Dataset Information

Identification of hot spot residues at protein-protein interface.

ABSTRACT: It is known that binding free energy of protein-protein interaction is mainly contributed by hot spot (high energy) interface residues. Here, we investigate the characteristics of hot spots by examining inter-atomic sidechain-sidechain interactions using a dataset of 296 alanine-mutated interface residues. Results show that hot spots participate in strong and energetically favorable sidechain-sidechain interactions. Subsequently, we describe a novel, yet simple 'hot spot' prediction model with an accuracy that is similar to many available approaches. The model is also shown to efficiently distinguish specific protein-protein interactions from non-specific interactions.

SUBMITTER: Li L

PROVIDER: S-EPMC1891667 | biostudies-literature | 2006 Apr

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Identification of hot spot residues at protein-protein interface.

Li Lei L Zhao Bing B Cui Zhanhua Z Gan Jacob J Sakharkar Meena Kishore MK Kangueane Pandjassarame P

Bioinformation 20060404 4

It is known that binding free energy of protein-protein interaction is mainly contributed by hot spot (high energy) interface residues. Here, we investigate the characteristics of hot spots by examining inter-atomic sidechain-sidechain interactions using a dataset of 296 alanine-mutated interface residues. Results show that hot spots participate in strong and energetically favorable sidechain-sidechain interactions. Subsequently, we describe a novel, yet simple 'hot spot' prediction model with a ...[more]

PMID: 17597870

Similar Datasets

Project description:BackgroundAlanine scanning mutagenesis is a powerful experimental methodology for investigating the structural and energetic characteristics of protein complexes. Individual amino-acids are systematically mutated to alanine and changes in free energy of binding (DeltaDeltaG) measured. Several experiments have shown that protein-protein interactions are critically dependent on just a few residues ("hot spots") at the interface. Hot spots make a dominant contribution to the free energy of binding and if mutated they can disrupt the interaction. As mutagenesis studies require significant experimental efforts, there is a need for accurate and reliable computational methods. Such methods would also add to our understanding of the determinants of affinity and specificity in protein-protein recognition.ResultsWe present a novel computational strategy to identify hot spot residues, given the structure of a complex. We consider the basic energetic terms that contribute to hot spot interactions, i.e. van der Waals potentials, solvation energy, hydrogen bonds and Coulomb electrostatics. We treat them as input features and use machine learning algorithms such as Support Vector Machines and Gaussian Processes to optimally combine and integrate them, based on a set of training examples of alanine mutations. We show that our approach is effective in predicting hot spots and it compares favourably to other available methods. In particular we find the best performances using Transductive Support Vector Machines, a semi-supervised learning scheme. When hot spots are defined as those residues for which DeltaDeltaG >or= 2 kcal/mol, our method achieves a precision and a recall respectively of 56% and 65%.ConclusionWe have developed an hybrid scheme in which energy terms are used as input features of machine learning models. This strategy combines the strengths of machine learning and energy-based methods. Although so far these two types of approaches have mainly been applied separately to biomolecular problems, the results of our investigation indicate that there are substantial benefits to be gained by their integration.

Dataset Information

Identification of hot spot residues at protein-protein interface.

Publications

Identification of hot spot residues at protein-protein interface.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets