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_max (A, unadjusted; B, adjusted), MTV (C, unadjusted; D, adjusted) and TLG (E, unadjusted; F, adjusted).

<p>The Chi² 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

Flowchart of Study Selection.

<p>Flowchart of Study Selection.</p