title: Network measures for protein folding state discrimination.
abstract: Proteins fold using a two-state or multi-state kinetic mechanisms, but up to now there is not a first-principle model to explain this different behavior. We exploit the network properties of protein structures by introducing novel observables to address the problem of classifying the different types of folding kinetics. These observables display a plain physical meaning, in terms of vibrational modes, possible configurations compatible with the native protein structure, and folding cooperativity. The relevance of these observables is supported by a classification performance up to 90%, even with simple classifiers such as discriminant analysis.
authors.affiliation: Department of Physics and Astronomy and INFN Sez. Bologna, University of Bologna, Viale B. Pichat 6/2 40127 Bologna, Italy.
authors.forename: Giulia
authors.initials: G
authors.lastname: Menichetti
authors.affiliation: Department of Comparative Biomedicine and Food Science, University of Padova, Viale dell'Universitá 16 35020 Legnaro, Italy.
authors.forename: Piero
authors.initials: P
authors.lastname: Fariselli
authors.affiliation: Department of Physics and Astronomy and INFN Sez. Bologna, University of Bologna, Viale B. Pichat 6/2 40127 Bologna, Italy.
authors.forename: Daniel
authors.initials: D
authors.lastname: Remondini
mesh_terms: D000465:Algorithms; D007700:Kinetics; D008962:Models, Theoretical; D017510:Protein Folding; D011506:Proteins / Q000737:chemistry*
chemical_list: D011506:Proteins