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Comparison of topological clustering within protein networks using edge metrics that evaluate full sequence, full structure, and active site microenvironment similarity.

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Comparison of topological clustering within protein networks using edge metrics that evaluate full sequence, full structure, and active site microenvironment similarity.

Content Provider	World Health Organization (WHO)-Global Index Medicus
Author	Kumar, Kiran Fetrow, Jacquelyn S. Leuthaeuser, Janelle B. Knutson, Stacy T. Babbitt, Patricia C.
Description	Author Affiliation: Leuthaeuser JB ( Department of Molecular Genetics and Genomics, Wake Forest University, Winston-Salem, North Carolina, 27106.); Knutson ST ( Departments of Computer Science and Physics, Wake Forest University, Winston-Salem, North Carolina, 27106.); Kumar K ( Departments of Computer Science and Physics, Wake Forest University, Winston-Salem, North Carolina, 27106.); Babbitt PC ( Department of Bioengineering and Therapeutic Sciences, Institute for Quantitative Biosciences University of California San Francisco, San Francisco, California, 94158.); Fetrow JS ( Department of Pharmaceutical Chemistry, Institute for Quantitative Biosciences University of California San Francisco, San Francisco, California, 94158.)
Abstract	The development of accurate protein function annotation methods has emerged as a major unsolved biological problem. Protein similarity networks, one approach to function annotation via annotation transfer, group proteins into similarity-based clusters. An underlying assumption is that the edge metric used to identify such clusters correlates with functional information. In this contribution, this assumption is evaluated by observing topologies in similarity networks using three different edge metrics: sequence (BLAST), structure (TM-Align), and active site similarity (active site profiling, implemented in DASP). Network topologies for four well-studied protein superfamilies (enolase, peroxiredoxin (Prx), glutathione transferase (GST), and crotonase) were compared with curated functional hierarchies and structure. As expected, network topology differs, depending on edge metric; comparison of topologies provides valuable information on structure/function relationships. Subnetworks based on active site similarity correlate with known functional hierarchies at a single edge threshold more often than sequence- or structure-based networks. Sequence- and structure-based networks are useful for identifying sequence and domain similarities and differences; therefore, it is important to consider the clustering goal before deciding appropriate edge metric. Further, conserved active site residues identified in enolase and GST active site subnetworks correspond with published functionally important residues. Extension of this analysis yields predictions of functionally determinant residues for GST subgroups. These results support the hypothesis that active site similarity-based networks reveal clusters that share functional details and lay the foundation for capturing functionally relevant hierarchies using an approach that is both automatable and can deliver greater precision in function annotation than current similarity-based methods.
ISSN	09618368
e-ISSN	1469896X
DOI	10.1002/pro.2724
Journal	Protein Science
Issue Number	9
Volume Number	24
Language	English
Publisher	Wiley-Blackwell (on behalf of The Protein Society)
Publisher Date	2015-09-01
Publisher Place	United States
Access Restriction	Open
Subject Keyword	Molecular Sequence Annotation Proteins Chemistry Amino Acid Sequence Catalytic Domain Cellular Microenvironment Cluster Analysis Computational Biology Databases, Protein Molecular Sequence Data Protein Interaction Maps Structure-activity Relationship Comparative Study Research Support, N.i.h., Extramural Discipline Biochemistry
Content Type	Text
Resource Type	Article
Subject	Medicine Molecular Biology Biochemistry

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Comparison of topological clustering within protein networks using edge metrics that evaluate full sequence, full structure, and active site microenvironment similarity

Evolutionary information hidden in a single protein structure.

Relationships between functional subclasses and information contained in active-site and ligand-binding residues in diverse superfamilies.

Protein function annotation by local binding site surface similarity.

Structural divergence between the two subgroups of P5 ATPases

Functional evolution of PLP-dependent enzymes based on active-site structural similarities.

An Extended Active-site Motif Controls the Reactivity of the Thioredoxin $Fold^{*}$

Effective function annotation through catalytic residue conservation.

The crystal structure of the catalytic domain of the NF-κB inducing kinase reveals a narrow but flexible active site.

Comparison of topological clustering within protein networks using edge metrics that evaluate full sequence, full structure, and active site microenvironment similarity.

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Comparison of topological clustering within protein networks using edge metrics that evaluate full sequence, full structure, and active site microenvironment similarity

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