CoNSEnsX: an ensemble view of protein structures and NMR-derived experimental data

Abstract

Background: In conjunction with the recognition of the functional role of internal dynamics of proteins at various timescales, there is an emerging use of dynamic structural ensembles instead of individual conformers. These ensembles are usually substantially more diverse than conventional NMR ensembles and eliminate the expectation that a single conformer should fulfill all NMR parameters originating from 10(16) - 10(17) molecules in the sample tube. Thus, the accuracy of dynamic conformational ensembles should be evaluated differently to that of single conformers.

Results: We constructed the web application CoNSEnsX (Consistency of NMR-derived Structural Ensembles with eXperimental data) allowing fast, simple and convenient assessment of the correspondence of the ensemble as a whole with diverse independent NMR parameters available. We have chosen different ensembles of three proteins, human ubiquitin, a small protease inhibitor and a disordered subunit of cGMP phosphodiesterase 5/6 for detailed evaluation and demonstration of the capabilities of the CoNSEnsX approach.

Conclusions: Our results present a new conceptual method for the evaluation of dynamic conformational ensembles resulting from NMR structure determination. The designed CoNSEnsX approach gives a complete evaluation of these ensembles and is freely available as a web service at http://consensx.chem.elte.hu.

Figure 1

Scheme of the CoNSEnsX approach. Gives the schematic structure of the CoNSEnsX web server.

Figure 2

Ribbon representation of human ubiquitin ensembles generated for this study. A: U_COCO ensemble (20 members); B: U_NNR ensemble (32 conformers); C: U_1UBQMD ensemble (32 conformers). Figures were prepared with MOLMOL.

Figure 3

Selected parts of the CoNSEnsX server output for the U_NNR ensemble.

Figure 4

Graphs of correlations of selected experimental parameters to those backcalculated from various ubiquitin ensembles. Lines are shown only to make the graphs easier to read.

Figure 5

Graphs of correlations of selected experimental parameters to those backcalculated from various SGCI ensembles. Lines are shown only to make the graphs easier to read.

Figure 6

Ribbon representation of the disordered PDE γ subunit (PDB ID 2JU4). Figure prepared with MOLMOL.

Figure 7

Correlations of experimental and back-calculated chemical shifts in the 100-membered conformational ensemble of the PDE γ subunit (PDB ID 2JU4). Blue line: correlation per model; red line: average correlation per model; green line: correlation calculated for the ensemble. A: Hα shifts, B: amide H shifts, C: carbonyl C shifts, D: amide N shifts.