Addressing the Role of Conformational Diversity in Protein Structure Prediction

<div><p>Computational modeling of tertiary structures has become of standard use to study proteins that lack experimental characterization. Unfortunately, 3D structure prediction methods and model quality assessment programs often overlook that an ensemble of conformers in equilibrium populates the native state of proteins. In this work we collected sets of publicly available protein models and the corresponding target structures experimentally solved and studied how they describe the conformational diversity of the protein. For each protein, we assessed the quality of the models against known conformers by several standard measures and identified those models ranked best. We found that model rankings are defined by both the selected target conformer and the similarity measure used. 70% of the proteins in our datasets show that different models are structurally closest to different conformers of the same protein target. We observed that model building protocols such as template-based or <i>ab initio</i> approaches describe in similar ways the conformational diversity of the protein, although for template-based methods this description may depend on the sequence similarity between target and template sequences. Taken together, our results support the idea that protein structure modeling could help to identify members of the native ensemble, highlight the importance of considering conformational diversity in protein 3D quality evaluations and endorse the study of the variability of the native structure for a meaningful biological analysis.</p></div

Examples of structural differences between pairs of known conformers (left panels, red and blue) and z-score distribution based on RMSD per position derived from the structural superpositions between conformers and with their corresponding best decoys (right panels).

<p>(a) Human unbound receptor associated protein (RAP) (PDB ID: 2FTU, red) and RAP bound to low-density lipoprotein receptor (LDLR) (PDB ID: 2FCW, blue). (b) Human SHIP2 protein conformers (PDB ID: 4A9C, red; PDB ID: 3NR8, blue; synthetic ligand, green). (c) NMR structures of RGS (Regulator of G protein signaling) domain of human protein RGS18 (PDB ID: 2OWI, model 2, red; PDB ID: 2OWI, model 18, blue). (d) Atx1 yeast metallochaperone in Cu(I)-bound form (PDB ID:1FD8, red) and the Atx1-Ccc2 ATPase complex (PDB ID: 2GGP, model 19, blue). See text for further details.</p

Comparison of target structure-best decoy RMSD values between both conformers of a protein showing maximum conformational diversity.

<p>(a) Distribution of decoy-target RMSD values calculated for each protein in our dataset choosing pairs of best decoys for each conformer. (b) The same comparison showed in a, but swapping conformers and best decoys. Symbols indicate the three datasets used in the study (CASP 3–10, pink circles; QUARK, green circles; Rosetta@home 2007, blue circles).</p

Datasets used in the analysis.

<p>Shown are the number of proteins and the overall number of decoys in each dataset, the minimum and average TM-score (in TM-score units), the minimum and average GDT_TS (in GDT_TS units) for all targets in each dataset and their maximum and average Cα-RMSD (in Å) calculated likewise. Datasets and statistics are available as <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0154923#pone.0154923.s002" target="_blank">S1 Dataset</a> and as a compressed file from our website at <a href="http://ufq.unq.edu.ar/sbg/files/Palopoli-Monzon_2016_SI.tar.gz" target="_blank">http://ufq.unq.edu.ar/sbg/files/Palopoli-Monzon_2016_SI.tar.gz</a>.</p

Comparison of GDT_TS against RMSD, calculated for pairs of conformers with maximum conformational diversity for each protein in our dataset.

<p>Pairs were taken from the CoDNaS database of different structures (from available PDB files) for each represented protein. RMSD scores are expressed in Å while GDT_TS values are normalized to the range [0, 1].</p

Distribution of Spearman’s rank correlation coefficients per target, computed between structural rankings for all proposed structural models against each of the conformers in the pair of maximum conformational diversity, as a function of different average measures of structural similarity between native conformers.

<p>(a) Correlation against GDT_TS between native conformers. (b) Idem (a) but using TM-score. (c) Idem (a) but using RMSD, with distinction among the three dataset used in the study (CASP 3–10, pink circles; QUARK, green circles; Rosetta@home 2007, blue circles). (b) Same as (c) but with distinction between experimental methods used for determining protein structure according to PDB (X-RD, x-ray diffraction, red circles; NMR, blue triangles).</p

Backbone-independent conformational diversity characterization.

