Correlating the Chemical Modification of <i>Escherichia coli</i> Ribosomal Proteins with Crystal Structure Data

Various chemical modifications have been applied to study protein structures. In this paper, amidination of E. coli ribosomal proteins was investigated to profile the structure of this large protein/RNA complex. The extent of ribosomal protein amidination was correlated with the solvent accessibility of amine groups in E. coli ribosome crystal structures. The modification of many residues was confirmed by CID of tryptic peptides. The amidination of proteins in the intact ribosome is very consistent with crystal structure data. The extent to which monomethylated amine groups can be amidinated was also examined. This information was used to interpret the amidination of several ribosomal proteins. Interestingly, ribosomal proteins L7 and L12, which share the same sequence and differ only by acetylation of the N-terminus, were found to be methylated to different extents. L12 is largely monomethylated but only a small portion of L7 is so modified

The data mining processes for clinical data (stages as example).

We sort all normal solid tissue stemness data (a: EREG-mRNAsi) and clinical data (b: cancer stage) by patient IDs (TCGAlong.id or id) and pair them with the identical IDs (c). With Table 1, we give each stage a numerical value (d) and sort the data (e) by stemness index (EREG-mRNAsi). By moving the average (f: N = 3 here, N = 21 for actual data), we reduce the noise for the stage trend (g).</p

The sample discrete value table of TCGA clinical-stage data.

The sample discrete value table of TCGA clinical-stage data.</p

Paired data.

Paired data by patient ID. 639 groups contain both normal tissue stemness and clinical cancer metastasis staging data. (https://doi.org/10.6084/m9.figshare.20407044). (XLSX)</p

The sample discrete value table of TCGA clinical-stage data.

The sample discrete value table of TCGA clinical-stage data.</p

Four clinical data as a function of normal stemness.

The linear regression results of the denoised clinical data (a: cancer stage; b: tumor size and invasion; c: distant metastasis; d: lymph node involvement) and Normal stemness in all intervals (0, 1) are shown as solid black lines, and black numbers represent the linear regression results on the interval (0.5, 1) with solid red lines and red numbers. Orange error bars show the calculated SEM while denoising. The error of the slope and intercept is directly expressed in the regression equation (± behind the value represents error; N is the total number of data; R2 is the square of the correlation coefficient; p is the p-value for which the slope is not zero; * means significant).</p

The noise reduction of the four clinical data on the abscissa of the normal tissue stem cell index.

We use Table 2 to quantify the four clinical data (a0, b0, c0 and d0). Each column is the same clinical data type (a0–a4: cancer stage; b0–b4: tumor size and invasion; c0–c4: distant metastasis; d0–d4: lymph node involvement). When using higher and higher noise reduction levels (moving window N in Fig 1), the noise becomes lower and lower (N = 5: a1–d1; N = 11: a2–d2; N = 15: a3–d3; N = 21: a4–d4). All abscissas are normal stemness. Orange error bars represent the SEM for each data point.</p

TCGA Sample IDs of five tumors sampled in Normal stemness (0–1) and cancer stage (stage I–stage IV, or T1–T4) grids.

Red: BRCA (Breast invasive carcinoma); Purple: LUAD (Lung adenocarcinoma); Green: PRAD(Prostate adenocarcinoma); Blue: LUSC(Lung squamous cell carcinoma); Black: STAD(Stomach adenocarcinoma); ‘/’: There is no eligible TCGA Sample ID in this area.</p