title: Learning To Fold Proteins Using Energy Landscape Theory.
abstract: This review is a tutorial for scientists interested in the problem of protein structure prediction, particularly those interested in using coarse-grained molecular dynamics models that are optimized using lessons learned from the energy landscape theory of protein folding. We also present a review of the results of the AMH/AMC/AMW/AWSEM family of coarse-grained molecular dynamics protein folding models to illustrate the points covered in the first part of the article. Accurate coarse-grained structure prediction models can be used to investigate a wide range of conceptual and mechanistic issues outside of protein structure prediction; specifically, the paper concludes by reviewing how AWSEM has in recent years been able to elucidate questions related to the unusual kinetic behavior of artificially designed proteins, multidomain protein misfolding, and the initial stages of protein aggregation.
authors.affiliation: Department of Physics, Rice University, Houston, TX 77005, USA ; Center for Theoretical Biological Physics, Rice University, Houston, TX 77005, USA.
authors.forename: N P
authors.initials: NP
authors.lastname: Schafer
authors.affiliation: Department of Chemistry, Rice University, Houston, TX 77005, USA ; Center for Theoretical Biological Physics, Rice University, Houston, TX 77005, USA.
authors.forename: B L
authors.initials: BL
authors.lastname: Kim
authors.affiliation: Department of Chemistry, Rice University, Houston, TX 77005, USA ; Center for Theoretical Biological Physics, Rice University, Houston, TX 77005, USA.
authors.forename: W
authors.initials: W
authors.lastname: Zheng
authors.affiliation: Department of Physics, Rice University, Houston, TX 77005, USA ; Department of Chemistry, Rice University, Houston, TX 77005, USA ; Center for Theoretical Biological Physics, Rice University, Houston, TX 77005, USA.
authors.forename: P G
authors.initials: PG
authors.lastname: Wolynes