Funnel Hunting in a Rough Terrain: Learning and Discriminating Native Energy Funnels

Nir London; Ora Schueler-Furman

doi:10.1016/j.str.2007.11.013

Funnel Hunting in a Rough Terrain: Learning and Discriminating Native Energy Funnels

Nir London, Ora Schueler-Furman^*

^*Corresponding author for this work

Department of Microbiology and Molecular Genetics

Research output: Contribution to journal › Article › peer-review

28 Scopus citations

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.

Original language	English
Pages (from-to)	269-279
Number of pages	11
Journal	Structure
Volume	16
Issue number	2
DOIs	https://doi.org/10.1016/j.str.2007.11.013
State	Published - 12 Feb 2008

Bibliographical note

Funding Information:
We thank the many scientists who have participated in the development of the Rosetta software suite. In particular, Phil Bradley and John Karanicolas developed a large set of features for the characterization of Rosetta models. We thank the Baker Lab, in particular Keight Laidig, for the use of computer clusters in the initial stages of this work. Finally we thank Chu Wang, Yael Mandel-Gutfreund, Sarel Fleishman, Dan Reshef, and Barak Raveh for critical comments on the manuscript. This work was supported by The Israel Science Foundation founded by the Israel Academy of Science and Humanities, grant number 306/6.

Keywords

PROTEINS

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10.1016/j.str.2007.11.013

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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.",

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Funnel Hunting in a Rough Terrain: Learning and Discriminating Native Energy Funnels

Abstract

Bibliographical note

Keywords

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