Uncovering allosteric pathways in caspase-1 with a multiscale community detection method and random-walk network analysis
نویسنده
چکیده
Our network is constructed by assigning edges between atoms which interact covalently and non-covalently. Each edge is weighted with weight given by the strength of the interaction between the two atoms it joins. Covalent bond strengths are obtained from tables assuming standard bond lengths. We include three types of non-covalent interactions: hydrophic interactions, hydrogen bonds, and electrostatic interactions.
منابع مشابه
Uncovering allosteric pathways in caspase-1 using Markov transient analysis and multiscale community detection.
Allosteric regulation at distant sites is central to many cellular processes. In particular, allosteric sites in proteins are major targets to increase the range and selectivity of new drugs, and there is a need for methods capable of identifying intra-molecular signalling pathways leading to allosteric effects. Here, we use an atomistic graph-theoretical approach that exploits Markov transient...
متن کاملتشخیص اجتماعات ترکیبی در شبکههای اجتماعی
One of the great challenges in Social Network Analysis (SNA) is community detection. Community is a group of vertices which have high intra connections and sparse inter connections. Community detection or Clustering reveals community structure of social networks and hidden relationships among their constituents. By considering the increase of datasets related to social networks, we need scalabl...
متن کاملSampling from social networks’s graph based on topological properties and bee colony algorithm
In recent years, the sampling problem in massive graphs of social networks has attracted much attention for fast analyzing a small and good sample instead of a huge network. Many algorithms have been proposed for sampling of social network’ graph. The purpose of these algorithms is to create a sample that is approximately similar to the original network’s graph in terms of properties such as de...
متن کاملAn Improved Random Walk Based Community Detection Algorithm
Community detection is an important issue in social network analysis, which aims at finding potential community structures such that the internal nodes of a community have higher closeness than external nodes. Taking into account node attribute information, this paper presents an improved community detection algorithm based on random walk. Based on the basic understanding that people getting to...
متن کاملDetecting Overlapping Communities in Social Networks using Deep Learning
In network analysis, a community is typically considered of as a group of nodes with a great density of edges among themselves and a low density of edges relative to other network parts. Detecting a community structure is important in any network analysis task, especially for revealing patterns between specified nodes. There is a variety of approaches presented in the literature for overlapping...
متن کامل