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

Statistical filtering for NMR based structure generation

The constitutional assignment of natural products by NMR spectroscopy is usually based on 2D NMR experiments like COSY, HSQC, and HMBC. The difficulty of a structure elucidation problem depends more on the type of the investigated molecule than on its size. Saturated compounds can usually be assigned unambiguously by hand using only COSY and 13C-HMBC data, whereas condensed heterocycles are problematic due to their lack of protons that could show interatomic connectivities. Different computer programs were developed to aid in the structural assignment process, one of them COCON. In the case of unsaturated and substituted molecules structure generators frequently will generate a very large number of possible solutions. This article presents a "statistical filter" for the reduction of the number of results. The filter works by generating 3D conformations using smi23d, a simple MD approach. All molecules for which the generation of constitutional restraints failed were eliminated from the result set. Some structural elements removed by the statistical filter were analyzed and checked against Beilstein. The automatic removal of molecules for which no MD parameter set could be created was included into WEBCOCON. The effect of this filter varies in dependence of the NMR data set used, but in no case the correct constitution was removed from the resulting set

Method and System for Identification of Metabolites Using Mass Spectra

A method and system is provided for mass spectrometry for identification of a specific elemental formula for an unknown compound which includes but is not limited to a metabolite. The method includes calculating a natural abundance probability (NAP) of a given isotopologue for isotopes of non-labelling elements of an unknown compound. Molecular fragments for a subset of isotopes identified using the NAP are created and sorted into a requisite cache data structure to be subsequently searched. Peaks from raw spectrum data from mass spectrometry for an unknown compound. Sample-specific peaks of the unknown com- pound from various spectral artifacts in ultra-high resolution Fourier transform mass spectra are separated. A set of possible isotope-resolved molecular formula (IMF) are created by iteratively searching the molecular fragment caches and combining with additional isotopes and then statistically filtering the results based on NAP and mass-to-charge (m/2) matching probabilities. An unknown compound is identified and its corresponding elemental molecular formula (EMF) from statistically-significant caches of isotopologues with compatible IMFs

Novel methods for the analysis of small molecule fragmentation mass spectra

The identification of small molecules, such as metabolites, in a high throughput manner plays an important in many research areas. Mass spectrometry (MS) is one of the predominant analysis technologies and is much more sensitive than nuclear magnetic resonance spectroscopy. Fragmentation of the molecules is used to obtain information beyond its mass. Gas chromatography-MS is one of the oldest and most widespread techniques for the analysis of small molecules. Commonly, the molecule is fragmented using electron ionization (EI). Using this technique, the molecular ion peak is often barely visible in the mass spectrum or even absent. We present a method to calculate fragmentation trees from high mass accuracy EI spectra, which annotate the peaks in the mass spectrum with molecular formulas of fragments and explain relevant fragmentation pathways. Fragmentation trees enable the identification of the molecular ion and its molecular formula if the molecular ion is present in the spectrum. The method works even if the molecular ion is of very low abundance. MS experts confirm that the calculated trees correspond very well to known fragmentation mechanisms.Using pairwise local alignments of fragmentation trees, structural and chemical similarities to already-known molecules can be determined. In order to compare a fragmentation tree of an unknown metabolite to a huge database of fragmentation trees, fast algorithms for solving the tree alignment problem are required. Unfortunately the alignment of unordered trees, such as fragmentation trees, is NP-hard. We present three exact algorithms for the problem. Evaluation of our methods showed that thousands of alignments can be computed in a matter of minutes. Both the computation and the comparison of fragmentation trees are rule-free approaches that require no chemical knowledge about the unknown molecule and thus will be very helpful in the automated analysis of metabolites that are not included in common libraries

Probabilistic Interaction Network of Evidence Algorithm and its Application to Complete Labeling of Peak Lists from Protein NMR Spectroscopy

The process of assigning a finite set of tags or labels to a collection of observations, subject to side conditions, is notable for its computational complexity. This labeling paradigm is of theoretical and practical relevance to a wide range of biological applications, including the analysis of data from DNA microarrays, metabolomics experiments, and biomolecular nuclear magnetic resonance (NMR) spectroscopy. We present a novel algorithm, called Probabilistic Interaction Network of Evidence (PINE), that achieves robust, unsupervised probabilistic labeling of data. The computational core of PINE uses estimates of evidence derived from empirical distributions of previously observed data, along with consistency measures, to drive a fictitious system M with Hamiltonian H to a quasi-stationary state that produces probabilistic label assignments for relevant subsets of the data. We demonstrate the successful application of PINE to a key task in protein NMR spectroscopy: that of converting peak lists extracted from various NMR experiments into assignments associated with probabilities for their correctness. This application, called PINE-NMR, is available from a freely accessible computer server (http://pine.nmrfam.wisc.edu). The PINE-NMR server accepts as input the sequence of the protein plus user-specified combinations of data corresponding to an extensive list of NMR experiments; it provides as output a probabilistic assignment of NMR signals (chemical shifts) to sequence-specific backbone and aliphatic side chain atoms plus a probabilistic determination of the protein secondary structure. PINE-NMR can accommodate prior information about assignments or stable isotope labeling schemes. As part of the analysis, PINE-NMR identifies, verifies, and rectifies problems related to chemical shift referencing or erroneous input data. PINE-NMR achieves robust and consistent results that have been shown to be effective in subsequent steps of NMR structure determination

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

Robust automatic assignment of nuclear magnetic resonance spectra for small molecules

Abstract. In this document we describe a fully automatic assignment system for Nuclear Magnetic Resonance (NMR) for small molecules. This system has 3 main features: 1. it uses as input raw NMR data. Which means it should be able to extract from them the information that is useful while ignores the noise; 2. it assigns the signals to atoms in the structure, and associates to each assignment a confidence value, which is used to sort all possible solutions; 3. it does not depend on chemical shifts predictions. So it can use the connectivity information observed in 2D NMR spectra and integrals to perform an assignment(coupling constants are also a possibility, but were not explored in this work). However, the system can use chemical shifts if available.; 4. it can learn in an unsupervised fashion, the relation between configurations of atoms and chemical shifts while solving assignment problems, which allows the system to improve while working. Analogous to the way a human works. This system is completely open source, as well as the data used in this work.En este trabajo describimos un sistema completamente automático de asignación de espectros de Resonancia Magnética Nuclear(RMN) para moléculas pequeñas. Este sistema tiene la siguientes características: 1. usa como entrada datos de RMN crudos. Lo que significa que debe ser capaz de extraer de ellos, la información que es útil y dejar de lado el ruido; 2. asigna las señales a átomos en la estructura, y asocia a cada asignación un valor de confianza, que es usado para ordenar todas las posibles soluciones; 3. no depende de predicciones de desplazamientos químicos, de forma que puede usar solo la información de conectividad observada en los espectros de RMN 2D y las integrales( las constantes de acople también son una posibilidad, pero no fueron exploradas en este trabajo). Sin embargo el sistema puede usar los desplazamientos químicos si están disponibles; 4. puede aprender de forma no supervisada, la relación entre configuraciones de átomos y desplazamientos químicos mientras resuelve problemas de asignación, lo que le permite mejorar mientras trabaja, de forma análoga a como lo hace un humano. Este sistema es completamente de código abierto, al igual que los datos que se usaron en este trabajo.Doctorad