Visualizing Human Protein‐Protein Interactions and Subcellular Localizations on Cell Images Through CellMap

Abstract

Visualizing protein data remains a challenging and stimulating task. Useful and intuitive visualization tools may help advance biomolecular and medical research; unintuitive tools may bar important breakthroughs. This protocol describes two use cases for the CellMap (http://cellmap.protein.properties) web tool. The tool allows researchers to visualize human protein‐protein interaction data constrained by protein subcellular localizations. In the simplest form, proteins are visualized on cell images that also show protein‐protein interactions (PPIs) through lines (edges) connecting the proteins across the compartments. At a glance, this simultaneously highlights spatial constraints that proteins are subject to in their physical environment and visualizes PPIs against these localizations. Visualizing two realities helps in decluttering the protein interaction visualization from “hairball” phenomena that arise when single proteins or groups thereof interact with hundreds of partners. © 2019 The Authors. Basic Protocol 1: Visualizing proteins and their interactions on cell images Basic Protocol 2: Displaying all interaction partners for a protein

LambdaPP: Fast and accessible protein-specific phenotype predictions

“…An interactive connection between residue-level features and 3D protein structure allows to map the predictions onto the 3D visualization while displaying additional information in tooltips. The protein-level section visualizes subcellular location through colored images (19; 64) and GO-term predictions as lists of predicted GO-terms along with scores reflecting reliability (RI) and links to the reference protein used for the annotation transfer (38). The Single Amino acid Variant (SAV) effect section features the predictions of how much point mutations (SAVs) negatively affect molecular function.…”

LambdaPP: Fast and accessible protein‐specific phenotype predictions

“…An interactive connection between residue‐level features and 3D structure maps predictions onto 3D visualization while displaying additional information in tooltips. The protein‐level section visualizes subcellular location through colored images (Dallago et al, 2020 ; Stärk et al, 2021 ) and GO‐term predictions as lists of predicted GO‐terms along with scores reflecting reliability (RI) and links to the reference protein used for the annotation transfer (Littmann, Heinzinger, Dallago, Olenyi, et al, 2021 ). The Single Amino acid Variant (SAV) effect section features the predictions of how much point mutations (SAVs) negatively affect molecular function.…”

“…AlphaFold2's 3D prediction suggests it folds into a new superfamily (Bordin et al, 2022 ). The predicted pLDDT of the 3D structure (very low 1–29, low 30–33, very low 34–45, low 46–48) matches partially with UniProtKB disorder annotations (Abriata et al, 2018 ; Ahdritz et al, 2021 ; Alexander‐Brett & Kober, 2015 ; Alley et al, 2019 ; Almagro Armenteros et al, 2017 ; Baek & Baker, 2022 ; Ben Chorin et al, 2020 ; Bengio et al, 2013 ; Bepler & Berger, 2019 , 2021 ; Berezin et al, 2004 ; Bernhofer et al, 2021 ; Bernhofer & Rost, 2022 ; Bileschi et al, 2022 ; Bordin et al, 2022 ; Buchfink et al, 2021 ; Cardim‐Pires et al, 2021 ; Chowdhary, 2020 ; Cid et al, 2016 ; Dallago et al, 2020 , 2021 ; Dass et al, 2020 ; El‐Mabrouk & Slonim, 2020 ; Elnaggar et al, 2021 ; Heinzinger et al, 2019 , 2022 ; Henikoff & Henikoff, 1992 ; Hie et al, 2022 ; Høie et al, 2022 ) and LambdaPP‐included disorder predictions (residues 1–39; N‐terminal region looping to the lower left (Figure 3b )). This region is also predicted as poorly conserved.…”

Overview of methods for characterization and visualization of a protein–protein interaction network in a multi-omics integration context

“…Visualization tools are evaluated by four criteria: compatibility (available on which OS (operating systems): Windows, Mac Os, and Linux, analytic functions (presence of functions measuring the topological properties of the network, weak interactions of external data, etc. ), visualizations (graph layout, dynamics, and parallel implementation), and the extensibility of the tool (addition of plugins, type of input, and output file) forming distinct classes ( Sanz-Pamplona et al, 2012 ; Agapito, Guzzi and Cannataro, 2013 ; Dallago et al, 2020 ). In the context of biological network analysis and in particular protein networks, one of the essential criteria is dynamic visualization tools ( Xia, Benner and Hancock, 2014 ; Zhou and Xia, 2018 ).…”