Significance estimation for large scale metabolomics annotations by spectral matching.

The annotation of small molecules in untargeted mass spectrometry relies on the matching of fragment spectra to reference library spectra. While various spectrum-spectrum match scores exist, the field lacks statistical methods for estimating the false discovery rates (FDR) of these annotations. We present empirical Bayes and target-decoy based methods to estimate the false discovery rate (FDR) for 70 public metabolomics data sets. We show that the spectral matching settings need to be adjusted for each project. By adjusting the scoring parameters and thresholds, the number of annotations rose, on average, by +139% (ranging from -92 up to +5705%) when compared with a default parameter set available at GNPS. The FDR estimation methods presented will enable a user to assess the scoring criteria for large scale analysis of mass spectrometry based metabolomics data that has been essential in the advancement of proteomics, transcriptomics, and genomics science.

Exploring the Limits of the Geometric Copolymerization Model.

The geometric copolymerization model is a recently introduced statistical Markov chain model. Here, we investigate its practicality. First, several approaches to identify the optimal model parameters from observed copolymer fingerprints are evaluated using Monte Carlo simulated data. Directly optimizing the parameters is robust against noise but has impractically long running times. A compromise between robustness and running time is found by exploiting the relationship between monomer concentrations calculated by ordinary differential equations and the geometric model. Second, we investigate the applicability of the model to copolymerizations beyond living polymerization and show that the model is useful for copolymerizations involving termination and depropagation reactions.

New Statistical Models for Copolymerization.

For many years, copolymerization has been studied using mathematical and statistical models. Here, we present new Markov chain models for copolymerization kinetics: the Bernoulli and Geometric models. They model copolymer synthesis as a random process and are based on a basic reaction scheme. In contrast to previous Markov chain approaches to copolymerization, both models take variable chain lengths and time-dependent monomer probabilities into account and allow for computing sequence likelihoods and copolymer fingerprints. Fingerprints can be computed from copolymer mass spectra, potentially allowing us to estimate the model parameters from measured fingerprints. We compare both models against Monte Carlo simulations. We find that computing the models is fast and memory efficient.

New kids on the block: novel informatics methods for natural product discovery.

Covering: 2008 to 2014 Mass spectrometry is a key technology for the identification and structural elucidation of natural products. Manual interpretation of the resulting data is tedious and time-consuming, so methods for automated analysis are highly sought after. In this review, we focus on four recently developed methods for the detection and investigation of small molecules, namely MetFrag/MetFusion, ISIS, FingerID, and FT-BLAST. These methods have the potential to significantly advance the field of computational mass spectrometry for the research of natural products. For example, they may help with the dereplication of compounds at an early stage of the drug discovery process; that is, the detection of molecules that are identical or highly similar to known drugs or drug leads. Furthermore, when a potential drug lead has been determined, these tools may help to identify it and elucidate its structure.

Munronia pinnata (Wall.) Theob.: unveiling phytochemistry and dual inhibition of 5-lipoxygenase and microsomal prostaglandin E2 synthase (mPGES)-1.

ETHNOPHARMACOLOGICAL RELEVANCE: Preparations from Munronia pinnata (Wall.) Theob. are extensively used in traditional medicine in Sri Lanka for the treatment of inflammatory conditions. However, neither the pharmacological features nor the phytochemistry of this plant are explored in order to understand and rationalize the reported ethnobotanical significance. As 5-lipoxygenase (5-LO) and microsomal prostaglandin E2 synthase (mPGES)-1 are crucial enzymes in inflammatory disorders, we evaluated their inhibition by M. pinnata extracts and studied the chemical profile of the plant for the identification of relevant constituents. MATERIALS AND METHODS: Cell-free and cell-based assays were employed in order to investigate the suppression of 5-LO and mPGES-1 activity. Cell viability, radical scavenger activities, and inhibition of reactive oxygen species formation (ROS) in neutrophils were studied to assess cytotoxic and antioxidant effects. Gas and liquid chromatography coupled to mass spectrometric analysis enabled the characterization of secondary metabolites. RESULTS: The n-hexane extract of M. pinnata efficiently suppressed 5-LO activity in stimulated human neutrophils (IC50 =8.7µg/ml) and potently inhibited isolated human recombinant 5-LO (IC50 =0.48µg/ml) and mPGES-1 (IC50 =1.0µg/ml). In contrast, no significant radical scavenging activity or suppression of ROS formation was observed, and neutrophil viability was unaffected. The phytochemistry of the plant was unveiled for the first time and phytosterols, fatty acids, sesquiterpenes and several other types of secondary metabolites were identified. CONCLUSIONS: Together, potent inhibition of 5-LO and mPGES-1 activity, without concomitant antioxidant activity and cytotoxic effects, rationalizes the ethnopharmacological use of M. pinnata as anti-inflammatory remedy. Detailed chromatographic/mass spectrometric analysis reveals discrete chemical structures of relevant constituents.

