title: Funnel hunting in a rough terrain: learning and discriminating native energy funnels.
abstract: Protein folding and binding is commonly depicted as a search for the minimum energy conformation. Modeling of protein complex structures by RosettaDock often results in a set of low-energy conformations near the native structure. Ensembles of low-energy conformations can appear, however, in other regions, especially when backbone movements occur upon binding. What then characterizes the energy landscape near the correct orientation? We applied a machine learning algorithm to distinguish ensembles of low-energy conformations around the native conformation from other low-energy ensembles. The resulting classifier, FunHunt, identifies the native orientation in 50/52 protein complexes in a test set. The features used by FunHunt teach us about the nature of native interfaces. Remarkably, the energy decrease of trajectories toward near-native orientations is significantly larger than for other orientations. This provides a possible explanation for the stability of association in the native orientation.
authors.affiliation: Department of Molecular Genetics and Biotechnology, Hadassah Medical School, POB 12272, The Hebrew University, Jerusalem, 91120, Israel.
authors.forename: Nir
authors.initials: N
authors.lastname: London
authors.forename: Ora
authors.initials: O
authors.lastname: Schueler-Furman
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D008958:Models, Molecular; D046912:Multiprotein Complexes / Q000737:chemistry*; D011485:Protein Binding; D011487:Protein Conformation; D017510:Protein Folding
chemical_list: D046912:Multiprotein Complexes