scholarly journals Entropy based distance cutoff for protein internal contact networks

2021 ◽  
Author(s):  
Marcin Sobieraj ◽  
Piotr Setny
Keyword(s):  
Contact Networks ◽  
Internal Contact ◽  
Distance Cutoff

scholarly journals Entropy based distance cutoff for protein internal contact networks

2021 ◽  
Author(s):  
Marcin Sobieraj ◽  
Piotr Setny
Keyword(s):  
Molecular Dynamics ◽  
Protein Structure ◽  
Mutual Information ◽  
Internal Communication ◽  
Objective Criterion ◽  
Contact Networks ◽  
Contact Formation ◽  
Distance Cutoff ◽  
Probability Threshold ◽  
Cutoff Distance

Protein structure networks (PSNs) have long been used to provide a coarse yet meaningful representation of protein structure, dynamics, and internal communication pathways. An important question is what criteria should be applied to construct the network so that to include relevant interresidue contacts while avoiding unnecessary connections. To address this issue we systematically considered varying residue distance cutoff length and the probability threshold for contact formation to construct PSNs based on atomistic molecular dynamics in order to assess the amount of mutual information within the resulting representations. We found that the minimum in mutual information is universally achieved at the cutoff length of 5 Å, irrespective of the applied contact formation probability threshold in all considered, distinct proteins. Assuming that the optimal PSNs should be characterised by the least amount of redundancy, which corresponds to the minimum in mutual information, this finding suggests an objective criterion for cutoff distance and supports the existing preference towards its customary selection around 5 Å length, typically based to date on heuristic criteria.

The Role of Contact Networks in Reconsidering Urban Systems Hierarchies

Netcom ◽  
2000 ◽  
Vol 14 (3) ◽  
pp. 253-266
Author(s):  
Maria Giaoutzi ◽  
Anastasia Stratigea
Keyword(s):  
Urban Systems ◽  
Contact Networks

scholarly journals Identification of effective spreaders in contact networks using dynamical influence

2021 ◽  
Vol 6 (1) ◽  
Author(s):  
Ruaridh A. Clark ◽  
Malcolm Macdonald
Keyword(s):  
Disease Transmission ◽  
Average Rate ◽  
Laplacian Matrix ◽  
Disease Spread ◽  
Eigenvector Centrality ◽  
Contact Networks ◽  
Human Contact ◽  
Spread Dynamics ◽  
Definition Of ◽  
High Degree

AbstractContact networks provide insights on disease spread due to the duration of close proximity interactions. For systems governed by consensus dynamics, network structure is key to optimising the spread of information. For disease spread over contact networks, the structure would be expected to be similarly influential. However, metrics that are essentially agnostic to the network’s structure, such as weighted degree (strength) centrality and its variants, perform near-optimally in selecting effective spreaders. These degree-based metrics outperform eigenvector centrality, despite disease spread over a network being a random walk process. This paper improves eigenvector-based spreader selection by introducing the non-linear relationship between contact time and the probability of disease transmission into the assessment of network dynamics. This approximation of disease spread dynamics is achieved by altering the Laplacian matrix, which in turn highlights why nodes with a high degree are such influential disease spreaders. From this approach, a trichotomy emerges on the definition of an effective spreader where, for susceptible-infected simulations, eigenvector-based selections can either optimise the initial rate of infection, the average rate of infection, or produce the fastest time to full infection of the network. Simulated and real-world human contact networks are examined, with insights also drawn on the effective adaptation of ant colony contact networks to reduce pathogen spread and protect the queen ant.

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