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Journal ArticleDOI

Network analysis of protein structures identifies functional residues.

TLDR
This work transformed protein structures into residue interaction graphs (RIGs), where amino acid residues are graph nodes and their interactions with each other are the graph edges, and found that active site, ligand-binding and evolutionary conserved residues, typically have high closeness values.
About
This article is published in Journal of Molecular Biology.The article was published on 2004-12-03. It has received 463 citations till now. The article focuses on the topics: Protein structure & Active site.

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Citations
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Dissertation

From structure to function in proteins : a computational study

Tracey Bray
TL;DR: This chapter discusses protein function in the absence of Sequence or Structural Similarity, and the role of enzymes in this role and structure by EC Class.
Journal ArticleDOI

BetaSearch: a new method for querying β-residue motifs

TL;DR: A new method for querying β-residue motifs, called BetaSearch, which leverages the natural planar constraints of β-sheets by indexing them as 2D matrices, thus avoiding much of the computational complexities involved with structural and graph querying.
Proceedings ArticleDOI

Prediction of Protein Catalytic Residues by Local Structural Rigidity

TL;DR: A novel method to calculate the local structural rigidity (LSR) of protein shows that catalytic residues have distinct structural properties and are shown to be extremely rigid based on the calculation of LSR.
Proceedings ArticleDOI

Network Properties of Protein Structures at Three Different Length Scales

Haiyan Li, +1 more
TL;DR: The results show that Protein Contact Networks and Short-range Interaction networks regardless of their structural classes exhibit the ‘small-world’ property and Long-rangeInteraction networks indicate ‘scale-free’ behavior except all β protein networks.
Journal ArticleDOI

Learning Heterogeneous Network Embedding From Text and Links

TL;DR: This paper proposes a novel approach to learn node embedding for heterogeneous networks through a joint learning framework of both network links and text associated with nodes, which outperforms the current state-of-the-art method on a link prediction task in a heterogeneous network data set.
References
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Journal ArticleDOI

Collective dynamics of small-world networks

TL;DR: Simple models of networks that can be tuned through this middle ground: regular networks ‘rewired’ to introduce increasing amounts of disorder are explored, finding that these systems can be highly clustered, like regular lattices, yet have small characteristic path lengths, like random graphs.
Journal ArticleDOI

The Protein Data Bank

TL;DR: The goals of the PDB are described, the systems in place for data deposition and access, how to obtain further information and plans for the future development of the resource are described.
Journal ArticleDOI

Emergence of Scaling in Random Networks

TL;DR: A model based on these two ingredients reproduces the observed stationary scale-free distributions, which indicates that the development of large networks is governed by robust self-organizing phenomena that go beyond the particulars of the individual systems.
Journal ArticleDOI

Statistical mechanics of complex networks

TL;DR: In this paper, a simple model based on the power-law degree distribution of real networks was proposed, which was able to reproduce the power law degree distribution in real networks and to capture the evolution of networks, not just their static topology.
Journal ArticleDOI

Centrality in social networks conceptual clarification

TL;DR: In this article, three distinct intuitive notions of centrality are uncovered and existing measures are refined to embody these conceptions, and the implications of these measures for the experimental study of small groups are examined.
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