The protein folding network.

By Francesco Rao, Amedeo Caflisch. Journal of Molecular Biology, 2004

329 citations recorded by the source corpus.

The conformation space of a 20 residue antiparallel beta-sheet peptide, sampled by molecular dynamics simulations, is mapped to a network. Snapshots saved along the trajectory are grouped according to secondary structure into nodes of the network and the transitions between them are links. The conformation space network describes the significant free energy minima and their dynamic connectivity without requiring arbitrarily chosen reaction coordinates. As previously found for the Internet and the World-Wide Web as well as for social and biological networks, the conformation space network is scale-free and contains highly connected hubs like the native state which is the most populated free energy basin. Furthermore, the native basin exhibits a hierarchical organization, which is not found for a random heteropolymer lacking a predominant free-energy minimum. The network topology is used to identify conformations in the folding transition state (TS) ensemble, and provides a basis for understanding the heterogeneity of the TS and denatured state ensemble as well as the existence of multiple pathways.

Subfields: Computational Theory and Mathematics, Molecular Biology

View Paper: https://www.semanticscholar.org/paper/f2a7ac4a723e1aa7de37c941bc7d21a750e8bacb

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Citations in the corpus

Markov models of molecular kinetics: generation and validation. (2011, background): “A natural approach towards modeling the kinetics of molecules is by first partitioning the conformation space into discrete states [27, 25, 28, 29, 30, 31, 32, 26, 33, 34, 35].”
Single-molecule force spectroscopy reveals signatures of glassy dynamics in the energy landscape of ubiquitin (2006, background): “Molecular dynamics simulations have shown that the connections in the network of the conformational space of a folding protein are scale-free and have a native-state basin with a hierarchical organization of free energie”
Identification of slow molecular order parameters for Markov model construction. (2013, background): “Kinetic models usually first partition the conformation space into discrete states [93, 61, 40, 33, 94, 14, 74, 56, 21, 80, 69].”
On the Approximation Quality of Markov State Models (2010, methodology): “This observation justifies the algorithmic strategy of constructing MSMs by finely partitioning the state space using clustering algorithms that has been employed by several researchers in the field [6, 32, 33].”
Analyzing and modeling real-world phenomena with complex networks: a survey of applications (2007, methodology): “Is important obse rve that the reaction graphs were used in previous papers, as [304] to investigate the isomers of wa ter dimer and [305] to study interconnection of the the protein conformal space.”
Structure and dynamics of molecular networks: A novel paradigm of drug discovery: A comprehensive review (2012, background): “…representations of energy landscapes (or fitness landscapes) resulting in transition networks, and in the recurrence-based time series analysis resulting in correlation networks, cycle networks, recurrence networks or visibility graphs (Doye, 2002; Rao & Caflisch, 2004; Donner et al., 2011).”