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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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Proceedings ArticleDOI

The scoring function based on amino acid network

TL;DR: It is found that the Sn scorning function has a better performance than the RP on some items and is proposed as a new docking scoring function Sn for 42 systems' bound docking results.
Proceedings ArticleDOI

JProNet: Systematic Network Analysis of Topological Properties of Protein 3D Structures

TL;DR: JProNet is a Java-based network tool for systematic analysis of structure-based topological properties of proteins that incorporates new developments in network theory such as the scale-free and the small-world properties.
Journal ArticleDOI

In Silico Study of Natural Xanthones as Potential Inhibitors of Alpha-Glucosidase and Alpha-Amylase

TL;DR: This study performed molecular docking simulations on all 515 natural xanthones with alpha-glucosidase and alpha-amylase as the protein targets and revealed prenylated xanthone have a good potential as anti-type 2 diabetes mellitus agents through in silico method.
Proceedings ArticleDOI

Identifying the Nucleus Residues in Proteins by Network Approach

Haiyan Li
TL;DR: The protein structure is modeled as a network with the amino acid residues the vertices and contacts between them the edges and the nucleus residues of GB1 and CI2 are identified by the analysis of the network parameter: betweenness of transition states.
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.
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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.
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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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