Enveda Therapeutics (United States)

Nuclear Magnetic Resonance (NMR) is among the most widely used techniques for structure determination, yet automated workflows remain underdeveloped compared to mass spectrometry. In this work, we introduce NMR molecular networking and apply it to Heteronuclear Single Quantum Coherence (HSQC) spectra, a key 2D-NMR experiment for structure elucidation. We adapt core principles of MS² networking such as transitivity across multiple spectra, dereplication, and annotation propagation to NMR-driven workflows. First, we develop a modified Hungarian distance metric for HSQC peak matching. Benchmarks show that using this metric, traditional spectral lookup with this score recovers ~70-80% of available structural similarity, but efficiency does not improve when increasing the size of the spectral library. Second, we establish NMR molecular networking using HSQC spectra to propagate annotations and dereplicate compounds. Case studies of experimental natural product spectra demonstrate that annotation transitivity within networks accelerates and improves identification of unknowns. Third, we introduce algorithmic molecular networking, which integrates graph topology metrics to correct inefficient rankings and reduce false positives. Together, these approaches define the first generalizable framework for NMR molecular networking, enabling scalable, high-throughput annotation for natural product discovery and drug development.

Nuclear Magnetic Resonance (NMR) is among the most widely used techniques for structure determination, yet automated workflows remain underdeveloped compared to mass spectrometry. In this work, we introduce NMR molecular networking and apply it to Heteronuclear Single Quantum Coherence (HSQC) spectra, a key 2D-NMR experiment for structure elucidation. We adapt core principles of MS² networking—transitivity across multiple spectra, dereplication, and annotation propagation—to NMR-driven workflows. First, we develop a modified Hungarian distance metric for HSQC peak matching. Benchmarks show that using this metric, traditional spectral lookup with this score recovers ~70-80% of available structural similarity, but efficiency does not improve when increasing the size of the spectral library. Second, we establish NMR molecular networking using HSQC spectra to propagate annotations and dereplicate compounds. Case studies of experimental natural product spectra demonstrate that annotation transitivity within networks accelerates and improves identification of unknowns. Third, we introduce algorithmic molecular networking, which integrates graph topology metrics to correct inefficient rankings and reduce false positives. Together, these approaches define the first generalizable framework for NMR molecular networking, enabling scalable, high-throughput annotation for natural product discovery and drug development.

Calculatingspectral similarity is a fundamental step in MS/MSdata analysis in untargeted metabolomics experiments, as it facilitatesthe identification of related spectra and the annotation of compounds.To improve matching accuracy when querying an experimental mass spectrumagainst a spectral library, previous approaches have proposed increasingpeak intensities for high m/z ranges.These high m/z values tend to besmaller in magnitude, yet they offer more crucial information foridentifying the chemical structure. Here, we evaluate the impact ofusing these weights for identifying structurally related compoundsand mass spectral library searches. Additionally, we propose a weightingapproach that (i) takes into account the frequency of the m/z values within a spectral library inorder to assign higher importance to the most common peaks and (ii)increases the intensity of lower peaks, similar to previous approaches.To demonstrate our approach, we applied weighting preprocessing tomodified cosine, entropy, and fidelity distance metrics and benchmarkedit against previously reported weights. Our results demonstrate howweighting-based preprocessing can assist in annotating the structureof unknown spectra as well as identifying structurally similar compounds.Finally, we examined scenarios in which the utilization of weightsresulted in diminished performance, pinpointing spectral featureswhere the application of weights might be detrimental.

On August 9-10, 2023, a workshop was convened at the Pacific Northwest National Laboratory (PNNL) in Richland, WA that brought together a group of internationally recognized experts in metabolomics, natural products discovery, chemical ecology, chemical and biological threat assessment, cheminformatics, computational chemistry, cloud computing, artificial intelligence, and novel technology development. These experts were invited to assess the value and feasibility of a grand-scale project to create new technologies that would allow the identification and quantification of all small molecules, or to decode the molecular universe. The Decoding the Molecular Universe project would extend and complement the success of the Human Genome Project by developing new capabilities and technologies to measure small molecules (defined as non-protein, non-polymer molecules less than 1500 Daltons) of any origin and generated in biological systems or produced abiotically. Workshop attendees 1) explored what new understanding of biological and environmental systems could be revealed through the lens of small molecules; 2) characterized the similarities in current needs and technical challenges between each science or mission area for unambiguous and comprehensive determination of the composition and quantities of small molecules of any sample; 3) determined the extent to which technologies or methods currently exist for unambiguously and comprehensively determining the small molecule composition of any sample and in a reasonable time; and 4) identified the attributes of the ideal technology or approach for universal small molecule measurement and identification. The workshop concluded with a discussion of how a project of this scale could be undertaken, possible thrusts for the project, early proof-of-principle applications, and similar efforts upon which the project could be modeled.