<p>On each boxplot, bottom and top of the box correspond to the first and third quartile, the vertical bar inside the box is the median (Second quartile) and the notches displays the median absolute deviation (M.A.D). Also, the violin plot under the boxplot shows the probability density of given variable. (A) Maximum pocket volume distribution between conformers in maximum pair of conformational diversity. Cavities are significantly greater in partially disordered and malleable proteins (Kruskal—Wallis rank sum test shows significant differences between this groups P << 0.001, with a Nemenyi post-hoc test shows that the volume of cavities in rigid proteins are significantly different of the cavity volumes in partially disordered or malleable proteins with P < 0.001). (B) Maximum tunnel length variation distribution between conformers in maximum pair of conformational diversity. The parameter expresses the proportion of variation between largest tunnels in each conformer and it is calculated as |<i>L</i>₁ − <i>L</i>₂ |/max(<i>L</i>₁,<i>L</i>₂), where L_i is the length of the largest tunnel on the corresponding conformer in the maximum pair. The mean value in rigid proteins is significantly greater than partially disordered proteins (Wilcoxon rank sum test, P < 0.001). (C) Mean ratio of longest tunnel distribution between conformers in maximum pair of conformational diversity. The tunnel length is normalized by the conformer length. (D) Average degree distributions between conformers in maximum pair of conformational diversity. Partially disordered proteins show greater mean values than rigid proteins (Kruskal—Wallis rank sum test shows significant differences between this groups P << 0.001 with a Nemenyi post-hoc test).</p

Maximum conformational diversity distributions.

<p>(A) Pairs of conformers showing maximum RMSD for proteins with/without IDRs considering all the available conformers per protein. In light red, we show the distribution without IDRs and in light yellow proteins with IDRs in at least one of their conformers. The distribution of proteins without IDRs had significantly lower overall RMSD values compared with proteins with IDRs (Kolmogorov—Smirnov test, P << 0.001). (B) The maximum conformational diversity distribution can be represented by three main sets of proteins: rigid (all conformers per protein without IDRs), partially disordered (with IDRs at least in one conformer and in the maximum RMSD pair of conformational diversity) and malleable (with IDRs in at least one conformer but the maximum pair of conformational diversity without IDRs). These three distributions are significantly different in their median values (Kruskal—Wallis rank sum test, P << 0.001 with a Nemenyi post-hoc test).</p

Structural superposition of the cellulose cel48F conformations from Clostridium cellulolyticum.

<p>This enzyme is a processive endo-cellulase with a large active and binding site to locate a cellulose chain which enters to the protein through a cleft located in the surface of the protein [<a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1005398#pcbi.1005398.ref041" target="_blank">41</a>]. In our dataset, this protein contains 8 conformers (PDB codes: 1F9D_A, 1F9O_A, 1G9G_A, 1FAE_A, 1FBO_A, 1FBW_A, 1FCE_A, 2QNO_A) with a maximum RMSD of 0.21 Å. (A) We can see that there is almost no significant structural difference in the carbon-alpha trace between conformers in the pair of maximum RMSD (PDB codes: 1F9O_A, 1G9G_A), however, the tunnels (in red) as large as 65 Å long (predicted by MOLE) appeared in one conformer (PDB code: 1F9O_A) which contains different ligands while in another conformer this tunnel is absent. (B) The superposition of all conformers only shows slight rotations and minimal movements in lining residues (in yellow) of this main tunnel, producing the opening and closing of the tunnel. Besides our conformers comparison, molecular dynamic simulations have also confirmed the rigidity of this protein [<a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1005398#pcbi.1005398.ref042" target="_blank">42</a>].</p

Visual comparison of main structural features in each of the three protein sets described in this work.

<p>Area of each circle is proportional to the average corresponding quantitative measure.</p