57 research outputs found
Stable <i>S</i>‑Adenosylmethionine Analogue for Enzymatic Fluoromethylation
Fluorine
is an important atom in medicinal chemistry
and agrochemistry,
and the fluoromethyl group, an isostere for various functional groups,
can improve the metabolic stability and biological activity of compounds.
However, enzymes that introduce fluorine and fluorine-containing groups
are rare, and performing selective fluoromethylation remains a great
challenge in organic chemistry. Biocatalytic fluoromethylation is
severely limited by the instability of fluoro S-adenosylmethionine
(SAM). Here, we designed and synthesized a stable fluoro SAM analogue,
fluoro decarboxyl SAM (F-dcSAM). The F-dcSAM analogue is stable and
can be accepted by many O-, S-,
and N-methyltransferases, transferring fluoromethyl
groups to their substrate. F-dcSAM and methyltransferases were applied
to fluoromethylate various compounds, including several bioactive
natural products, with high chemo- and regioselectivity. Kinetics
studies showed that compared to SAM, F-dcSAM is an analogous or even
better substrate for the methyltransferases NtCOMT
and DnrK. We further showed that F-dcSAM can be readily prepared enzymatically
by halide methyltransferase (HMT) from decarboxyl S-adenosyl-l-homocysteine (dcSAH) and CH2FI. The
enzyme cascade reaction involving HMT and methyltransferases can transfer
the CH2F group from CH2FI to substrates efficiently
with multiple turnovers. Therefore, F-dcSAM can be directly used for
enzymatic fluoromethylation or generated in situ through the coupled
activities of HMT and methyltransferases. Our results suggest that
F-dcSAM is a general abiological cofactor of methyltransferases for
late-stage enzymatic fluoromethylation and facilitates the preparation
of fluoro analogues of drug molecules. In addition, F-dcSAM is a stable
nonaromatic sulfonium ion compound that serves as a fluoromethyl donor,
which provides new opportunities for the development of novel CH2F reagents
Additional file 4 of m6A genotypes and prognostic signature for assessing the prognosis of patients with acute myeloid leukemia
Additional file 4
Additional file 2 of m6A genotypes and prognostic signature for assessing the prognosis of patients with acute myeloid leukemia
Additional file 2: Supplementary Figure 2. Identification of the m6A molecular subtypes. (A) CDF curves in consensus clustering (B) Clustering consistency at k = 2-10 (C) Heatmap of sample consistency for optimal clustering groupings
JNSViewer—A JavaScript-based Nucleotide Sequence Viewer for DNA/RNA secondary structures
<div><p>Many tools are available for visualizing RNA or DNA secondary structures, but there is scarce implementation in JavaScript that provides seamless integration with the increasingly popular web computational platforms. We have developed JNSViewer, a highly interactive web service, which is bundled with several popular tools for DNA/RNA secondary structure prediction and can provide precise and interactive correspondence among nucleotides, dot-bracket data, secondary structure graphs, and genic annotations. In JNSViewer, users can perform RNA secondary structure predictions with different programs and settings, add customized genic annotations in GFF format to structure graphs, search for specific linear motifs, and extract relevant structure graphs of sub-sequences. JNSViewer also allows users to choose a transcript or specific segment of <i>Arabidopsis thaliana</i> genome sequences and predict the corresponding secondary structure. Popular genome browsers (i.e., JBrowse and BrowserGenome) were integrated into JNSViewer to provide powerful visualizations of chromosomal locations, genic annotations, and secondary structures. In addition, we used StructureFold with default settings to predict some RNA structures for <i>Arabidopsis</i> by incorporating <i>in vivo</i> high-throughput RNA structure profiling data and stored the results in our web server, which might be a useful resource for RNA secondary structure studies in plants. JNSViewer is available at <a href="http://bioinfolab.miamioh.edu/jnsviewer/index.html" target="_blank">http://bioinfolab.miamioh.edu/jnsviewer/index.html</a>.</p></div
Additional file 3 of m6A genotypes and prognostic signature for assessing the prognosis of patients with acute myeloid leukemia
Additional file 3: Supplementary Figure 3. m6APR_Score independence analysis and evaluation of Nomogram predictive performance. (A) 1-year, 3-year, 5-year calibration curves for Nomogram (B) Decision curves for Nomogram, m6APR_Score
Additional file 5 of m6A genotypes and prognostic signature for assessing the prognosis of patients with acute myeloid leukemia
Additional file 5
Additional file 1 of m6A genotypes and prognostic signature for assessing the prognosis of patients with acute myeloid leukemia
Additional file 1: Supplementary Figure 1. Identification of m6A models. (A) CDF curves in consensus clustering (B) Clustering consistency at k = 2-10 (C) Heatmap of sample consistency for optimal clustering groupings
Forest plots of HR for deaths with SUV<sub>max</sub> (A, unadjusted; B, adjusted), MTV (C, unadjusted; D, adjusted) and TLG (E, unadjusted; F, adjusted).
<p>The Chi<sup>2</sup> test is a measurement of heterogeneity. <i>P</i><0.05 indicates significant heterogeneity. Squares = individual study point estimates. Horizontal lines = 95% CIs. Rhombus = summarized estimate and its 95%CI. Fixed: fixed effect model. Random: random effect model.</p
Secondary structure graphs of a selected rRNA in <i>Arabidopsis</i>.
<p>(a) rRNA (Ensembl ID “ATCG00920.1”) without experimental data as constraints. (b) rRNA (Ensembl ID “ATCG00920.1”) with experimental data as constraints.</p
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