Linking the Resource Description Framework to cheminformatics and proteochemometrics

<p>Abstract</p> <p>Background</p> <p>Semantic web technologies are finding their way into the life sciences. Ontologies and semantic markup have already been used for more than a decade in molecular sciences, but have not found widespread use yet. The semantic web technology Resource Description Framework (RDF) and related methods show to be sufficiently versatile to change that situation.</p> <p>Results</p> <p>The work presented here focuses on linking RDF approaches to existing molecular chemometrics fields, including cheminformatics, QSAR modeling and proteochemometrics. Applications are presented that link RDF technologies to methods from statistics and cheminformatics, including data aggregation, visualization, chemical identification, and property prediction. They demonstrate how this can be done using various existing RDF standards and cheminformatics libraries. For example, we show how IC₅₀ and K<it>_i</it> values are modeled for a number of biological targets using data from the ChEMBL database.</p> <p>Conclusions</p> <p>We have shown that existing RDF standards can suitably be integrated into existing molecular chemometrics methods. Platforms that unite these technologies, like Bioclipse, makes this even simpler and more transparent. Being able to create and share workflows that integrate data aggregation and analysis (visual and statistical) is beneficial to interoperability and reproducibility. The current work shows that RDF approaches are sufficiently powerful to support molecular chemometrics workflows.</p

Encoding, Storing and Searching of Analytical Properties and Assigned Metabolite Structures

Informationen über Metabolite und andere kleine organische Moleküle sind von entscheidender Bedeutung in vielen verschiedenen Bereichen der Naturwissenschaften. Sie spielen z.B. eine entscheidende Rolle in metabolischen Netzwerken und das Wissen über ihre Eigenschaften, hilft komplexe biologische Prozesse und komplette biologische Systeme zu verstehen. Da in biologischen und chemischen Laboren täglich Daten anfallen, welche diese Moleküle beschreiben, existiert eine umfassende Datengrundlage, die sich kontinuierlich erweitert. Um Wissenschaftlern die Verarbeitung, den Austausch, die Archivierung und die Suche innerhalb dieser Informationen unter Erhaltung der semantischen Zusammenhänge zu ermöglichen, sind komplexe Softwaresysteme und Datenformate nötig. Das Ziel dieses Projektes bestand darin, Anwendungen und Algorithmen zu entwickeln, welche für die effiziente Kodierung, Sammlung, Normalisierung und Analyse molekularer Daten genutzt werden können. Diese sollen Wissenschaftler bei der Strukturaufklärung, der Dereplikation, der Analyse von molekularen Wechselwirkungen und bei der Veröffentlichung des so gewonnenen Wissens unterstützen. Da die direkte Beschreibung der Struktur und der Funktionsweise einer unbekannten Verbindung sehr schwierig und aufwändig ist, wird dies hauptsächlich indirekt, mit Hilfe beschreibender Eigenschaften erreicht. Diese werden dann zur Vorhersage struktureller und funktioneller Charakteristika genutzt. In diesem Zusammenhang wurden Programmmodule entwickelt, welche sowohl die Visualisierung von Struktur- und Spektroskopiedaten, die gegliederte Darstellung und Veränderung von Metadaten und Eigenschaften, als auch den Import und Export von verschiedenen Datenformaten erlauben. Diese wurden durch Methoden erweitert, welche es ermöglichen, die gewonnenen Informationen weitergehend zu analysieren und Struktur- und Spektroskopiedaten einander zuzuweisen. Außerdem wurde ein System zur strukturierten Archivierung und Verwaltung großer Mengen molekularer Daten und spektroskopischer Informationen, unter Beibehaltung der semantischen Zusammenhänge, sowohl im Dateisystem, als auch in Datenbanken, entwickelt. Um die verlustfreie Speicherung zu gewährleisten, wurde ein offenes und standardisiertes Datenformat definiert (CMLSpect). Dieses erweitert das existierende CML (Chemical Markup Language) Vokabular und erlaubt damit die einfache Handhabung von verknüpften Struktur- und Spektroskopiedaten. Die entwickelten Anwendungen wurden in das Bioclipse System für Bio- und Chemoinformatik eingebunden und bieten dem Nutzer damit eine hochqualitative Benutzeroberfläche und dem Entwickler eine leicht zu erweiternde modulare Programmarchitektur

