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Large oligomeric complex structures can be computationally assembled by efficiently combining docked interfaces.

ABSTRACT: Macromolecular oligomeric assemblies are involved in many biochemical processes of living organisms. The benefits of such assemblies in crowded cellular environments include increased reaction rates, efficient feedback regulation, cooperativity and protective functions. However, an atom-level structural determination of large assemblies is challenging due to the size of the complex and the difference in binding affinities of the involved proteins. In this study, we propose a novel combinatorial greedy algorithm for assembling large oligomeric complexes from information on the approximate position of interaction interfaces of pairs of monomers in the complex. Prior information on complex symmetry is not required but rather the symmetry is inferred during assembly. We implement an efficient geometric score, the transformation match score, that bypasses the model ranking problems of state-of-the-art scoring functions by scoring the similarity between the inferred dimers of the same monomer simultaneously with different binding partners in a (sub)complex with a set of pregenerated docking poses. We compiled a diverse benchmark set of 308 homo and heteromeric complexes containing 6 to 60 monomers. To explore the applicability of the method, we considered 48 sets of parameters and selected those three sets of parameters, for which the algorithm can correctly reconstruct the maximum number, namely 252 complexes (81.8%) in, at least one of the respective three runs. The crossvalidation coverage, that is, the mean fraction of correctly reconstructed benchmark complexes during crossvalidation, was 78.1%, which demonstrates the ability of the presented method to correctly reconstruct topology of a large variety of biological complexes.

SUBMITTER: Dietzen M

PROVIDER: S-EPMC5049452 | biostudies-literature | 2015 Oct

REPOSITORIES: biostudies-literature

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Large oligomeric complex structures can be computationally assembled by efficiently combining docked interfaces.

Dietzen Matthias M Kalinina Olga V OV Taškova Katerina K Kneissl Benny B Hildebrandt Anna-Katharina AK Jaenicke Elmar E Decker Heinz H Lengauer Thomas T Hildebrandt Andreas A

Proteins 20150824 10

Macromolecular oligomeric assemblies are involved in many biochemical processes of living organisms. The benefits of such assemblies in crowded cellular environments include increased reaction rates, efficient feedback regulation, cooperativity and protective functions. However, an atom-level structural determination of large assemblies is challenging due to the size of the complex and the difference in binding affinities of the involved proteins. In this study, we propose a novel combinatorial ...[more]

PMID: 26248608

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Project description:BackgroundProtein-DNA interaction governs a large number of cellular processes, and it can be altered by a small fraction of interface residues, i.e., the so-called hot spots, which account for most of the interface binding free energy. Accurate prediction of hot spots is critical to understand the principle of protein-DNA interactions. There are already some computational methods that can accurately and efficiently predict a large number of hot residues. However, the insufficiency of experimentally validated hot-spot residues in protein-DNA complexes and the low diversity of the employed features limit the performance of existing methods.ResultsHere, we report a new computational method for effectively predicting hot spots in protein-DNA binding interfaces. This method, called PreHots (the abbreviation of Predicting Hotspots), adopts an ensemble stacking classifier that integrates different machine learning classifiers to generate a robust model with 19 features selected by a sequential backward feature selection algorithm. To this end, we constructed two new and reliable datasets (one benchmark for model training and one independent dataset for validation), which totally consist of 123 hot spots and 137 non-hot spots from 89 protein-DNA complexes. The data were manually collected from the literature and existing databases with a strict process of redundancy removal. Our method achieves a sensitivity of 0.813 and an AUC score of 0.868 in 10-fold cross-validation on the benchmark dataset, and a sensitivity of 0.818 and an AUC score of 0.820 on the independent test dataset. The results show that our approach outperforms the existing ones.ConclusionsPreHots, which is based on stack ensemble of boosting algorithms, can reliably predict hot spots at the protein-DNA binding interface on a large scale. Compared with the existing methods, PreHots can achieve better prediction performance. Both the webserver of PreHots and the datasets are freely available at: http://dmb.tongji.edu.cn/tools/PreHots/ .

Dataset Information

Large oligomeric complex structures can be computationally assembled by efficiently combining docked interfaces.

Publications

Large oligomeric complex structures can be computationally assembled by efficiently combining docked interfaces.

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