SiGNet: A signaling network data simulator to enable signaling network inference

<div><p>Network models are widely used to describe complex signaling systems. Cellular wiring varies in different cellular contexts and numerous inference techniques have been developed to infer the structure of a network from experimental data of the network’s behavior. To objectively identify which inference strategy is best suited to a specific network, a gold standard network and dataset are required. However, suitable datasets for benchmarking are difficult to find. Numerous tools exist that can simulate data for transcriptional networks, but these are of limited use for the study of signaling networks. Here, we describe SiGNet (<u>Si</u>gnal <u>G</u>enerator for <u>Net</u>works): a Cytoscape app that simulates experimental data for a signaling network of known structure. SiGNet has been developed and tested against published experimental data, incorporating information on network architecture, and the directionality and strength of interactions to create biological data in silico. SiGNet is the first tool to simulate biological signaling data, enabling an accurate and systematic assessment of inference strategies. SiGNet can also be used to produce preliminary models of key biological pathways following perturbation.</p></div

Distinctive Behaviors of Druggable Proteins in Cellular Networks

<div><p>The interaction environment of a protein in a cellular network is important in defining the role that the protein plays in the system as a whole, and thus its potential suitability as a drug target. Despite the importance of the network environment, it is neglected during target selection for drug discovery. Here, we present the first systematic, comprehensive computational analysis of topological, community and graphical network parameters of the human interactome and identify discriminatory network patterns that strongly distinguish drug targets from the interactome as a whole. Importantly, we identify striking differences in the network behavior of targets of cancer drugs versus targets from other therapeutic areas and explore how they may relate to successful drug combinations to overcome acquired resistance to cancer drugs. We develop, computationally validate and provide the first public domain predictive algorithm for identifying druggable neighborhoods based on network parameters. We also make available full predictions for 13,345 proteins to aid target selection for drug discovery. All target predictions are available through <a href="http://canSAR.icr.ac.uk" target="_blank">canSAR.icr.ac.uk</a>. Underlying data and tools are available at <a href="https://cansar.icr.ac.uk/cansar/publications/druggable_network_neighbourhoods/" target="_blank">https://cansar.icr.ac.uk/cansar/publications/druggable_network_neighbourhoods/</a>.</p></div

SiGNet simulates node responses to signaling interactions of different strengths and types.

<p>(<b>A</b>) Schematic showing the structure of a small network where node A weakly inhibits node B, inhibits node C, strongly inhibits node D, weakly activates node E, activates node F and strongly activates node G. (<b>B</b>) Graph showing the signaling output from each of the nodes shown in (A) over time, as predicted by SiGNet. Data are mean values calculated from ten ‘experimental replicates’ produced using SiGNet. (<b>C</b>) Schematic showing two networks, ABC and DEF, with identical network structures. The ABC network is perturbed by the experimental inhibition of A. (<b>D</b>) Graphs showing the signaling output of nodes A, B and C (upper) and nodes D, E and F (lower). The inhibition of node A is removed at time point 1.</p

Using the SiGNet plugin to simulate experimental data for a biological network.

<p><b>(A</b> to <b>D)</b> Screen snapshots showing how SiGNet is used. After a network is imported into or drawn out in Cytoscape (A), the user determines the number of ‘replicates’, ‘time points’, the amount of noise and whether nodes are inhibited or activated (B). SiGNet then generates simulated data for each replicate and time point. Here (C), nodes are colored according to their average value at time point 1. The data are exported and used for network inference (D). The accuracy of predicted edges can be benchmarked against the structure of the network used in A and scores of sensitivity, precision and recall can be calculated. A more detailed walkthrough example of how SiGNet can be used to aid benchmarking of inference techniques is available at signet.icr.ac.uk.</p

Cancer-drug targets are enriched for highly connected Graphlets.

<p>A) Interaction network highlighting the distribution of targets of approved cancer drugs (pink); targets of approved drugs from non-cancer therapeutic areas (blue); and targets predicted to be druggable by different druggability prediction methodologies(light and dark green). Druggable proteins are spread widely across the network while targets of current approved drugs tend to cluster into few areas. B) Cumulative fraction of all drug targets covered by communities. As indicated, a small number of communities cover the majority of drug targets. C) The network communities most enriched in drug targets are listed against the fold enrichment of the number of targets found in them (compared to what can be expected at random).</p

Enrichment and depletion of key parameters in drug targets over what can be expected at random from the interactome.

<p>A) Graphlets and their constituent isomorphism orbits. The graph shows the graphlets and orbits, ordered by descending size and complexity, most enriched in cancer-drug targets (light blue bars). These same graphlets and orbits are either slightly depleted or not differentiated from random in targets of non-cancer drugs (dark blue). The gray line represents graphlets size and complexity (high-to-low). B) The distribution of detected community sizes and the enrichment or depletion of cancer drug targets (light blule) versus targets of drugs used to treat other diseases (dark blue). C) Box plots showing distinction of degree and google page rank; as well as the vertex modularity which distinguishes inter- versus intra-community communication of nodes. Further parameters are shown in the Supporting Information.</p

Network profiles and interactions between targets of drug combinations.

<p>A) Radar plots showing representative network property profiles of targets of drug combination. MEK and BRAF network property profiles are more similar to one another than the network profiles of CDKs and HMGCR. This may be related to the long-term effectiveness of the combinations of drugs targeting these proteins. B) Interactions between proteins targeted by drug combination showing high level of connectivity between targets such as EGFR, BRAF and MEK. The dotted edge indicates that no direct interaction takes place between HMGCR and the other proteins in the network.</p

Simulating the effect of experimental perturbation on a real biological signaling network in SiGNet.

<p>SiGNet was used to simulate the effect of EGF treatment on EGFR and its downstream proteins, and the simulated data tested against published experimental data [<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0177701#pone.0177701.ref032" target="_blank">32</a>]. (<b>A</b>) Schematic showing the network structure, which was based on interactions reported by Blagoev et al. The network was drawn in Cytoscape and used as an input for SiGNet. (<b>B</b>) Data simulated for the network in (A) using SiGNet, with and without the optional decay function. Data shown are mean values calculated from ten ‘experimental replicates’. Pearson correlations between simulated and real data are shown. Additional simulations of this network incorporating simple feedback mechanisms are illustrated in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0177701#pone.0177701.s002" target="_blank">S2</a>, <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0177701#pone.0177701.s003" target="_blank">S3</a> and <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0177701#pone.0177701.s004" target="_blank">S4</a> Figs.</p

Comparing SiGNet to tools that simulate transcriptional network datasets.

<p>Comparing SiGNet to tools that simulate transcriptional network datasets.</p

Ligand efficiency indices for an effective mapping of chemico-biological space: the concept of an atlas-like representation

We propose a numerical framework that permits an effective Atlas-like representation of Chemico-Biological Space (CBS) based on a series of Cartesian planes mapping the ligands with the corresponding targets connected by an affinity parameter (Ki or related). The numerical framework is derived from the concept of ligand efficiency indices (LEIs), which provide a natural coordinate system combining the potency toward the target (biological space) with the physicochemical properties of the ligand (chemical space). This framework facilitates navigation in the multidimensional drug-discovery space using map-like representations based on pairs of combined variables related to the efficiency of the ligands per Dalton (MW or number of non-hydrogen atoms) and per unit of polar surface area (or number of polar atoms)