Automated analysis and validation of open chemical data

Methods to automatically extract Open Data from the chemical literature, validate it, and use it to validate theory are examined. Chemical identifiers which assist the automatic location of chemical structures using commercial Web search engines are investigated. The IUPAC International Chemical Idenfitifer (InChI) gives almost 100% recall and precision, though is shown to be too long for present search engines. A combination of InChI and InChIKey, a shorter, fixed-length hash of the InChI string, is concluded to be the best current method of identifying structures. The proportion of published, Open Crystallographic Information Files (CIFs) that are valid with respect to the specification is shown to be improving, and is around 99% in 2007. The error rate in the conversion of valid CIFs to Chemical Markup Language (CML) is less than 0.2%. The machine generation of connection tables from CIFs requires many heuristics, and in some cases it is impossible to deduce the exact connection table. CrystalEye, a fully-automated system for the reformulation of the fragmented crystallographic Web into a structured XML-based repository is described. Published, Open CIFs can be located and aggregated programmatically with almost 100% recall. It is shown that, by converting CIF data to CML, software can be created to use the latest Web standards and technologies to enhance the ability of Web users to browse, find, keep updated, download and reuse the latest published crystallography. A workflow for the high-throughput calculation of solid-state geometry using a semi-empirical method is described. A wide-range of organic and inorganic systems provided by CrystalEye are used to test both the data and the method. Several errors in the method are discovered, many of which can be attributed to the parameterization process. An Open NMR experiment to perform high-throughput prediction of 13C chemical shifts using a GIAO protocol is described. The data and analysis were provided on publicly-available webpages to enable crowdsourcing, which assisted in discovering an error rate of 6.1% in the starting data. The protocol was refined during the work and shown to have an average unsigned error of 2.24ppm for 13C nuclei of small, rigid molecules; comparable to the errors observed elsewhere for general structures using HOSE and Neural Network methods

Building blocks for automated elucidation of metabolites: Machine learning methods for NMR prediction

BACKGROUND: Current efforts in Metabolomics, such as the Human Metabolome Project, collect structures of biological metabolites as well as data for their characterisation, such as spectra for identification of substances and measurements of their concentration. Still, only a fraction of existing metabolites and their spectral fingerprints are known. Computer-Assisted Structure Elucidation (CASE) of biological metabolites will be an important tool to leverage this lack of knowledge. Indispensable for CASE are modules to predict spectra for hypothetical structures. This paper evaluates different statistical and machine learning methods to perform predictions of proton NMR spectra based on data from our open database NMRShiftDB. RESULTS: A mean absolute error of 0.18 ppm was achieved for the prediction of proton NMR shifts ranging from 0 to 11 ppm. Random forest, J48 decision tree and support vector machines achieved similar overall errors. HOSE codes being a notably simple method achieved a comparatively good result of 0.17 ppm mean absolute error. CONCLUSION: NMR prediction methods applied in the course of this work delivered precise predictions which can serve as a building block for Computer-Assisted Structure Elucidation for biological metabolites

Sherlock—A Free and Open-Source System for the Computer-Assisted Structure Elucidation of Organic Compounds from NMR Data

The structure elucidation of small organic molecules (<1500 Dalton) through 1D and 2D nuclear magnetic resonance (NMR) data analysis is a potentially challenging, combinatorial problem. This publication presents Sherlock, a free and open-source Computer-Assisted Structure Elucidation (CASE) software where the user controls the chain of elementary operations through a versatile graphical user interface, including spectral peak picking, addition of automatically or user-defined structure constraints, structure generation, ranking and display of the solutions. A set of forty-five compounds was selected in order to illustrate the new possibilities offered to organic chemists by Sherlock for improving the reliability and traceability of structure elucidation results

ChemEngine: harvesting 3D chemical structures of supplementary data from PDF files

Additional file 2. Recreated 3D geometry optimized structures of 29 molecules as visualized in the original program (Gauss View)

Blue Obelisk - Interoperability in chemical informatics

The Blue Obelisk Movement (http://www.blueobelisk.org/) is the name used by a diverse Internet group promoting reusable chemistry via open source software development, consistent and complimentary chemoinformatics research, open data, and open standards. We outline recent examples of cooperation in the Blue Obelisk group:  a shared dictionary of algorithms and implementations in chemoinformatics algorithms drawing from our various software projects; a shared repository of chemoinformatics data including elemental properties, atomic radii, isotopes, atom typing rules, and so forth; and Web services for the platform-independent use of chemoinformatics programs

Understanding the Polymerization of Polyfurfuryl Alcohol: Ring Opening and Diels-Alder Reactions

Polyfurfuryl alcohol (PFA) is one of the most intriguing polymers because, despite its easy polymerization in acid environment, its molecular structure is definitely not obvious. Many studies have been performed in recent decades, and every time, surprising aspects came out. With the present study, we aim to take advantage of all of the findings of previous investigations and exploit them for the interpretation of the completely cured PFA spectra registered with three of the most powerful techniques for the characterization of solid, insoluble polymers: Solid-State 13C-NMR, Attenuated Total Reflectance (ATR), Fourier Transform Infrared (FTIR) spectroscopy, and UV-resonant Raman spectroscopy at different excitation wavelengths, using both an UV laser source and UV synchrotron radiation. In addition, the foreseen structures were modeled and the corresponding 13C-NMR and FTIR spectra were simulated with first-principles and semi-empiric methods to evaluate their matching with experimental ones. Thanks to this multi-technique approach, based on complementary analytical tools and computational support, it was possible to conclude that, in addition to the major linear unconjugated polymerization, the PFA structure consists of Diels-Alder rearrangements occurring after the opening of some furanic units, while the terminal moieties of the chain involves \u3b3-lactone arrangements. The occurrence of head-head methylene ether bridges and free hydroxyl groups (from unreacted furfuryl alcohol, FA, or terminal chains) could be excluded, while the conjugated systems could be considered rather limited