Plants are one of the primary sources of natural products for drug development. However, despite centuries of research, only a limited region of the phytochemical space has been studied. To understand the scope of what is explored versus unexplored in the phytochemical space, we begin by reconstructing the known chemical space of the plant kingdom, mapping the distribution of secondary metabolites, chemical classes, and plants traditionally used for medicinal purposes (i.e., medicinal plants) across various levels of the taxonomy. We identify hotspot taxonomic clades occupied by a large proportion of medicinal plants and characterized secondary metabolites, as well as clades requiring further characterization with regard to their chemical composition. In a complementary analysis, we build a chemotaxonomy which has a high level of concordance with the taxonomy at the genus level, highlighting the close relationship between chemical profiles and evolutionary relationships within the plant kingdom. Next, we delve into regions of the phytochemical space with known bioactivity that have been used in modern drug discovery. While we find that the vast majority of approved drugs from phytochemicals are derived from known medicinal plants, we also show that medicinal and non-medicinal plants do not occupy distinct regions of the known phytochemical landscape and their phytochemicals exhibit properties similar to bioactive compounds. Moreover, we also reveal that only a few thousand phytochemicals have been screened for bioactivity and that there are hundreds of known bioactive compounds present in both medicinal and non-medicinal plants, suggesting that non-medicinal plants also have potential therapeutic applications. Overall, these results support the hypothesis that there are many plants with medicinal properties awaiting discovery.

Abstract BackgroundStudies attempting to observe microbes commonly considered uncultivable under standard laboratory conditions are turning to so-called “deep” environmental sequencing approaches whereby they may access these unculturable organisms’ genomes viain silicoapproaches. A typical workflow involves metagenome assembly, annotation, and binning for reconstruction of each respective organism’s genome (or metagenome-assembled genome, MAG). Many automated genome binning approaches have been developed and have displayed a wide range of variation in performance. Therefore, refinement methods have been developed in order to aid manual curation following the automated genome binning process. Current manual curation tools were developed with a focus towards teaching metagenomics concepts and may fail when handling complex datasets containing many microbes. Automappa was developed with a focus on overlaying a variety of annotations such as taxonomy, coverage and marker-gene prevalence while maintaining an implementation that may scale to the complexity of environmental samples.ResultsWe present Automappa, a companion tool and interactive interface for exploration and refinement of Autometa taxon and genome binning results from metagenomes. Selections provide real-time updates of MAG metrics to aid manual curation. Furthermore, researchers may detect unbinned MAGs as well as manually improve their draft-quality MAGs with contigs that closely match the MAG’s genome characteristics. Automappa’s utility has previously been demonstrated on host-associated, marine and terrestrial systems with a total of 242 curated MAGs across fourteen published metagenomes. Of these refined MAGs, the number of high-quality and medium-quality bins increased, consequently lowering the number of low-quality bins and decreasing the amount of data discarded from downstream analyses. The recovery of higher quality MAGs improved the confidence in results and strengthened the resultant conclusions of these respective studies. Automappa consists of three tabs, one for uploading a user’s metagenome data, another for exploration and refinement and the last for providing an overall summary of the refined MAG results.ConclusionsAutomappa is an open source software package that allows researchers to easily assess and refine undetected or draft-quality MAGs from their respective metagenomes. It is freely available under the GPLv3 license athttps://github.com/WiscEvan/Automappaand through Figshare (doi: 10.6084/m9.figshare.22593235).

