Building blocks for automated elucidation of metabolites: natural product-likeness for candidate ranking

Jayaseelan, Kalai Vanii; Steinbeck, Christoph

doi:10.1186/1471-2105-15-234

Cited by 22 publications

(18 citation statements)

References 13 publications

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“…Data-filtering strategies and other preprocessing methods can be used to optimize models and to increase the interpretability of models focusing on important biochemical changes relating to single or combinatorial pathologies by removing extraneous variation. 43,44 Data-fusion methods have also been developed to interrelate and interpret multiple data matrices, such as NMR, clinical chemistry or gene expression, to achieve a more holistic picture of the dynamic and interactive biological processes in the individual. [45][46][47] Statistical correlation methods applied to spectroscopic data can be used to find candidate biomarkers for a disease, to identify drugs and their metabolites 48,49 and to establish pathway connections between molecules.…”

Section: Key Pointsmentioning

confidence: 99%

The promise of metabolic phenotyping in gastroenterology and hepatology

Holmes

Wijeyesekera

Taylor-Robinson

et al. 2015

Nat Rev Gastroenterol Hepatol

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Disease risk and treatment response are determined, at the individual level, by a complex history of genetic and environmental interactions, including those with our endogenous microbiomes. Personalized health care requires a deep understanding of patient biology that can now be measured using a range of '-omics' technologies. Patient stratification involves the identification of genetic and/or phenotypic disease subclasses that require different therapeutic strategies. Stratified medicine approaches to disease diagnosis, prognosis and therapeutic response monitoring herald a new dimension in patient care. Here, we explore the potential value of metabolic profiling as applied to unmet clinical needs in gastroenterology and hepatology. We describe potential applications in a number of diseases, with emphasis on large-scale population studies as well as metabolic profiling on the individual level, using spectrometric and imaging technologies that will leverage the discovery of mechanistic information and deliver novel health care solutions to improve clinical pathway management.

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Section: Key Pointsmentioning

confidence: 99%

The promise of metabolic phenotyping in gastroenterology and hepatology

Holmes

Wijeyesekera

Taylor-Robinson

et al. 2015

Nat Rev Gastroenterol Hepatol

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“…CASE expert systems have been developed for over 40 years. Currently available packages include the open source Seneca platform [ 105 , 106 ], the commercial ACD/Structure Elucidator Suite [ 107 , 108 , 109 ], LSD [ 110 ], and CMC-se ( ). Here, we review some recent achievements of CASE expert systems.…”

Section: Dereplicationmentioning

confidence: 99%

Computational Methodologies in the Exploration of Marine Natural Product Leads

Pereira

Aires-de-Sousa

2018

Marine Drugs

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Computational methodologies are assisting the exploration of marine natural products (MNPs) to make the discovery of new leads more efficient, to repurpose known MNPs, to target new metabolites on the basis of genome analysis, to reveal mechanisms of action, and to optimize leads. In silico efforts in drug discovery of NPs have mainly focused on two tasks: dereplication and prediction of bioactivities. The exploration of new chemical spaces and the application of predicted spectral data must be included in new approaches to select species, extracts, and growth conditions with maximum probabilities of medicinal chemistry novelty. In this review, the most relevant current computational dereplication methodologies are highlighted. Structure-based (SB) and ligand-based (LB) chemoinformatics approaches have become essential tools for the virtual screening of NPs either in small datasets of isolated compounds or in large-scale databases. The most common LB techniques include Quantitative Structure–Activity Relationships (QSAR), estimation of drug likeness, prediction of adsorption, distribution, metabolism, excretion, and toxicity (ADMET) properties, similarity searching, and pharmacophore identification. Analogously, molecular dynamics, docking and binding cavity analysis have been used in SB approaches. Their significance and achievements are the main focus of this review.

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“…SENECA is a freeware program for Computer Assisted Structure Elucidation (CASE), that allows for the determination of the correct structure and causes of human errors to be detected. This can be very helpful to the spectroscopist, especially when (NMR) spectroscopic data can lead to more than one structural assignment [13]. Alternatively, CAST (CAnonical-representation of STereochemistry)/CNMR Structure Elucidator is a system which uses sophisticated algorithms to elucidate exactly a correct structure.…”

Section: Introductionmentioning

confidence: 99%

“…This may be due to resonance overlap in NMR signals; incorrect chemical shift annotations or misjudged interpretation inferred from the presence or the absence of characteristic spectral features, no matter what the pulse sequence(s) or analytical tools are used [13]. This means a multi-faceted analysis and careful interpretation of the data are often helpful.…”

Section: Introductionmentioning

confidence: 99%

Current and Future Perspectives on the Structural Identification of Small Molecules in Biological Systems

et al. 2016

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Although significant advances have been made in recent years, the structural elucidation of small molecules continues to remain a challenging issue for metabolite profiling. Many metabolomic studies feature unknown compounds; sometimes even in the list of features identified as "statistically significant" in the study. Such metabolic "dark matter" means that much of the potential information collected by metabolomics studies is lost. Accurate structure elucidation allows researchers to identify these compounds. This in turn, facilitates downstream metabolite pathway analysis, and a better understanding of the underlying biology of the system under investigation. This review covers a range of methods for the structural elucidation of individual compounds, including those based on gas and liquid chromatography hyphenated to mass spectrometry, single and multi-dimensional nuclear magnetic resonance spectroscopy, and high-resolution mass spectrometry and includes discussion of data standardization. Future perspectives in structure elucidation are also discussed; with a focus on the potential development of instruments and techniques, in both nuclear magnetic resonance spectroscopy and mass spectrometry that, may help solve some of the current issues that are hampering the complete identification of metabolite structure and function.

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Building blocks for automated elucidation of metabolites: natural product-likeness for candidate ranking

Cited by 22 publications

References 13 publications

The promise of metabolic phenotyping in gastroenterology and hepatology

The promise of metabolic phenotyping in gastroenterology and hepatology

Computational Methodologies in the Exploration of Marine Natural Product Leads

Current and Future Perspectives on the Structural Identification of Small Molecules in Biological Systems

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