GNPS Annotation of natural product compound families using molecular networking topology and structural similarity fingerprinting
Ontology highlight
ABSTRACT:
INSTRUMENT(S): Waters Synapt G2-Si
ORGANISM(S): Marine Actinobacteria
SUBMITTER: 
Roger Linington
PROVIDER: MSV000089680 | GNPS | Fri Jun 17 07:22:00 BST 2022
REPOSITORIES: GNPS
Similar Datasets
Project description:Spectral matching of MS2 fragmentation spectra has become a popular method for characterizing natural products libraries but identification remains challenging due to differences in MS2 fragmentation properties between instruments and the low coverage of current spectral reference libraries. To address this bottleneck we present Structural similarity Network Annotation Platform for Mass Spectrometry (SNAP-MS) which matches chemical similarity grouping in the Natural Products Atlas to grouping of mass spectrometry features from molecular networking. This approach assigns compound families to molecular networking subnetworks without the need for experimental or calculated reference spectra. We demonstrate SNAP-MS can accurately annotate subnetworks built from both reference spectra and an in-house microbial extract library, and correctly predict compound families from published molecular networks acquired on a range of MS instrumentation. Compound family annotations for the microbial extract library are validated by co-injection of standards or isolation and spectroscopic analysis. SNAP-MS is freely available at www.npatlas.org/discover/snapms .
| S-EPMC9852437 | biostudies-literature
Project description:HDX-MS data comparing deuteration of the M. tuberculosis proteasome core particle across four protein states (WT, T1A, OG and Ixazomib-bound) collected on a SYNAPT G2-Si.
| MSV000095758 | MassIVE
Project description:Natural products are a rich resource of bioactive compounds for valuable applications across multiple fields such as food, agriculture, and medicine. For natural product discovery, high throughput in silico screening offers a cost-effective alternative to traditional resource-heavy assay-guided exploration of structurally novel chemical space. In this data descriptor, we report a characterized database of 67,064,204 natural product-like molecules generated using a recurrent neural network trained on known natural products, demonstrating a significant 165-fold expansion in library size over the approximately 400,000 known natural products. This study highlights the potential of using deep generative models to explore novel natural product chemical space for high throughput in silico discovery.
| S-EPMC10199072 | biostudies-literature
Project description:Pathway analysis is an informative method for comparing and contrasting drug-induced gene expression in cellular systems. Here, we define the effects of the marine natural product fucoxanthin, separately and in combination with the prototypic phosphatidylinositol 3-kinase (PI3K) inhibitor LY-294002, on gene expression in a well-established human glioblastoma cell system, U87MG. Under conditions which inhibit cell proliferation, LY-294002 and fucoxanthin modulate many pathways in common, including the retinoblastoma, DNA damage, DNA replication and cell cycle pathways. In sharp contrast, we see profound differences in the expression of genes characteristic of pathways such as apoptosis and lipid metabolism, contributing to the development of a differentiated and distinctive drug-induced gene expression signature for each compound. Furthermore, in combination, fucoxanthin synergizes with LY-294002 in inhibiting the growth of U87MG cells, suggesting complementarity in their molecular modes of action and pointing to further treatment combinations. The synergy we observe between the dietary nutraceutical fucoxanthin and the synthetic chemical LY-294002 in producing growth arrest in glioblastoma, illustrates the potential of nutri-pharmaceutical combinations in targeting this challenging disease.
| S-EPMC7500592 | biostudies-literature
Project description:Owing to biotransformation, xenobiotics are often found in conjugated form in biological samples such as urine and plasma. Liquid chromatography coupled with accurate mass spectrometry with multistage collision-induced dissociation provides spectral information concerning these metabolites in complex materials. Unfortunately, compound databases typically do not contain a sufficient number of records for such conjugates. We report here on the development of a novel protocol, referred to as ChemProphet, to annotate compounds, including conjugates, using compound databases such as PubChem and ChemSpider. The annotation of conjugates involves three steps: 1. Recognition of the type and number of conjugates in the sample; 2. Compound search and annotation of the deconjugated form; and 3. In silico evaluation of the candidate conjugate. ChemProphet assigns a spectrum to each candidate by automatically exploring the substructures corresponding to the observed product ion spectrum. When finished, it annotates the candidates assigning a rank for each candidate based on the calculated score that ranks its relative likelihood. We assessed our protocol by annotating a benchmark dataset by including the product ion spectra for 102 compounds, annotating the commercially available standard for quercetin 3-glucuronide, and by conducting a model experiment using urine from mice that had been administered a green tea extract. The results show that by using the ChemProphet approach, it is possible to annotate not only the deconjugated molecules but also the conjugated molecules using an automatic interpretation method based on deconjugation that involves multistage collision-induced dissociation and in silico calculated conjugation.
| S-EPMC4570942 | biostudies-literature
Project description:This SuperSeries is composed of the SubSeries listed below.
