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. 2015 May;83(5):891-7.
doi: 10.1002/prot.24784. Epub 2015 Mar 25.

Protein models docking benchmark 2

Ivan Anishchenko  1 , Petras J Kundrotas, Alexander V Tuzikov, Ilya A Vakser
Affiliations

Protein models docking benchmark 2

Ivan Anishchenko et al. Proteins. 2015 May.

Abstract

Structural characterization of protein-protein interactions is essential for our ability to understand life processes. However, only a fraction of known proteins have experimentally determined structures. Such structures provide templates for modeling of a large part of the proteome, where individual proteins can be docked by template-free or template-based techniques. Still, the sensitivity of the docking methods to the inherent inaccuracies of protein models, as opposed to the experimentally determined high-resolution structures, remains largely untested, primarily due to the absence of appropriate benchmark set(s). Structures in such a set should have predefined inaccuracy levels and, at the same time, resemble actual protein models in terms of structural motifs/packing. The set should also be large enough to ensure statistical reliability of the benchmarking results. We present a major update of the previously developed benchmark set of protein models. For each interactor, six models were generated with the model-to-native C(α) RMSD in the 1 to 6 Å range. The models in the set were generated by a new approach, which corresponds to the actual modeling of new protein structures in the "real case scenario," as opposed to the previous set, where a significant number of structures were model-like only. In addition, the larger number of complexes (165 vs. 63 in the previous set) increases the statistical reliability of the benchmarking. We estimated the highest accuracy of the predicted complexes (according to CAPRI criteria), which can be attained using the benchmark structures. The set is available at http://dockground.bioinformatics.ku.edu.

Keywords: modeling of protein complexes; protein interactions; protein modeling; protein recognition; structure prediction.

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Figures

Figure 1
Figure 1
Flowchart of the model generating procedure.
Figure 2
Figure 2
Relative content of the secondary structure elements in models of different accuracy. The plots for the old (A) and the new (B) sets show distribution of the number of residues in α-helices and β-strands in a model divided by the corresponding number in the native structure. The curves were smoothed using Savitzky-Golay method in the Origin 2015 software package.
Figure 3
Figure 3
Comparison of X-ray, NEB and I-TASSER structures. Secondary structure content for the PDB entry 1oph, chain B; α-helices and β-strands are in cyan and red, respectively. The interface is shown by gray surface (A) and dots (B). The secondary structure is substantially distorted in the NEB model, whereas well preserved and close to the X-ray structure in the I-TASSER model.
Figure 4
Figure 4
Correlation of interface and full structure accuracy of the models. The y=x line is for reference. Open circles are average interface RMSD of all models at each level of full structure accuracy.
Figure 5
Figure 5
Quality of model-model complexes according to CAPRI criteria.
Figure 6
Figure 6
Source organisms for complexes in the previous and the new benchmark sets. Four most highly populated organisms are shown.
Figure 7
Figure 7
Dockground resource for protein recognition studies.
See this image and copyright information in PMC

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