Updates in metabolomics tools and resources: 2014-2015

Data processing and interpretation represent the most challenging and time-consuming steps in high-throughput metabolomic experiments, regardless of the analytical platforms (MS or NMR spectroscopy based) used for data acquisition. Improved machinery in metabolomics generates increasingly complex datasets that create the need for more and better processing and analysis software and in silico approaches to understand the resulting data. However, a comprehensive source of information describing the utility of the most recently developed and released metabolomics resources—in the form of tools, software, and databases—is currently lacking. Thus, here we provide an overview of freely-available, and open-source, tools, algorithms, and frameworks to make both upcoming and established metabolomics researchers aware of the recent developments in an attempt to advance and facilitate data processing workflows in their metabolomics research. The major topics include tools and researches for data processing, data annotation, and data visualization in MS and NMR-based metabolomics. Most in this review described tools are dedicated to untargeted metabolomics workflows; however, some more specialist tools are described as well. All tools and resources described including their analytical and computational platform dependencies are summarized in an overview Table

Computational characterization and prediction of metal-organic framework properties

In this introductory review, we give an overview of the computational chemistry methods commonly used in the field of metal-organic frameworks (MOFs), to describe or predict the structures themselves and characterize their various properties, either at the quantum chemical level or through classical molecular simulation. We discuss the methods for the prediction of crystal structures, geometrical properties and large-scale screening of hypothetical MOFs, as well as their thermal and mechanical properties. A separate section deals with the simulation of adsorption of fluids and fluid mixtures in MOFs

Extension of the B3LYP - Dispersion-Correcting Potential Approach to the Accurate Treatment of both Inter- and Intramolecular Interactions

We recently showed that dispersion-correcting potentials (DCPs), atom-centered Gaussian-type functions developed for use with B3LYP (J. Phys. Chem. Lett. 2012, 3, 1738-1744) greatly improved the ability of the underlying functional to predict non-covalent interactions. However, the application of B3LYP-DCP for the {\beta}-scission of the cumyloxyl radical led a calculated barrier height that was over-estimated by ca. 8 kcal/mol. We show in the present work that the source of this error arises from the previously developed carbon atom DCPs, which erroneously alters the electron density in the C-C covalent-bonding region. In this work, we present a new C-DCP with a form that was expected to influence the electron density farther from the nucleus. Tests of the new C-DCP, with previously published H-, N- and O-DCPs, with B3LYP-DCP/6-31+G(2d,2p) on the S66, S22B, HSG-A, and HC12 databases of non-covalently interacting dimers showed that it is one of the most accurate methods available for treating intermolecular interactions, giving mean absolute errors (MAEs) of 0.19, 0.27, 0.16, and 0.18 kcal/mol, respectively. Additional testing on the S12L database of complexation systems gave an MAE of 2.6 kcal/mol, showing that the B3LYP-DCP/6-31+G(2d,2p) approach is one of the best-performing and feasible methods for treating large systems dominated by non-covalent interactions. Finally, we showed that C-C making/breaking chemistry is well-predicted using the newly developed DCPs. In addition to predicting a barrier height for the {\beta}-scission of the cumyloxyl radical that is within 1.7 kcal/mol of the high-level value, application of B3LYP-DCP/6-31+G(2d,2p) to 10 databases that include reaction barrier heights and energies, isomerization energies and relative conformation energies gives performance that is amongst the best of all available dispersion-corrected density-functional theory approaches

Software Tools and Approaches for Compound Identification of LC-MS/MS Data in Metabolomics.

The annotation of small molecules remains a major challenge in untargeted mass spectrometry-based metabolomics. We here critically discuss structured elucidation approaches and software that are designed to help during the annotation of unknown compounds. Only by elucidating unknown metabolites first is it possible to biologically interpret complex systems, to map compounds to pathways and to create reliable predictive metabolic models for translational and clinical research. These strategies include the construction and quality of tandem mass spectral databases such as the coalition of MassBank repositories and investigations of MS/MS matching confidence. We present in silico fragmentation tools such as MS-FINDER, CFM-ID, MetFrag, ChemDistiller and CSI:FingerID that can annotate compounds from existing structure databases and that have been used in the CASMI (critical assessment of small molecule identification) contests. Furthermore, the use of retention time models from liquid chromatography and the utility of collision cross-section modelling from ion mobility experiments are covered. Workflows and published examples of successfully annotated unknown compounds are included

XML in Motion from Genome to Drug

Information technology (IT) has emerged as a central to the solution of contemporary genomics and drug discovery problems. Researchers involved in genomics, proteomics, transcriptional profiling, high throughput structure determination, and in other sub-disciplines of bioinformatics have direct impact on this IT revolution. As the full genome sequences of many species, data from structural genomics, micro-arrays, and proteomics became available, integration of these data to a common platform require sophisticated bioinformatics tools. Organizing these data into knowledgeable databases and developing appropriate software tools for analyzing the same are going to be major challenges. XML (eXtensible Markup Language) forms the backbone of biological data representation and exchange over the internet, enabling researchers to aggregate data from various heterogeneous data resources. The present article covers a comprehensive idea of the integration of XML on particular type of biological databases mainly dealing with sequence-structure-function relationship and its application towards drug discovery. This e-medical science approach should be applied to other scientific domains and the latest trend in semantic web applications is also highlighted

Identification of novel Cu, Ag, and Au ternary oxides from global structural prediction

We use ab initio global structural prediction, and specifically the minima hopping method, to explore the periodic table in search of novel oxide phases. In total, we study 183 different compositions of the form MXO2, where M=(Cu, Ag, Au) and X is an element of the periodic table. This set includes the well-known Cu delafossite compounds that are, up to now, the best p-type transparent conductive oxides known to mankind. Our calculations discover 81 stable compositions, out of which only 36 are included in available databases. Some of these new phases are potentially good candidates for transparent electrodes. These results demonstrate, on one hand, how incomplete is still our knowledge of the phase-space of stable ternary materials. On the other hand, we show that structural prediction combined with high-throughput approaches is a powerful tool to extend that knowledge, paving the way for the experimental discovery of new materials on a large scale

Identification of functionally related enzymes by learning-to-rank methods

Enzyme sequences and structures are routinely used in the biological sciences as queries to search for functionally related enzymes in online databases. To this end, one usually departs from some notion of similarity, comparing two enzymes by looking for correspondences in their sequences, structures or surfaces. For a given query, the search operation results in a ranking of the enzymes in the database, from very similar to dissimilar enzymes, while information about the biological function of annotated database enzymes is ignored. In this work we show that rankings of that kind can be substantially improved by applying kernel-based learning algorithms. This approach enables the detection of statistical dependencies between similarities of the active cleft and the biological function of annotated enzymes. This is in contrast to search-based approaches, which do not take annotated training data into account. Similarity measures based on the active cleft are known to outperform sequence-based or structure-based measures under certain conditions. We consider the Enzyme Commission (EC) classification hierarchy for obtaining annotated enzymes during the training phase. The results of a set of sizeable experiments indicate a consistent and significant improvement for a set of similarity measures that exploit information about small cavities in the surface of enzymes