Journal ArticleDOI
Network analysis of protein structures identifies functional residues.
Gil Amitai,Arye Shemesh,Einat Sitbon,Maxim Shklar,Dvir Netanely,Ilya Venger,Shmuel Pietrokovski +6 more
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.read more
Citations
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Journal ArticleDOI
Graph neural network approaches for drug-target interactions.
Zehong Zhang,Lifan Claire Chen,Feisheng Zhong,Dingyan Wang,Jiaxin Jiang,Sulin Zhang,Hualiang Jiang,Mingyue Zheng,Xutong Li +8 more
TL;DR: In this paper , the authors provide a brief overview of deep neural networks used in DTI models and summarize the database required for DTI prediction, followed by a comprehensive introduction of applications of graph neural networks for drug-target interaction prediction.
Journal ArticleDOI
Enhanced performance in prediction of protein active sites with THEMATICS and support vector machines.
TL;DR: An extension of the method to predict non‐ionizable catalytic residues is presented, and THEMATICS–SVM predicts a local network of ionizable residues with strong interactions between protonation events; this appears to be a special feature of enzyme active sites.
Journal ArticleDOI
Graph theoretic properties of networks formed by the Delaunay tessellation of protein structures.
Todd J. Taylor,Iosif I. Vaisman +1 more
TL;DR: Analysis of Delaunay tessellation of real and simplified model protein structures indicates that protein contact networks have small world character, but technically are not small world networks.
Journal ArticleDOI
Mapping the mutual information network of enzymatic families in the protein structure to unveil functional features.
TL;DR: This work has combined mutual information and structural data to describe the amino acid networks within a protein and their interactions and found that clusters of co-evolving residues related to the catalytic site of an enzyme have distinguishable topological properties in the network.
Journal ArticleDOI
Revealing unique properties of the ribosome using a network based analysis
TL;DR: It is demonstrated that mutations that strongly affect ribosome function and assembly can be distinguished from mild mutations based on their network properties, and a global topology perspective is suggested to characterize functional sites and to reveal the unique properties of the ribosomes.
References
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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
Helen M. Berman,John D. Westbrook,Zukang Feng,Gary L. Gilliland,Talapady N. Bhat,Helge Weissig,Ilya N. Shindyalov,Philip E. Bourne +7 more
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.
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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.
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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.