Modular decomposition of protein structure using community detection
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Authors
Grant, WP
Ahnert, SE
Estrada, E
Publication Date
2019Journal Title
Journal of Complex Networks
ISSN
2051-1310
Publisher
Oxford University Press (OUP)
Volume
7
Issue
1
Pages
101-113
Type
Article
This Version
VoR
Metadata
Show full item recordCitation
Grant, W., Ahnert, S., & Estrada, E. (2019). Modular decomposition of protein structure using community detection. Journal of Complex Networks, 7 (1), 101-113. https://doi.org/10.1093/comnet/cny014
Abstract
As the number of solved protein structures increases, the opportunities for
meta-analysis of this dataset increase too. Protein structures are known to be
formed of domains; structural and functional subunits that are often repeated
across sets of proteins. These domains generally form compact, globular
regions, and are therefore often easily identifiable by inspection, yet the
problem of automatically fragmenting the protein into these compact
substructures remains computationally challenging. Existing domain
classification methods focus on finding subregions of protein structure that
are conserved, rather than finding a decomposition which spans the full protein
structure. However, such a decomposition would find ready application in
coarse-graining molecular dynamics, analysing the protein's topology, in de
novo protein design and in fitting electron microscopy maps. Here, we present a
tool for performing this modular decomposition using the Infomap community
detection algorithm. The protein structure is abstracted into a network in
which its amino acids are the nodes, and where the edges are generated using a
simple proximity test. Infomap can then be used to identify highly
intra-connected regions of the protein. We perform this decomposition
systematically across 4000 distinct protein structures, taken from the Protein
Data Bank. The decomposition obtained correlates well with existing PFAM
sequence classifications, but has the advantage of spanning the full protein,
with the potential for novel domains. The coarse-grained network formed by the
communities can also be used as a proxy for protein topology at the
single-chain level; we demonstrate that grouping these proteins by their
coarse-grained network results in a functionally significant classification.
Sponsorship
EPSRC (1644501)
The Royal Society (uf080037)
Gatsby Charitable Foundation (GAT3395/CCD)
Engineering and Physical Sciences Research Council (EP/L015552/1)
Identifiers
External DOI: https://doi.org/10.1093/comnet/cny014
This record's URL: https://www.repository.cam.ac.uk/handle/1810/292541
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