The most prevalent protein phosphorylation involves the formation of phosphate ester bonds with hydroxyl side chains of serine, threonine, and tyrosine. Two opposing enzyme systems, namely kinases and phosphatases, catalyze protein phosphorylation and dephosphorylation, respectively. Phosphorylation is categorized into four types: O-phosphorylation (pSer, pThr, pTyr), N-phosphorylation (pHis, pArg, pLys), phosphoanhydride (pAsp, pGlu), and phosphorothioate (pCys).
Protein phosphorylation serves as a fundamental regulatory mechanism in numerous cellular processes, and disruptions in phosphorylation are associated with various human diseases. Therefore, conducting a comprehensive quantitative analysis of the phosphorylated protein landscape can significantly drive the exploration of new signaling pathways, drug targets, and biomarkers relevant to the diseases of interest.
Figure 1 Phosphorylation modification sites
Data Analysis of Phosphorylation
Venn Diagram
The commonalities and distinctions among various treatment groups are typically illustrated using a Venn diagram. BioVenn1 effectively depicts the element quantities of different groups, highlighting both similarities and differences, while also representing the size of the datasets.
Figure 2 BioVenn-1
Figure 3 BioVenn-1
Volcano Plot
In the experimental investigation of CHO-K1 cell adaptation to glutamine-free culture medium, a volcano plot was employed to illustrate the changes in protein expression levels among different groups. The threshold was set at fold change < 1.5 and p-value < 0.05 (as shown in Volcano Plot-1). The top 10 differentially expressed proteins were annotated in the plot. In the experiment, differentially expressed proteins with high expression levels and high significance are easily discerned and selected as target proteins. This approach contributes to a reduction in the subsequent experimental complexity.
Figure 4 Volcano Plot-1
Figure 5 Volcano Plot-2
KEGG Pathway Analysis
Proteins exhibiting significant enrichment in the rhythmic oscillation of phosphorylation levels in the liver are notably concentrated in specific metabolic pathways, such as insulin-related pathways, cellular autophagy, and circadian rhythm-associated metabolic pathways (as illustrated in KEGG PATHWAY1-2). In the graph, the horizontal axis represents the degree of enrichment, indicated by the Rich factor value, while the vertical axis depicts the magnitude of the adjusted P-value, underscoring the pivotal role of post-translational modifications in circadian rhythm regulation.
Figure 6 KEGG pathway-1
Figure 7 KEGG pathway-2
Gene Ontology (GO) Analysis
The purpose of GO analysis is to unveil the association between differentially expressed genes and individual characteristic gene functional classes or combinations of multiple functional classes. Taking three distinct growth stages of chili peppers as an example, we present the results of GO analysis in the form of a histogram. The horizontal axis represents the number of differentially expressed proteins mapped, while the vertical axis denotes the GO Term.
Figure 8 GO-1
Figure 9 GO-2
Figure 10 GO-3
Subcellular Localization
For proteins to exert their functions effectively, they must be transported to their designated subcellular structures. Failure to do so can lead to disruptions in organismal functions, resulting in various diseases. The subcellular location of a protein is among its most crucial attributes, aiding in the determination of protein function, unraveling molecular interaction mechanisms, understanding complex physiological processes, and contributing to research on drug development targets.
Figure 11 Subcellular Location
Principal Component Analysis (PCA)
Principal Component Analysis (PCA) is a mathematical dimensionality reduction method that utilizes orthogonal transformations to convert a set of potentially linearly correlated variables into a set of linearly uncorrelated new variables, often referred to as principal components. This transformation allows the representation of the characteristics of the study object in a reduced dimensionality. Few principal components can approximate the variations of the original variables. PCA reflects relationships between different samples, and for a large dataset, it aids in identifying outliers to assess the presence of discrepant samples, facilitating their exclusion in subsequent analyses.
Figure 12 PCA
Protein-Protein Interaction Analysis (PPI Analysis)
The exploration of Protein-Protein Interaction Networks (PPI Networks) is a crucial aspect of proteomics research. Proteins, in executing their biological functions, form PPI networks to maintain temporal and spatial coordination. By constructing interaction networks among differentially expressed proteins, PPI analysis at the proteome level unveils the trends in variations among these proteins. This approach aids in identifying key nodes within the network, providing further insights into the functional significance of differentially expressed proteins.
Figure 13 PPI-1
Figure 14 PPI-2
Motif Analysis
The partial biochemical preferences of enzymes for specific substrates may be determined by the residues surrounding the modification site. This specific residue pattern formed by protein or peptide sequences is referred to as a motif. In homologous protein sequences, the conservation level varies among different sites. Generally, sites that exert a more significant impact on protein function and structure tend to be more conserved, while others are less conserved. These conserved sites are termed "motifs." Functional predictions can be made based on protein sequence features, such as those derived from protein motifs.
Figure 15 motif-1
Figure 16 motif-2
Kinase Prediction Analysis
Protein kinases (PK), enzymes that catalyze the phosphorylation of proteins, play a crucial role in cellular signal transduction. The phosphorylation of proteins is the final step in the transmission of neural information within cells, leading to changes in the state of ion channel proteins and channel gates. Therefore, alterations in kinase activity are closely associated with cancer and various diseases. In the analysis of phosphorylated protein components, motif-based predictions are commonly employed to identify kinases, revealing participants in molecular processes. Frequently utilized kinase prediction websites include: http://hprd.org/PhosphoMotif_finder.
Figure 17 Kinase analysis-1
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