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Comprehensive experimental and computational analysis of binding energy hot spots at the NF-?B essential modulator/IKK? protein-protein interface.

ABSTRACT: We report a comprehensive analysis of binding energy hot spots at the protein-protein interaction (PPI) interface between nuclear factor kappa B (NF-?B) essential modulator (NEMO) and I?B kinase subunit ? (IKK?), an interaction that is critical for NF-?B pathway signaling, using experimental alanine scanning mutagenesis and also the FTMap method for computational fragment screening. The experimental results confirm that the previously identified NEMO binding domain (NBD) region of IKK? contains the highest concentration of hot-spot residues, the strongest of which are W739, W741, and L742 (??G = 4.3, 3.5, and 3.2 kcal/mol, respectively). The region occupied by these residues defines a potentially druggable binding site on NEMO that extends for ~16 Å to additionally include the regions that bind IKK? L737 and F734. NBD residues D738 and S740 are also important for binding but do not make direct contact with NEMO, instead likely acting to stabilize the active conformation of surrounding residues. We additionally found two previously unknown hot-spot regions centered on IKK? residues L708/V709 and L719/I723. The computational approach successfully identified all three hot-spot regions on IKK?. Moreover, the method was able to accurately quantify the energetic importance of all hot-spot residues involving direct contact with NEMO. Our results provide new information to guide the discovery of small-molecule inhibitors that target the NEMO/IKK? interaction. They additionally clarify the structural and energetic complementarity between "pocket-forming" and "pocket-occupying" hot-spot residues, and further validate computational fragment mapping as a method for identifying hot spots at PPI interfaces.

SUBMITTER: Golden MS

PROVIDER: S-EPMC3680600 | biostudies-literature | 2013 Apr

REPOSITORIES: biostudies-literature

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Publications

Comprehensive experimental and computational analysis of binding energy hot spots at the NF-κB essential modulator/IKKβ protein-protein interface.

Golden Mary S MS Cote Shaun M SM Sayeg Marianna M Zerbe Brandon S BS Villar Elizabeth A EA Beglov Dmitri D Sazinsky Stephen L SL Georgiadis Rosina M RM Vajda Sandor S Kozakov Dima D Whitty Adrian A

Journal of the American Chemical Society 20130410 16

We report a comprehensive analysis of binding energy hot spots at the protein-protein interaction (PPI) interface between nuclear factor kappa B (NF-κB) essential modulator (NEMO) and IκB kinase subunit β (IKKβ), an interaction that is critical for NF-κB pathway signaling, using experimental alanine scanning mutagenesis and also the FTMap method for computational fragment screening. The experimental results confirm that the previously identified NEMO binding domain (NBD) region of IKKβ contains ...[more]

PMID: 23506214

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Project description:BackgroundHot spots are interface residues that contribute most binding affinity to protein-protein interaction. A compact and relevant feature subset is important for building machine learning methods to predict hot spots on protein-protein interfaces. Although different methods have been used to detect the relevant feature subset from a variety of features related to interface residues, it is still a challenge to detect the optimal feature subset for building the final model.ResultsIn this study, three different feature selection methods were compared to propose a new hybrid feature selection strategy. This new strategy was proved to effectively reduce the feature space when we were building the prediction models for identifying hotspot residues. It was tested on eighty-two features, both conventional and newly proposed. According to the strategy, combining the feature subsets selected by decision tree and mRMR (maximum Relevance Minimum Redundancy) individually, we were able to build a model with 6 features by using a PSFS (Pseudo Sequential Forward Selection) process. Compared with other state-of-art methods for the independent test set, our model had shown better or comparable predictive performances (with F-measure 0.622 and recall 0.821). Analysis of the 6 features confirmed that our newly proposed feature CNSV_REL1 was important for our model. The analysis also showed that the complementarity between features should be considered as an important aspect when conducting the feature selection.ConclusionIn this study, most important of all, a new strategy for feature selection was proposed and proved to be effective in selecting the optimal feature subset for building prediction models, which can be used to predict hot spot residues on protein-protein interfaces. Moreover, two aspects, the generalization of the single feature and the complementarity between features, were proved to be of great importance and should be considered in feature selection methods. Finally, our newly proposed feature CNSV_REL1 had been proved an alternative and effective feature in predicting hot spots by our study. Our model is available for users through a webserver: http://zhulab.ahu.edu.cn/iPPHOT/ .

Dataset Information

Comprehensive experimental and computational analysis of binding energy hot spots at the NF-?B essential modulator/IKK? protein-protein interface.

Publications

Comprehensive experimental and computational analysis of binding energy hot spots at the NF-κB essential modulator/IKKβ protein-protein interface.

Similar Datasets

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