ADVERTISEMENT RETURN TO ISSUEPREVAddition/CorrectionNEXTORIGINAL ARTICLEThis notice is a correctionCorrection to "Software and Computational Tools for LC-MS-Based Epilipidomics: Challenges and Solutions"Tito DamianiTito DamianiInstitute of Organic Chemistry and Biochemistry of the Czech Academy of Sciences, Flemingovo nám. 2, 160 00 Praha 6, Czech RepublicMore by Tito Damianihttps://orcid.org/0000-0002-4616-900X, Stefano BonciarelliStefano BonciarelliDepartment of Chemistry, Biology and Biotechnology, University of Perugia, Via Elce di Sotto 8, 06123 Perugia, ItalyMore by Stefano Bonciarellihttps://orcid.org/0000-0003-0474-0922, Gerhard G. Thallinger*Gerhard G. ThallingerInstitute of Biomedical Informatics, Graz University of Technology, 8010 Graz, Austria*[email protected]More by Gerhard G. Thallingerhttps://orcid.org/0000-0002-2864-5404, Nikolai KoehlerNikolai KoehlerLipiTUM, Chair of Experimental Bioinformatics, Technical University of Munich, Maximus-von-Imhof Forum 3, 85354 Freising, GermanyMore by Nikolai Koehlerhttps://orcid.org/0000-0002-5103-883X, Christoph A. KrettlerChristoph A. KrettlerEnveda Biosciences, Boulder, Colorado 80301, United StatesMore by Christoph A. Krettlerhttps://orcid.org/0000-0002-5709-3864, Arif K. SalihoğluArif K. SalihoğluDepartment of Physiology, Faculty of Medicine and Institute of Health Sciences, Karadeniz Technical University, 61080 Trabzon, TurkeyMore by Arif K. Salihoğluhttps://orcid.org/0000-0003-3864-9377, Ansgar KorfAnsgar KorfBruker Daltonics GmbH & Co. KG, Fahrenheitstraße 4, 28359 Bremen, GermanyMore by Ansgar Korfhttps://orcid.org/0000-0002-2769-2460, Josch K. PaulingJosch K. PaulingLipiTUM, Chair of Experimental Bioinformatics, Technical University of Munich, Maximus-von-Imhof Forum 3, 85354 Freising, GermanyMore by Josch K. Pauling, Tomáš PluskalTomáš PluskalInstitute of Organic Chemistry and Biochemistry of the Czech Academy of Sciences, Flemingovo nám. 2, 160 00 Praha 6, Czech RepublicMore by Tomáš Pluskalhttps://orcid.org/0000-0002-6940-3006, Zhixu Ni*Zhixu NiCenter of Membrane Biochemistry and Lipid Research, Faculty of Medicine Carl Gustav Carus of TU Dresden, 01307 Dresden, Germany*[email protected]More by Zhixu Nihttps://orcid.org/0000-0003-3662-2621, and Laura Goracci*Laura GoracciDepartment of Chemistry, Biology and Biotechnology, University of Perugia, Via Elce di Sotto 8, 06123 Perugia, Italy*[email protected]More by Laura Goraccihttps://orcid.org/0000-0002-9282-9013Cite this: Anal. Chem. 2023, 95, 6, 3550Publication Date (Web):February 2, 2023Publication History Received19 January 2023Published online2 February 2023Published inissue 14 February 2023https://doi.org/10.1021/acs.analchem.3c00305Copyright © 2023 The Authors. Published by American Chemical SocietyRIGHTS & PERMISSIONSACS AuthorChoiceCC: Creative CommonsBY: Credit must be given to the creatorArticle Views351Altmetric-Citations-LEARN ABOUT THESE METRICSArticle Views are the COUNTER-compliant sum of full text article downloads since November 2008 (both PDF and HTML) across all institutions and individuals. These metrics are regularly updated to reflect usage leading up to the last few days.Citations are the number of other articles citing this article, calculated by Crossref and updated daily. Find more information about Crossref citation counts.The Altmetric Attention Score is a quantitative measure of the attention that a research article has received online. Clicking on the donut icon will load a page at altmetric.com with additional details about the score and the social media presence for the given article. Find more information on the Altmetric Attention Score and how the score is calculated. Share Add toView InAdd Full Text with ReferenceAdd Description ExportRISCitationCitation and abstractCitation and referencesMore Options Share onFacebookTwitterWechatLinked InReddit PDF (889 KB) Get e-Alerts Get e-Alerts

Excess labile heme, occurring under hemolytic conditions, displays a versatile modulator in the blood coagulation system. As such, heme provokes prothrombotic states, either by binding to plasma proteins or through interaction with participating cell types. However, despite several independent reports on these effects, apparently contradictory observations and significant knowledge gaps characterize this relationship, which hampers a complete understanding of heme-driven coagulopathies and the development of suitable and specific treatment options. Thus, the computational exploration of the complex network of heme-triggered effects in the blood coagulation system is presented herein. Combining hemostasis- and heme-specific terminology, the knowledge available thus far was curated and modeled in a mechanistic interactome. Further, these data were incorporated in the earlier established heme knowledge graph, “HemeKG”, to better comprehend the knowledge surrounding heme biology. Finally, a pathway enrichment analysis of these data provided deep insights into so far unknown links and novel experimental targets within the blood coagulation cascade and platelet activation pathways for further investigation of the prothrombotic nature of heme. In summary, this study allows, for the first time, a detailed network analysis of the effects of heme in the blood coagulation system.

Abstract The past two decades have brought a steady growth of pathway databases and pathway enrichment methods. However, the advent of pathway data has not been accompanied by an improvement with regards to interoperability across databases, thus, hampering the use of pathway knowledge from multiple databases for pathway enrichment analyses. While integrative databases have attempted to address this issue by collating pathway knowledge from multiple resources, these approaches do not account for redundant information across them. On the other hand, the majority of studies that employ pathway enrichment analyses still rely upon a single database, though the use of another resource could yield differing results, which is similarly the case when different pathway enrichment methods are employed. These shortcomings call for approaches that investigate the differences and agreements across databases and enrichment methods as their selection in the experimental design of a pathway analysis can be a crucial first step in ensuring the results of such an analysis are meaningful. Here we present DecoPath, a web application to assist in the interpretation of the results of pathway enrichment analysis. DecoPath provides an ecosystem to run pathway enrichment analysis or directly upload results and facilitate the interpretation of these results with custom visualizations that highlight the consensus and/or discrepancies at the pathway- and gene-levels. DecoPath is available at https://decopath.scai.fraunhofer.de and its source code and documentation can be found on GitHub at https://github.com/DecoPath/DecoPath.