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Christensen, N. J.

Publications and source records attributed to Christensen, N. J..

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Conformations of a highly expressed Z19 alpha-zein studied with AlphaFold2 and MD simulations

-zeins are amphiphilic maize proteins with interesting material properties suitable for numerous applications, e.g., in renewable plastics, foods, therapeutics and additive manufacturing (3D-printing). To exploit their full potential, molecular-level insights are essential. Since -zeins have resisted experimental atomic-resolution characterization, molecular models have been central to elucidating their structure, but deep-learning -zein models are largely unexplored. Therefore, this work studies an AlphaFold2 model of a highly expressed zein with molecular dynamics (MD) simulations. The mature protein sequence of the -zein cZ19C2 gave a loosely packed model with 7 -helical segments connected by turns/loops and 68% total -helicity. Compact tertiary structure was limited to a C-terminal bundle of three -helices, each aligning well with the published repeat sequence NPAAYLQQQQLLPFNQLA(V/A)(L/A). The model was subjected to MD simulations in water containing 0, 3, 23, 50, and 100 mol% ethanol for 400 ns, with extension of selected simulations to the s timescale. Although the simulations gave structurally diverse endpoints, several patterns were observed: In water and [&le;] 2 mol% ethanol, the model rapidly formed compact globular structures, largely preserving the C-terminal bundle. At [&ge;] 50 mol% ethanol, extended conformations prevailed, consistent with previous SAXS studies. Tertiary structure was partially stabilized in water and in 2 mol% and 23 mol% ethanol, but was disrupted in [&ge;] 50 mol% ethanol. Averaged results indicated slightly increased helicity with ethanol concentration. Multiple copies of a globular model conformation rapidly formed branched aggregates in aqueous all-atom and coarse-grained MD simulations. {beta}-sheet content was typically <1% across all simulations. In aqueous simulations with glycidyl methacrylate (GMA), 55% of GMA was found within 4 [A] of the model. Pre-reaction complexes for methacrylation were indicated by GMA epoxide carbons within 2.9 - 3.3 [A] of side chain hydroxyl oxygens, suggesting accessibility of reactive sites in compact -zein conformations.

biophysics↗

Identifying interactions in omics data for clinical biomarker discovery

The identification of predictive biomarker signatures from omics data for clinical applications is an active area of research. Recent developments in assay technologies and machine learning (ML) methods have led to significant improvements in predictive performance. However, most high-performing ML methods suffer from complex architectures and lack interpretability. Here, we present the application of a novel symbolic-regression-based algorithm, the QLattice, on a selection of clinical omics data sets. This approach generates parsimonious high-performing models that can both predict disease outcomes and reveal putative disease mechanisms. Due to their high performance, simplicity and explicit functional form, these biomarker signatures can be readily explained, thereby making them attractive tools for high-stakes applications in primary care, clinical decision making and patient stratification.

bioinformatics↗