Inhibition of 5-lipoxygenase as anti-inflammatory mode of action of Plectranthus zeylanicus Benth and chemical characterization of ingredients by a mass spectrometric approach.

ETHNOPHARMACOLOGICAL RELEVANCE: The perennial herb Plectranthus zeylanicus Benth is extensively used in traditional medicine in Sri Lanka and South India for treating inflammatory conditions, but pharmacological features of Plectranthus zeylanicus are hardly explored in order to understand and rationalize its use in ethnomedicine. As 5-lipoxygenase (5-LO) is a key enzyme in inflammatory disorders such as asthma or atherosclerosis, we investigated 5-LO inhibition by Plectranthus zeylanicus extracts and analyzed relevant constituents. MATERIALS AND METHODS: We applied cell-free and cell-based assays to investigate suppression of 5-LO activity. Cell viability, radical scavenger activities, and inhibition of reactive oxygen species formation (ROS) in neutrophils were analysed to exclude unspecific cytotoxic or antioxidant effects. Constituents of the extracts were characterized by bioassay-guided fractionation and by analysis using gas or liquid chromatography coupled to mass spectrometric (Orbitrap) analysis. RESULTS: Extracts of Plectranthus zeylanicus prepared with n-hexane or dichloromethane potently suppressed 5-LO activity in stimulated human neutrophils (IC50=6.6 and 12µg/ml, respectively) and inhibited isolated human recombinant 5-LO (IC50=0.7 and 1.2µg/ml, respectively). In contrast, no significant radical scavenging activity or suppression of ROS formation was observed, and neutrophil viability was unaffected. Besides ubiquitously occurring ingredients, coleone P, cinncassiol A and C, and callistric acid were identified as constituents in the most active fraction. CONCLUSIONS: Together, potent inhibition of 5-LO activity, without concomitant anti-oxidant activity and cytotoxic effects, rationalizes the ethnopharmacological use of Plectranthus zeylanicus as anti-inflammatory remedy. Modern chromatographic/mass spectrometric analysis reveals discrete chemical structures of relevant constituents.

Computational mass spectrometry for small molecules.

: The identification of small molecules from mass spectrometry (MS) data remains a major challenge in the interpretation of MS data. This review covers the computational aspects of identifying small molecules, from the identification of a compound searching a reference spectral library, to the structural elucidation of unknowns. In detail, we describe the basic principles and pitfalls of searching mass spectral reference libraries. Determining the molecular formula of the compound can serve as a basis for subsequent structural elucidation; consequently, we cover different methods for molecular formula identification, focussing on isotope pattern analysis. We then discuss automated methods to deal with mass spectra of compounds that are not present in spectral libraries, and provide an insight into de novo analysis of fragmentation spectra using fragmentation trees. In addition, this review shortly covers the reconstruction of metabolic networks using MS data. Finally, we list available software for different steps of the analysis pipeline.

Molecular Formula Identification with SIRIUS.

We present results of the SIRIUS2 submission to the 2012 CASMI contest. Only results for Category 1 (molecular formula identification) were submitted. The SIRIUS method and the parameters used are briefly described, followed by detailed analysis of the results and a discussion of cases where SIRIUS2 was unable to come up with the correct molecular formula. SIRIUS2 returns consistently high quality results, with the exception of fragmentation pattern analysis of time-of-flight data. We then discuss possibilities for further improving SIRIUS2 in the future.

Identifying the unknowns by aligning fragmentation trees.

Mass spectrometry allows sensitive, automated, and high-throughput analysis of small molecules. In principle, tandem mass spectrometry allows us to identify "unknown" small molecules not in any database, but the automated interpretation of such data is in its infancy. Fragmentation trees have recently been introduced for the automated analysis of the fragmentation patterns of small molecules. We present a method for the automated comparison of such fragmentation patterns, based on aligning the compounds' fragmentation trees. We cluster compounds based solely on their fragmentation patterns and show a good agreement with known compound classes. Fragmentation pattern similarities are strongly correlated with the chemical similarity of molecules. We present a tool for searching a database for compounds with fragmentation pattern similar to an unknown sample compound. We apply this tool to metabolites from Icelandic poppy. Our method allows fully automated computational identification of small molecules that cannot be found in any database.

