An automated decision-tree approach to predicting protein interaction hot spots.

Published

Journal Article

Protein-protein interactions can be altered by mutating one or more "hot spots," the subset of residues that account for most of the interface's binding free energy. The identification of hot spots requires a significant experimental effort, highlighting the practical value of hot spot predictions. We present two knowledge-based models that improve the ability to predict hot spots: K-FADE uses shape specificity features calculated by the Fast Atomic Density Evaluation (FADE) program, and K-CON uses biochemical contact features. The combined K-FADE/CON (KFC) model displays better overall predictive accuracy than computational alanine scanning (Robetta-Ala). In addition, because these methods predict different subsets of known hot spots, a large and significant increase in accuracy is achieved by combining KFC and Robetta-Ala. The KFC analysis is applied to the calmodulin (CaM)/smooth muscle myosin light chain kinase (smMLCK) interface, and to the bone morphogenetic protein-2 (BMP-2)/BMP receptor-type I (BMPR-IA) interface. The results indicate a strong correlation between KFC hot spot predictions and mutations that significantly reduce the binding affinity of the interface.

Full Text

Duke Authors

Cited Authors

  • Darnell, SJ; Page, D; Mitchell, JC

Published Date

  • September 1, 2007

Published In

Volume / Issue

  • 68 / 4

Start / End Page

  • 813 - 823

PubMed ID

  • 17554779

Pubmed Central ID

  • 17554779

Electronic International Standard Serial Number (EISSN)

  • 1097-0134

Digital Object Identifier (DOI)

  • 10.1002/prot.21474

Language

  • eng

Conference Location

  • United States