title: Folding funnels: the key to robust protein structure prediction.
abstract: Natural proteins fold because their free energy landscapes are funneled to their native states. The degree to which a model energy function for protein structure prediction can avoid the multiple minima problem and reliably yield at least low-resolution predictions is also dependent on the topography of the energy landscape. We show that the degree of funneling can be quantitatively expressed in terms of a few averaged properties of the landscape. This allows us to optimize simplified energy functions for protein structure prediction even in the absence of homology information. Here we outline the optimization procedure in the context of associative memory energy functions originally introduced for tertiary structure recognition and demonstrate that even partially funneled landscapes lead to qualitatively correct, low-resolution predictions.
authors.affiliation: Center for Biophysics and Computational Biology, University of Illinois, Urbana 61801, USA.
authors.forename: Corey
authors.initials: C
authors.lastname: Hardin
authors.forename: Michael P
authors.initials: MP
authors.lastname: Eastwood
authors.forename: Michael
authors.initials: M
authors.lastname: Prentiss
authors.forename: Z
authors.initials: Z
authors.lastname: Luthey-Schulten
authors.forename: Peter G
authors.initials: PG
authors.lastname: Wolynes
mesh_terms: D019295:Computational Biology / Q000379:methods; D008956:Models, Chemical; D011487:Protein Conformation; D017510:Protein Folding; D017434:Protein Structure, Tertiary; D011506:Proteins / Q000737:chemistry*
chemical_list: D011506:Proteins