Using Sequence Similarity Networks for Visualization of Relationships Across Diverse Protein Superfamilies

By Holly J. Atkinson, J. H. Morris, T. Ferrin, Patricia C. Babbitt. PLoS ONE, 2009

438 citations recorded by the source corpus.

The dramatic increase in heterogeneous types of biological data—in particular, the abundance of new protein sequences—requires fast and user-friendly methods for organizing this information in a way that enables functional inference. The most widely used strategy to link sequence or structure to function, homology-based function prediction, relies on the fundamental assumption that sequence or structural similarity implies functional similarity. New tools that extend this approach are still urgently needed to associate sequence data with biological information in ways that accommodate the real complexity of the problem, while being accessible to experimental as well as computational biologists. To address this, we have examined the application of sequence similarity networks for visualizing functional trends across protein superfamilies from the context of sequence similarity. Using three large groups of homologous proteins of varying types of structural and functional diversity—GPCRs and kinases from humans, and the crotonase superfamily of enzymes—we show that overlaying networks with orthogonal information is a powerful approach for observing functional themes and revealing outliers. In comparison to other primary methods, networks provide both a good representation of group-wise sequence similarity relationships and a strong visual and quantitative correlation with phylogenetic trees, while enabling analysis and visualization of much larger sets of sequences than trees or multiple sequence alignments can easily accommodate. We also define important limitations and caveats in the application of these networks. As a broadly accessible and effective tool for the exploration of protein superfamilies, sequence similarity networks show great potential for generating testable hypotheses about protein structure-function relationships.

Subfields: Molecular Biology

View Paper: https://www.semanticscholar.org/paper/b64e6f51fd099f84623eb01c7a9c4fea5a6796ab

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In Molecular Biology

Citations in the corpus

Enzyme Function Initiative-Enzyme Similarity Tool (EFI-EST): A web tool for generating protein sequence similarity networks (2015, positive): “Babbitt and coworkers described the use of protein sequence similarity networks (SSNs) as an “easy to compute” alternate method for assessing sequence relationships within enzyme families [12].”
Comprehensive phylogenetic analysis of the ribonucleotide reductase family reveals an ancestral clade (2022, neutral): “Unlike sequence analyses, such as sequence similarity network (SSN) analysis ( Atkinson et al., 2009 ), which provide insight exclusively on the global similarities shared between extant sequences, phylogenetic inference delineates the evolutionary history of related sequences utilizing complex…”
BIOLOGICAL NETWORK EXPLORATION WITH CYTOSCAPE 3 (2014, methodology): “…networks, but Cytoscape has been used to explore structural networks (Morris et al. , 2007; Doncheva et al. , 2011), protein-protein similarity networks (Atkinson et al. , 2009), and biological pathways (see the Wikipathways App, the CyKEGG Parser App, and the MetScape App (Gao et al. , 2010).”
Large-Scale Determination of Sequence, Structure, and Function Relationships in Cytosolic Glutathione Transferases across the Biosphere (2014, background): “Descriptions of the creation and validation of thresholded protein similarity networks, along with some of their uses and limitations, have been reported elsewhere [28,29].”
Annotation Error in Public Databases: Misannotation of Molecular Function in Enzyme Superfamilies (2009, methodology): “A protein similarity network [60] was created from the BLAST results using the software Cytoscape [59].”