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. 2013 Dec 17;110(51):20533-8.
doi: 10.1073/pnas.1315625110. Epub 2013 Dec 2.

Coevolutionary signals across protein lineages help capture multiple protein conformations

Affiliations

Coevolutionary signals across protein lineages help capture multiple protein conformations

Faruck Morcos et al. Proc Natl Acad Sci U S A. .

Abstract

A long-standing problem in molecular biology is the determination of a complete functional conformational landscape of proteins. This includes not only proteins' native structures, but also all their respective functional states, including functionally important intermediates. Here, we reveal a signature of functionally important states in several protein families, using direct coupling analysis, which detects residue pair coevolution of protein sequence composition. This signature is exploited in a protein structure-based model to uncover conformational diversity, including hidden functional configurations. We uncovered, with high resolution (mean ~1.9 Å rmsd for nonapo structures), different functional structural states for medium to large proteins (200-450 aa) belonging to several distinct families. The combination of direct coupling analysis and the structure-based model also predicts several intermediates or hidden states that are of functional importance. This enhanced sampling is broadly applicable and has direct implications in protein structure determination and the design of ligands or drugs to trap intermediate states.

Keywords: conformational plasticity; covariation; molecular dynamics; statistical inference.

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Conflict of interest statement

The authors declare no conflict of interest.

Figures

Fig. 1.
Fig. 1.
A hybrid SBM+DCA model of the l-leucine binding protein is able to uncover its two-state (apo/holo) conformational landscape. A compares the native open and closed contact maps and B compares a DCA contact map with the native closed state. In A, comparing the native contact map of the open conformation (PDB ID 1usg; lower triangular map) and the closed conformation (PDB ID 1usi; upper triangular map) shows a clear set of contacts (shaded box) that are exclusive to the closed state. In B a predicted contact map using highly ranked DCA residue pairs (lower triangular map) shows a very accurate reconstruction of the complete map that includes the extra contacts in the closed conformation (upper triangular map). (C) Structural comparison between the apo and the holo states of the l-leucine binding protein, showing domain closure. (D) Integrating a SBM of the open-state topology with DCA contacts produces a distinct bimodal landscape, as opposed to the single-basin distribution observed when we use the same number of extra contacts but randomly distributed.
Fig. 2.
Fig. 2.
The glutamate receptor has a ligand-dependent domain closure. (A) The conformational landscape observed after combining an open-state topology with coevolutionary restraints obtained from the family of bacterial extracellular solute-binding proteins (PF00497). The landscape includes conformations with an rmsd of less than 2 Å from the crystal structures of open and closed states. An intermediate state is also present that is between the closed and the open conformations. (B) After using the gromos clustering algorithm (31) for the molecular dynamics trajectory, the top three cluster centroids cover 98% of the conformations. The centroid of the most populated cluster is in fact structurally very similar to the kainite-bound structure of the glutamate receptor (rmsd 0.8 Å). Kainate is a partial agonist that brings the protein to a semiclosed state. (C) Structural comparison between the centroids and experimental structures of antagonist (open; PDB ID 1fto), partial agonist (semiclosed; PDB ID 1fw0), and agonist (closed; PDB ID 1ftm) states. The predictions for these three states have an ∼1-Å rmsd with respect to the crystal structures.
Fig. 3.
Fig. 3.
d-Ribose binding protein (PDB IDs 1urp and 2dri) goes through large conformational changes upon ribose binding. (A) Comparison between the open and the closed native contact maps. Blue marks illustrate open-state contacts and red contacts are uniquely found in the closed state. The lower triangular part of the map shows the contacts estimated using DCA for which we can identify the global structure as well as the closed-state contacts (red dashed box). We can also see in the map an additional set of contacts (black dashed box) that may lead to a third state that has not been observed experimentally. (B) The conformational landscape shows a very distinct third state, which is not observed in the dual-basin control simulations, even if random extra contacts are added to increase the sampling of the conformational space (control in Fig. S5). (C) If we represent ligand constraints with few contacts with experimental distance parameters, then the landscape shows the ligand-bound closed state while the population of the intermediate state is still present. (D) The contact maps and structures for the cluster centroids are shown for the open, intermediate, and closed states. There is a distinct set of competing contacts that induce the observed landscape only when we integrate our model with DCA information. The comparison of the centroid cluster structures shows how two helices from the two domains are aligned for the open and closed states, as in their native structures, whereas a twisted alignment was found for the intermediate state. Ravindranathan et al. have provided evidence for this intermediate state (3). Similar evidence exists for other sugar-bound proteins like d-Glucose and Maltose binding protein (36, 38, 39).

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