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Biology subjects

Maloney, K. N.

Publications and source records attributed to Maloney, K. N..

3 recordsLinked to original sources

Flavoaffinins, elusive cellulose-binding natural products from an anaerobic bacterium

Cellulose is the most abundant polymer on earth. Many anaerobic cellulose-degrading bacteria produce uncharacterized yellow-orange, cellulose-binding pigments known as yellow affinity substances (here referred to as flavoaffinins) that are associated with cellulose degradation. Here, we isolate and structurally characterize the flavoaffinins from Clostridium (Hungateiclostridium) thermocellum, a key workhorse for the industrial conversion of cellulosic feedstocks to ethanol. Flavoaffinins represent an unprecedented structural juxtaposition of an aryl polyene chain with a hydroxy-diene {gamma}-lactone. We also shed light on their biosynthesis using stable-isotope feeding experiments. This effort lays the groundwork for understanding the biological function(s) of the flavoaffinins and expands the limited number of natural products isolated from obligately anaerobic microbes.

biochemistry↗

GNPS Dashboard: Collaborative Analysis of Mass Spectrometry Data in the Web Browser

Access to web-based platforms has enabled scientists to perform research remotely. A critical aspect of mass spectrometry data analysis is the inspection, analysis, and visualization of the raw data to validate data quality and confirm statistical observations. We developed the GNPS Dashboard, a web-based data visualization tool, to facilitate synchronous collaborative inspection, visualization, and analysis of private and public mass spectrometry data remotely.

bioinformatics↗

Algorithmic Learning for Auto-deconvolution of GC-MS Data to Enable Molecular Networking within GNPS.

Gas chromatography-mass spectrometry (GC-MS) represents an analytical technique with significant practical societal impact. Spectral deconvolution is an essential step for interpreting GC-MS data. No public GC-MS repositories that also enable repository-scale analysis exist, in part because deconvolution requires significant user input. We therefore engineered a scalable machine learning workflow for the Global Natural Product Social Molecular Networking (GNPS) analysis platform to enable the mass spectrometry community to store, process, share, annotate, compare, and perform molecular networking of GC-MS data. The workflow performs auto-deconvolution of compound fragmentation patterns via unsupervised non-negative matrix factorization, using a Fast Fourier Transform-based strategy to overcome scalability limitations. We introduce a "balance score" that quantifies the reproducibility of fragmentation patterns across all samples. We demonstrate the utility of the platform with breathomics analysis applied to the early detection of oesophago-gastric cancer, and by creating the first molecular spatial map of the human volatilome.

bioinformatics↗