ProTargetMiner as a proteome signature library of anticancer molecules for functional discovery

Abstract

Deconvolution of targets and action mechanisms of anticancer compounds is fundamental in drug development. Here, we report on ProTargetMiner as a publicly available expandable proteome signature library of anticancer molecules in cancer cell lines. Based on 287 A549 adenocarcinoma proteomes affected by 56 compounds, the main dataset contains 7,328 proteins and 1,307,859 refined protein-drug pairs. These proteomic signatures cluster by compound targets and action mechanisms. The targets and mechanistic proteins are deconvoluted by partial least square modeling, provided through the website http://protargetminer.genexplain.com. For 9 molecules representing the most diverse mechanisms and the common cancer cell lines MCF-7, RKO and A549, deep proteome datasets are obtained. Combining data from the three cell lines highlights common drug targets and cell-specific differences. The database can be easily extended and merged with new compound signatures. ProTargetMiner serves as a chemical proteomics resource for the cancer research community, and can become a valuable tool in drug discovery.

System-wide identification and prioritization of enzyme substrates by thermal analysis

“…The VIP-values quantify the impact each variable (i.e. protein) has on the OPLS-DA model, with a higher value corresponding to a greater contribution 27 , 30 . Thus the proteins with the highest VIP values are suitable as candidates for validation.…”

“…1 ). The idea of specific response is borrowed from our methods of Functional Identification of Target by Expression Proteomics 26 and ProTargetMiner 27 . Using orthogonal partial least squares-discriminant analysis (OPLS-DA) 28 , we create models where the proteins’ Tm values for the enzyme + cosubstrate treatment are contrasted against those of the other groups: enzyme-treated and cosubstrate-treated lysates.…”

System-wide identification and prioritization of enzyme substrates by thermal analysis (SIESTA)

“…1). The idea of specific response is borrowed from our methods of Functional Identification of Target by Expression Proteomics (FITExP) 21 and ProTargetMiner 22 . In this approach, using orthogonal partial least squares-discriminant analysis (OPLS-DA) 23 , protein Tm in "enzyme + cosubstrate" treatment can be contrasted with those in "control" (cell lysate incubated with vehicle), "enzyme"-treated lysate, and "cosubstrate"-treated lysate.…”

“…In OPLS-DA, the VIP-values show the impact each x-variable (i.e. protein) have on the model with a higher value corresponding to a greater contribution 22,25 . In Supplementary Fig.…”

AA_stat: intelligent profiling ofin vivoandin vitromodifications from open search results

“…Table 4 summarizes the sets of fixed and variable modifications recommended by AA_stat in comparison with the modification sets from annotation of the respective studies. [24][25][26][27][28][29][30][31] Comparing the annotated and AA_stat-retrieved modifications, both expected and unexpected modifications can be mentioned. For example, Cys-carbamidomethylation was annotated for all data, however, two subsets with enrichments of phosphorylated peptides (#9, #10 in Figure 2) and one subset with R-methylation enrichment (#12 in AA_stat demonstrates a potential for uncovering rare modifications.…”