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Identification of interacting hot spots in the beta3 integrin stalk using comprehensive interface design.

ABSTRACT: Protein-protein interfaces are usually large and complementary surfaces, but specific side chains, representing energetic "hot spots," often contribute disproportionately to binding free energy. We used a computational method, comprehensive interface design, to identify hot spots in the interface between the stalk regions of the ?3 and the complementary ?IIb and ?v integrin subunits. Using the Rosetta alanine-scanning and design algorithms to predict destabilizing, stabilizing, and neutral mutations in the ?3 region extending from residues Lys(532) through Gly(690), we predicted eight alanine mutations that would destabilize the ?IIb?3 interface as well as nine predicted to destabilize the ?v?3 interface, by at least 0.3 kcal/mol. The mutations were widely and unevenly distributed, with four between residues 552 and 563 and five between 590 and 610, but none between 565 and 589, and 611 and 655. Further, mutations destabilizing the ?v?3 and ?IIb?3 interfaces were not identical. The predictions were then tested by introducing selected mutations into the full-length integrins expressed in Chinese hamster ovary cells. Five mutations predicted to destabilize ?IIb and ?3 caused fibrinogen binding to ?IIb?3, whereas three of four predicted to be neutral or stabilizing did not. Conversely, a mutation predicted to destabilize ?v?3, but not ?IIb?3 (D552A), caused osteopontin binding to ?v?3, but not fibrinogen binding to ?IIb?3. These results indicate that stability of the distal stalk interface is involved in constraining integrins in stable, inactive conformations. Further, they demonstrate the ability of comprehensive interface design to identify functionally significant integrin mutations.

SUBMITTER: Donald JE

PROVIDER: S-EPMC2992298 | biostudies-literature | 2010 Dec

REPOSITORIES: biostudies-literature

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Identification of interacting hot spots in the beta3 integrin stalk using comprehensive interface design.

Donald Jason E JE Zhu Hua H Litvinov Rustem I RI DeGrado William F WF Bennett Joel S JS

The Journal of biological chemistry 20101007 49

Protein-protein interfaces are usually large and complementary surfaces, but specific side chains, representing energetic "hot spots," often contribute disproportionately to binding free energy. We used a computational method, comprehensive interface design, to identify hot spots in the interface between the stalk regions of the β3 and the complementary αIIb and αv integrin subunits. Using the Rosetta alanine-scanning and design algorithms to predict destabilizing, stabilizing, and neutral mutat ...[more]

PMID: 20929856

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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/ .

Project description:Background Ross River virus (RRV) is responsible for the most common vector-borne disease of humans reported in Australia. The virus circulates in enzootic cycles between multiple species of mosquitoes, wildlife reservoir hosts and humans. Public health concern about RRV is increasing due to rising incidence rates in Australian urban centres, along with increased circulation in Pacific Island countries. Australia experienced its largest recorded outbreak of 9544 cases in 2015, with the majority reported from south east Queensland (SEQ). This study examined potential links between disease patterns and transmission pathways of RRV. Methods The spatial and temporal distribution of notified RRV cases, and associated epidemiological features in SEQ, were analysed for the period 2001–2016. This included fine-scale analysis of disease patterns across the suburbs of the capital city of Brisbane, and those of 8 adjacent Local Government Areas, and host spot analyses to identify locations with significantly high incidence. Results The mean annual incidence rate for the region was 41/100,000 with a consistent seasonal peak in cases between February and May. The highest RRV incidence was in adults aged from 30 to 64?years (mean incidence rate: 59/100,000), and females had higher incidence rates than males (mean incidence rates: 44/100,000 and 34/100,000, respectively). Spatial patterns of disease were heterogeneous between years, and there was a wide distribution of disease across both urban and rural areas of SEQ. Overall, the highest incidence rates were reported from predominantly rural suburbs to the north of Brisbane City, with significant hot spots located in peri-urban suburbs where residential, agricultural and conserved natural land use types intersect. Conclusions Although RRV is endemic across all of SEQ, transmission is most concentrated in areas where urban and peri-urban environments intersect. The drivers of RRV transmission across rural-urban landscapes should be prioritised for further investigation, including identification of specific vectors and hosts that mediate human spillover.

Dataset Information

Identification of interacting hot spots in the beta3 integrin stalk using comprehensive interface design.

Publications

Identification of interacting hot spots in the beta3 integrin stalk using comprehensive interface design.

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