Integrating Protein Localization with Automated Signaling Pathway Reconstruction

Abstract

Understanding cellular responses via signal transduction is a core focus in systems biology. Tools to automatically reconstruct signaling pathways from protein-protein interactions (PPIs) can help biologists generate testable hypotheses about signaling. However, automatic reconstruction of signaling pathways suffers from many interactions with the same confidence score leading to many equally good candidates. Further, some reconstructions are biologically misleading due to ignoring protein localization information. We propose LocPL, a method to improve the automatic reconstruction of signaling pathways from PPIs by incorporating information about protein localization in the reconstructions. The method relies on a dynamic program to ensure that the proteins in a reconstruction are localized in cellular compartments that are consistent with signal transduction from the membrane to the nucleus. LocPL and existing reconstruction algorithms are applied to two PPI networks and assessed using both global and local definitions of accuracy. LocPL produces more accurate and biologically meaningful reconstructions on a versatile set of signaling pathways. LocPL is a powerful tool to automatically reconstruct signaling pathways from PPIs that leverages cellular localization information about proteins. The underlying dynamic program and signaling model are flexible enough to study cellular signaling under different settings of signaling flow across the cellular compartments.

Automating parameter selection to avoid implausible biological pathway models

“…Our distance metric focuses only on topology and does not include any information about the biological context. ResponseNet (Basha et al, 2019) and PathLinker (Youssef et al, 2018) extensions consider tissue-specificity and protein localization context, respectively. A possible extension of pathway parameter advising would be to account for this information, such as adding a penalty for interactions that occur in different tissues or cellular compartments.…”

Augmenting Signaling Pathway Reconstructions

“…Protein-protein interactions can be modeled as graphs where the nodes stand in for proteins and the edges represent a (possibly directed) interaction between two proteins. These graphs, called interactomes, can be built from experimental dataset repositories and can be weighted by confidence in the interaction, functional relationship, or tissue [16,[18][19][20][21][22][23][24]. Interactomes provide a background set of plausible interactions for consideration in a pathway of interest, and network-based methods generally have been successful with amplifying the signal of interacting proteins [25].…”

“…Interactome: We use PLNET 2 , a weighted, directed interactome constructed from both molecular interaction data and signaling pathway databases (including the database used for ground truth pathways) [16]. PLNET 2 is weighted using an evidence-based Bayesian method introduced by RN [9] that assigns a high confidence to edges supported by experimental methods that successfully predict signaling interactions [16]. PLNET 2 contains 17, 168 nodes (UniProtKB identifiers [27]) and 612, 516 directed edges, including 286, 520 physical interactions that are converted to bidirected edges.…”

“…Many of these approaches generate new predictions by integrating protein-protein interaction data with gene [6][7][8][9][10][11] or protein [12][13][14][15] expression. Other approaches work to remove biologically implausible predictions [16,17]. Here, we will focus on methods that use protein-protein interaction data to predict new proteins and molecular interactions involved in canonical signaling pathways.…”

Augmenting Signaling Pathway Reconstructions

“…Many of these approaches generate new predictions by integrating protein-protein interaction data with gene [7,12,21,21,29,30,33] or protein [4,15,19,24] expression. Other approaches work to remove biologically implausible predictions [20,34]. Here, we will focus on methods that use protein-protein interaction data to predict new proteins and molecular interactions involved in canonical signaling pathways.…”