Proteome-Wide Characterization of Phosphorylation-Induced Conformational Changes in Breast Cancer

Because of the close link between protein function and protein folding stability, knowledge about phosphorylation-induced protein folding stability changes can lead to a better understanding of the functional effects of protein phosphorylation. Here, the stability of proteins from rates of oxidation (SPROX) and limited proteolysis (LiP) techniques are used to compare the conformational properties of proteins in two MCF-7 cell lysates including one that was and one that was not dephosphorylated with alkaline phosphatase. A total of 168 and 251 protein hits were identified with dephosphorylation-induced stability changes using the SPROX and LiP techniques, respectively. Many protein hits are previously known to be differentially phosphorylated or differentially stabilized in different human breast cancer subtypes, suggesting that the phosphorylation-induced stability changes detected in this work are disease related. The SPROX hits were enriched in proteins with aminoacyl-tRNA ligase activity. These enriched protein hits included many aminoacyl-tRNA synthetases (aaRSs), which are known from previous studies to have their catalytic activity modulated by phosphorylation. The SPROX results revealed that the magnitudes of the destabilizing effects of dephoshporylation on the different aaRSs were directly correlated with their previously reported aminoacylation activity change upon dephosphorylation. This substantiates the close link between protein folding and function

Correlations of R₁₀ with the variations of SOC, soil TN, and soil P in broadleaved and mixed forests.

<p>Correlations of R₁₀ with the variations of SOC, soil TN, and soil P in broadleaved and mixed forests.</p

Relationships of Q₁₀ with variation of herbaceous carbon stock in both forest types.

<p>Relationships of Q₁₀ with variation of herbaceous carbon stock in both forest types.</p

Correlations of R₁₀ with soil temperature range for both forest types.

<p>Correlations of R₁₀ with soil temperature range for both forest types.</p

Chemical Denaturation and Protein Precipitation Approach for Discovery and Quantitation of Protein–Drug Interactions

Described here is a mass spectrometry-based proteomics approach for the large-scale analysis of protein–drug interactions. The approach involves the evaluation of ligand-induced protein folding free energy changes (ΔΔ<i>G</i>_f) using chemical denaturation and protein precipitation (CPP) to identify the protein targets of drugs and to quantify protein–drug binding affinities. This is accomplished in a chemical denaturant-induced unfolding experiment where the folded and unfolded protein fractions in each denaturant containing buffer are quantified by the amount of soluble or precipitated protein (respectively) that forms upon abrupt dilution of the chemical denaturant and subsequent centrifugation of the sample. In the proof-of-principle studies performed here, the CPP technique was able to identify the well-known protein targets of cyclosporin A and geldanamycin in a yeast. The technique was also used to identify protein targets of sinefungin, a broad-based methyltransferase inhibitor, in a human MCF-7 cell lysate. The CPP technique also yielded dissociation constant (<i>K</i>_d) measurements for these well-studied drugs that were in general agreement with previously reported <i>K</i>_d or IC₅₀ values. In comparison to a similar energetics-based technique, termed stability of proteins from rates of oxidation (SPROX), the CPP technique yielded significantly better (∼50% higher) proteomic coverage and a largely reduced false discovery rate

The effects of biophysical variables on R₁₀ and Q₁₀ analyzed by the method of Redundancy Analysis (RDA).

<p>SBD: soil bulk density; DBH: diameter at breast height.</p>a<p>Describe marginal effects, which shows the variance when the variable is used as the only factor.</p>b<p>Describe conditional effects, which shows the additional variance each variable explains when it is included in the model.</p>c<p>The level of significance corresponding to Lambda-A when performing Monte Carlo test (with 499 random permutations) at the 0.05 significance level.</p>d<p>The Monte Carlo test statistics corresponding to Lambda-A at the 0.05 significance level.</p

Trends of R₁₀ and Q₁₀ with basal area ratio of coniferous to broadleaved tree species.

<p>Trends of R₁₀ and Q₁₀ with basal area ratio of coniferous to broadleaved tree species.</p

Large-Scale Analysis of Breast Cancer-Related Conformational Changes in Proteins Using SILAC-SPROX

Proteomic methods for disease state characterization and biomarker discovery have traditionally utilized quantitative mass spectrometry methods to identify proteins with altered expression levels in disease states. Here we report on the large-scale use of protein folding stability measurements to characterize different subtypes of breast cancer using the stable isotope labeling with amino acids in cell culture and stability of proteins from rates of oxidation (SILAC-SPROX) technique. Protein folding stability differences were studied in a comparison of two luminal breast cancer subtypes, luminal-A and -B (i.e., MCF-7 and BT-474 cells, respectively), and in a comparison of a luminal-A and basal subtype of the disease (i.e., MCF-7 and MDA-MB-468 cells, respectively). The 242 and 445 protein hits identified with altered stabilities in these comparative analyses included a large fraction with no significant expression level changes. This suggests thermodynamic stability measurements create a new avenue for protein biomarker discovery. A number of the identified protein hits are known from other biochemical studies to play a role in tumorigenesis and cancer progression. This not only substantiates the biological significance of the protein hits identified using the SILAC-SPROX approach, but it also helps elucidate the molecular basis for their disregulation and/or disfunction in cancer

Correlations of R₁₀ with basal area and CV of shrub carbon stock separately for both forest types.

<p>Correlations of R₁₀ with basal area and CV of shrub carbon stock separately for both forest types.</p

Relationships of Q₁₀ with soil physical factors separately in broadleaved and mixed forest types.

<p>Relationships of Q₁₀ with soil physical factors separately in broadleaved and mixed forest types.</p