piNET: a versatile web platform for downstream analysis and visualization of proteomics data

Abstract

Rapid progress in proteomics and large-scale profiling of biological systems at the protein level necessitates the continued development of efficient computational tools for the analysis and interpretation of proteomics data. Here, we present the piNET server that facilitates integrated annotation, analysis and visualization of quantitative proteomics data, with emphasis on PTM networks and integration with the LINCS library of chemical and genetic perturbation signatures in order to provide further mechanistic and functional insights. The primary input for the server consists of a set of peptides or proteins, optionally with PTM sites, and their corresponding abundance values. Several interconnected workflows can be used to generate: (i) interactive graphs and tables providing comprehensive annotation and mapping between peptides and proteins with PTM sites; (ii) high resolution and interactive visualization for enzyme-substrate networks, including kinases and their phospho-peptide targets; (iii) mapping and visualization of LINCS signature connectivity for chemical inhibitors or genetic knockdown of enzymes upstream of their target PTM sites. piNET has been built using a modular Spring-Boot JAVA platform as a fast, versatile and easy to use tool. The Apache Lucene indexing is used for fast mapping of peptides into UniProt entries for the human, mouse and other commonly used model organism proteomes. PTM-centric network analyses combine PhosphoSitePlus, iPTMnet and SIGNOR databases of validated enzyme-substrate relationships, for kinase networks augmented by DeepPhos predictions and sequence-based mapping of PhosphoSitePlus consensus motifs. Concordant LINCS signatures are mapped using iLINCS. For each workflow, a RESTful API counterpart can be used to generate the results programmatically in the json format. The server is available at http://pinet-server.org, and it is free and open to all users without login requirement.

Figure 1.

The overall flowchart of piNET proteoform-centric mapping, annotation and visualization workflow.

Figure 2.

Peptide to protein to gene mapping for a subset of P100 representative phospho-peptides with PTM modification sites mapped into the corresponding protein and gene entities. Note that some peptides match multiple proteins and/or genes (many to many mapping), as indicated by thick blue edges for the highlighted peptides.

Figure 3.

Functional annotation and visualization of a PTM network for a set of P100 phospho-peptide probes using curated causal annotations from SIGNOR, overlaid with a representative P100 expression (abundance) profile for a non-specific kinase inhibitor, staurosporin, generated as part of the LINCS project. The Z-score transformed relative abundance of P100 phospho-peptides in the right column is indicated by the color of nodes: blue for lower (down-regulated sites) and yellow for higher (up-regulated sites) than average, respectively. Note that P100 phospho-sites and their known modifiers (or regulators) are connected using red or blue edges for positive (or activating) versus negative (or inactivating) relationships, respectively. The modifying enzymes (kinases) in the left column are shown as red nodes, whereas other type of regulators, such as those involved in protein-protein interactions, are shown as black nodes.

Figure 4.

Connectivity analysis for a user provided input signature (here L1000 signature of a multi-targeted kinase inhibitor midostaurin), represented by the gray node in the figure. Strongly connected LINCS signatures of chemical perturbations, including midostaurin in different cell lines and related drugs, are shown in red for positive and blue for negative correlations, respectively. For each perturbation, the name of the compound and the cell line used are indicated in the labels. The shade of the node represents the strength of correlation (darker shades corresponding to higher correlations squared), whereas the size of the node represents its statistical significance (P-values computed by iLINCS).