2023-05-31 | GSE231359 | GEO
Project description:The cell wall-targeting echinocandin antifungals, although potent and well-tolerated, are inadequate in treating fungal infections due to their narrow spectrum of activity and their propensity to induce pathogen resistance. A promising strategy to overcome these drawbacks is to combine echinocandins with a molecule that improves their activity and also disrupts drug adaptation pathways. In this study, we show that puupehenone (PUUP), a marine sponge-derived sesquiterpene quinone potentiates the echinocandin drug, caspofungin (CAS) in CAS-resistant fungal pathogens. We have conducted RNA-Seq analysis, followed by genetic and molecular studies, to elucidate PUUP’s CAS-potentiating mechanism. We found that the combination of CAS and PUUP blocked the induction of CAS-responding genes required for the adaptation to cell wall stress through the cell wall integrity (CWI) pathway. Further analysis showed that PUUP inhibited the activation of Slt2 (Mpk1), the terminal MAP kinase in this pathway. We also found that PUUP induced heat shock response genes and inhibited the activity of heat shock protein 90 (Hsp90). Molecular docking studies predicted that PUUP occupies a binding site on Hsp90 required for the interaction between Hsp90 and its co-chaperone Cdc37. Thus, we show that PUUP potentiates CAS activity by a previously undescribed mechanism which involves disruption of Hsp90 activity and the CWI pathway. Given the requirement of the Hsp90-Cdc37 complex in Slt2 activation, we suggest that inhibitors of this complex would disrupt the CWI pathway and synergize with echinocandins. Therefore, the identification of PUUP’s CAS-potentiating mechanism has important implications in the development of new antifungal combination therapies.
2019-12-04 | GSE140563 | GEO
Project description:Natural products play a major role in the discovery of novel bioactive compounds. In this regard, the synthesis of natural product-inspired and -derived analogues is an active field that is further developing. Several strategies and principles for the design of such compounds have been developed to streamline their access and synthesis. This perspective describes how individual strategies or their elements can be combined depending on the project goal. Illustrative examples are shown that demonstrate the blurred lines between approaches and how they can work in concert to discover new biologically active molecules. Lastly, a general set of guidelines for choosing an appropriate strategy combination for the specific purpose is presented.
| S-EPMC11788825 | biostudies-literature
Project description:To identify biologically relevant and drug-like protein ligands for medicinal chemistry and chemical biology research the grouping of proteins according to evolutionary relationships and conservation of molecular recognition is an established method. We propose to employ structure similarity clustering of the ligand-sensing cores of protein domains (PSSC) in conjunction with natural product guided compound library development as a synergistic approach for the identification of biologically prevalidated ligands with high fidelity. This is supported by the concepts that (i) in nature spatial structure is more conserved than amino acid sequence, (ii) the number of fold types characteristic for all protein domains is limited, and (iii) the underlying frameworks of natural product classes with multiple biological activities provide evolutionarily selected starting points in structural space. On the basis of domain core similarity considerations and irrespective of sequence similarity, Cdc25A phosphatase, acetylcholinesterase, and 11beta-hydroxysteroid dehydrogenases type 1 and type 2 were grouped into a similarity cluster. A 147-member compound collection derived from the naturally occurring Cdc25A inhibitor dysidiolide yielded potent and selective inhibitors of the other members of the similarity cluster with a hit rate of 2-3%. Protein structure similarity clustering may provide an experimental opportunity to identify supersites in proteins.
| S-EPMC534721 | biostudies-literature
Project description:Interventions: Gold Standard:;Index test:
Primary outcome(s): arrival time;time to peak;base intensity;peak intensity
Study Design: Diagnostic test for accuracy
| 2662402 | ecrin-mdr-crc