title: Simple but predictive protein models.
abstract: The traditional approach to computational biophysics studies of molecular systems is brute force molecular dynamics simulations under the conditions of interest. The disadvantages of this approach are that the time and length scales that are accessible to computer simulations often do not reach biologically relevant scales. An alternative approach, which we call intuitive modeling, is hypothesis-driven and based on tailoring simplified protein models to the systems of interest. Using intuitive modeling, the length and time scales that can be achieved using simplified protein models exceed those of traditional molecular-dynamic simulations. Here, we describe several recent studies that signify the predictive power of simplified protein models within the intuitive-modeling approach.
authors.affiliation: Department of Biochemistry and Biophysics, School of Medicine, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA.
authors.forename: Feng
authors.initials: F
authors.lastname: Ding
authors.forename: Nikolay V
authors.initials: NV
authors.lastname: Dokholyan
mesh_terms: D003198:Computer Simulation; D008956:Models, Chemical; D008958:Models, Molecular; D017510:Protein Folding; D011506:Proteins / Q000737:chemistry*; D013482:Superoxide Dismutase / Q000737:chemistry; D018909:src Homology Domains
chemical_list: D011506:Proteins; D013482:Superoxide Dismutase