifier accuracy was assessed by cross-validation analysis. A typical sensitivity [TP/(TP+FN)] or specificity [TN/(TN+FP)] is ≈0.8. Our results suggest that metabolic reactivity of small-molecule compounds can be predicted with reasonable accuracy based on the presence of a potentially reactive functional group and the chemical features of its local environment. AVAILABILITY: The classifiers presented here can be used to predict reactions via a web site (http://cellsignaling.lanl.gov/Reactivity/). The web site is freely available.

MOTIVATION: Stable isotope labeling of small-molecule metabolites (e.g. (13)C-labeling of glucose) is a powerful tool for characterizing pathways and reaction fluxes in a metabolic network. Analysis of isotope labeling patterns requires knowledge of the fates of individual atoms and moieties in reactions, which can be difficult to collect in a useful form when considering a large number of enzymatic reactions. RESULTS: We report carbon-fate maps for 4605 enzyme-catalyzed reactions documented in the KEGG database. Every fate map has been manually checked for consistency with known reaction mechanisms. A map includes a standardized structure-based identifier for each reactant (namely, an InChI string); indices for carbon atoms that are uniquely derived from the metabolite identifiers; structural data, including an identification of homotopic and prochiral carbon atoms; and a bijective map relating the corresponding carbon atoms in substrates and products. Fate maps are defined using the BioNetGen language (BNGL), a formal model-specification language, which allows a set of maps to be automatically translated into isotopomer mass-balance equations. AVAILABILITY: The carbon-fate maps and software for visualizing the maps are freely available (http://cellsignaling.lanl.gov/FateMaps/).

MOTIVATION: Our knowledge of metabolism is far from complete, and the gaps in our knowledge are being revealed by metabolomic detection of small-molecules not previously known to exist in cells. An important challenge is to determine the reactions in which these compounds participate, which can lead to the identification of gene products responsible for novel metabolic pathways. To address this challenge, we investigate how machine learning can be used to predict potential substrates and products of oxidoreductase-catalyzed reactions. RESULTS: We examined 1956 oxidation/reduction reactions in the KEGG database. The vast majority of these reactions (1626) can be divided into 12 subclasses, each of which is marked by a particular type of functional group transformation. For a given transformation, the local structures of reaction centers in substrates and products can be characterized by patterns. These patterns are not unique to reactants but are widely distributed among KEGG metabolites. To distinguish reactants from non-reactants, we trained classifiers (linear-kernel Support Vector Machines) using negative and positive examples. The input to a classifier is a set of atomic features that can be determined from the 2D chemical structure of a compound. Depending on the subclass of reaction, the accuracy of prediction for positives (negatives) is 64 to 93% (44 to 92%) when asking if a compound is a substrate and 71 to 98% (50 to 92%) when asking if a compound is a product. Sensitivity analysis reveals that this performance is robust to variations of the training data. Our results suggest that metabolic connectivity can be predicted with reasonable accuracy from the presence or absence of local structural motifs in compounds and their readily calculated atomic features. AVAILABILITY: Classifiers reported here can be used freely for noncommercial purposes via a Java program available upon request.