Source-linked AI summary
Creative elements: network-based predictions of active centres in proteins, cellular and social networks
Peter Csermely
TL;DR
Existing protein active-centre prediction methods often rely on local structural or energy information or extensive evolutionary comparisons, motivating network-based analysis of global structure. The paper describes active-centre properties in residue networks and proposes extending the idea to other complex networks as creative elements that may help systems meet unexpected challenges.
Problem
Many existing prediction methods rely on local structural or energy information or extensive evolutionary comparisons, while global structural determinants may identify additional active-centre features.
Source: p. 1Method
The paper analyzes active centres through protein residue-network properties and proposes applying the network analogy to cells, social networks, and ecosystems.
Source: pp. 1, 2, 5Results
Protein active centres are reported to be central, often but not always hubs, and to provide unique neighborhood connections and integrate network communication.
Source: p. 4Takeaways & Limitations
The proposed creative elements may help complex systems survive unexpected challenges and are suggested as a way to consider active centres beyond proteins.
Source: pp. 5–6Centrality depends on protein conformation and can also identify allosteric sites or hinge elements; creative elements add noise that can become intolerable beyond a context-dependent threshold.
Source: pp. 3, 7
Abstract
from arXiv · showhide
Active centres and hot spots of proteins have a paramount importance in enzyme action, protein complex formation and drug design. Recently a number of publications successfully applied the analysis of residue networks to predict active centres in proteins. Most real-world networks show a number of properties, such as small-worldness or scale-free degree distribution, which are rather general features of networks, from molecules to society at large. Using analogy I propose that existing findings and methodology already enable us to detect active centres in cells, and can be expanded to social networks and ecosystems. Members of these active centres are termed here as creative elements of their respective networks, which may help them to survive unprecedented, novel challenges, and play a key role in the development, survival and evolvability of complex systems.
Glossary
The glossary distinguishes active centres in their traditional and network senses and defines the network terms used to describe elements, connections, structure, and dynamics.
Glossary
- Traditionally, an active centre is a catalytic protein segment; in this paper’s network sense, it is a cluster of amino acids with high centrality.Outside the Introduction, the paper uses the network sense.Source: Glossary · p. 10
- Centrality measures an element’s relative network importance, while degree counts its links and degree distribution distinguishes random, scale-free, and other networks.Source: Glossary · p. 10
- An elastic network model represents amino acids at alpha-carbon equilibrium positions as linked oscillators and calculates their most probable modes of oscillation.Source: Glossary · p. 10
- An element is a network building block, also called a vertex, node, site, or actor, and may itself be a complex network.Source: Glossary · p. 10
- Hubs are highly connected elements, hot spots have exceptionally high binding free energy, and fractals repeat self-similar forms across scales.Hubs usually have more than 1% of total interactions.Source: Glossary · p. 10
- Modules are relatively isolated groups with denser internal contacts and functional links; hierarchical networks organize modules as elements at successively higher levels.Source: Glossary · p. 10
- Network topology specifies links; scale-free networks have power-law degree distributions, while small worlds have logarithmically growing average reach.Source: Glossary · p. 10