Computing fragmentation trees from metabolite multiple mass spectrometry data.

Since metabolites cannot be predicted from the genome sequence, high-throughput de novo identification of small molecules is highly sought. Mass spectrometry (MS) in combination with a fragmentation technique is commonly used for this task. Unfortunately, automated analysis of such data is in its infancy. Recently, fragmentation trees have been proposed as an analysis tool for such data. Additional fragmentation steps (MS(n)) reveal more information about the molecule. We propose to use MS(n) data for the computation of fragmentation trees, and present the Colorful Subtree Closure problem to formalize this task: There, we search for a colorful subtree inside a vertex-colored graph, such that the weight of the transitive closure of the subtree is maximal. We give several negative results regarding the tractability and approximability of this and related problems. We then present an exact dynamic programming algorithm, which is parameterized by the number of colors in the graph and is swift in practice. Evaluation of our method on a dataset of 45 reference compounds showed that the quality of constructed fragmentation trees is improved by using MS(n) instead of MS² measurements.

Analysis of different synthetic homopolymers by the use of a new calculation software for tandem mass spectra.

The manual interpretation of tandem mass spectra of synthetic polymers is very time-consuming. Therefore, a new software tool was developed to accelerate the interpretation of spectra obtained without requiring any further knowledge about the polymer class or the fragmentation behavior under high-energy collision-induced dissociation (CID) conditions. The software only requires an alphabetical list of elements and a peak list of the measured substance as an xml file for the evaluation of the chosen mass spectrum. Tandem mass spectra of different homopolymers, like poly(2-oxazoline)s, poly(ethylene glycol) and poly(styrene), were interpreted by the new software tool. This contribution describes a fast and automated software tool for the rapid analysis of homopolymers.

Structural fragment clustering reveals novel structural and functional motifs in alpha-helical transmembrane proteins.

BACKGROUND: A large proportion of an organism's genome encodes for membrane proteins. Membrane proteins are important for many cellular processes, and several diseases can be linked to mutations in them. With the tremendous growth of sequence data, there is an increasing need to reliably identify membrane proteins from sequence, to functionally annotate them, and to correctly predict their topology. RESULTS: We introduce a technique called structural fragment clustering, which learns sequential motifs from 3D structural fragments. From over 500,000 fragments, we obtain 213 statistically significant, non-redundant, and novel motifs that are highly specific to alpha-helical transmembrane proteins. From these 213 motifs, 58 of them were assigned to function and checked in the scientific literature for a biological assessment. Seventy percent of the motifs are found in co-factor, ligand, and ion binding sites, 30% at protein interaction interfaces, and 12% bind specific lipids such as glycerol or cardiolipins. The vast majority of motifs (94%) appear across evolutionarily unrelated families, highlighting the modularity of functional design in membrane proteins. We describe three novel motifs in detail: (1) a dimer interface motif found in voltage-gated chloride channels, (2) a proton transfer motif found in heme-copper oxidases, and (3) a convergently evolved interface helix motif found in an aspartate symporter, a serine protease, and cytochrome b. CONCLUSIONS: Our findings suggest that functional modules exist in membrane proteins, and that they occur in completely different evolutionary contexts and cover different binding sites. Structural fragment clustering allows us to link sequence motifs to function through clusters of structural fragments. The sequence motifs can be applied to identify and characterize membrane proteins in novel genomes.

MeMotif: a database of linear motifs in alpha-helical transmembrane proteins.

Membrane proteins are important for many processes in the cell and used as main drug targets. The increasing number of high-resolution structures available makes for the first time a characterization of local structural and functional motifs in alpha-helical transmembrane proteins possible. MeMotif (http://projects.biotec.tu-dresden.de/memotif) is a database and wiki which collects more than 2000 known and novel computationally predicted linear motifs in alpha-helical transmembrane proteins. Motifs are fully described in terms of several structural and functional features and editable. Motifs contained in MeMotif can be used in different biological applications, from the identification of biochemically important functional residues which are candidates for mutagenesis experiments to the improvement of tools for transmembrane protein modeling.