Premium
Comparison of topological clustering within protein networks using edge metrics that evaluate full sequence, full structure, and active site microenvironment similarity
Author(s) -
Leuthaeuser Janelle B.,
Knutson Stacy T.,
Kumar Kiran,
Babbitt Patricia C.,
Fetrow Jacquelyn S.
Publication year - 2015
Publication title -
protein science
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 3.353
H-Index - 175
eISSN - 1469-896X
pISSN - 0961-8368
DOI - 10.1002/pro.2724
Subject(s) - network topology , similarity (geometry) , computational biology , cluster analysis , annotation , metric (unit) , computer science , active site , function (biology) , sequence (biology) , data mining , biology , artificial intelligence , genetics , engineering , biochemistry , operations management , image (mathematics) , enzyme , operating system
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.