Dynamics based alignment of proteins: an alternative approach to quantify dynamic similarity

<p>Abstract</p> <p>Background</p> <p>The dynamic motions of many proteins are central to their function. It therefore follows that the dynamic requirements of a protein are evolutionary constrained. In order to assess and quantify this, one needs to compare the dynamic motions of different proteins. Comparing the dynamics of distinct proteins may also provide insight into how protein motions are modified by variations in sequence and, consequently, by structure. The optimal way of comparing complex molecular motions is, however, far from trivial. The majority of comparative molecular dynamics studies performed to date relied upon prior sequence or structural alignment to define which residues were equivalent in 3-dimensional space.</p> <p>Results</p> <p>Here we discuss an alternative methodology for comparative molecular dynamics that does not require any prior alignment information. We show it is possible to align proteins based solely on their dynamics and that we can use these dynamics-based alignments to quantify the dynamic similarity of proteins. Our method was tested on 10 representative members of the PDZ domain family.</p> <p>Conclusions</p> <p>As a result of creating pair-wise dynamics-based alignments of PDZ domains, we have found evolutionarily conserved patterns in their backbone dynamics. The dynamic similarity of PDZ domains is highly correlated with their structural similarity as calculated with Dali. However, significant differences in their dynamics can be detected indicating that sequence has a more refined role to play in protein dynamics than just dictating the overall fold. We suggest that the method should be generally applicable.</p

JGromacs: A Java Package for Analyzing Protein Simulations

UNLABELLED: In this paper, we introduce JGromacs, a Java API (Application Programming Interface) that facilitates the development of cross-platform data analysis applications for Molecular Dynamics (MD) simulations. The API supports parsing and writing file formats applied by GROMACS (GROningen MAchine for Chemical Simulations), one of the most widely used MD simulation packages. JGromacs builds on the strengths of object-oriented programming in Java by providing a multilevel object-oriented representation of simulation data to integrate and interconvert sequence, structure, and dynamics information. The easy-to-learn, easy-to-use, and easy-to-extend framework is intended to simplify and accelerate the implementation and development of complex data analysis algorithms. Furthermore, a basic analysis toolkit is included in the package. The programmer is also provided with simple tools (e.g., XML-based configuration) to create applications with a user interface resembling the command-line interface of GROMACS applications. AVAILABILITY: JGromacs and detailed documentation is freely available from http://sbcb.bioch.ox.ac.uk/jgromacs under a GPLv3 license

The Role of Flexibility and Conformational Selection in the Binding Promiscuity of PDZ Domains

<div><p>In molecular recognition, it is often the case that ligand binding is coupled to conformational change in one or both of the binding partners. Two hypotheses describe the limiting cases involved; the first is the induced fit and the second is the conformational selection model. The conformational selection model requires that the protein adopts conformations that are similar to the ligand-bound conformation in the absence of ligand, whilst the induced-fit model predicts that the ligand-bound conformation of the protein is only accessible when the ligand is actually bound. The flexibility of the apo protein clearly plays a major role in these interpretations. For many proteins involved in signaling pathways there is the added complication that they are often promiscuous in that they are capable of binding to different ligand partners. The relationship between protein flexibility and promiscuity is an area of active research and is perhaps best exemplified by the PDZ domain family of proteins. In this study we use molecular dynamics simulations to examine the relationship between flexibility and promiscuity in five PDZ domains: the human Dvl2 (Dishevelled-2) PDZ domain, the human Erbin PDZ domain, the PDZ1 domain of InaD (inactivation no after-potential D protein) from fruit fly, the PDZ7 domain of GRIP1 (glutamate receptor interacting protein 1) from rat and the PDZ2 domain of PTP-BL (protein tyrosine phosphatase) from mouse. We show that despite their high structural similarity, the PDZ binding sites have significantly different dynamics. Importantly, the degree of binding pocket flexibility was found to be closely related to the various characteristics of peptide binding specificity and promiscuity of the five PDZ domains. Our findings suggest that the intrinsic motions of the apo structures play a key role in distinguishing functional properties of different PDZ domains and allow us to make predictions that can be experimentally tested.</p> </div

Mean dRMSD dissimilarity between the ligand-bound conformations and the most similar, 10 most similar, 100 most similar and 200 most similar snapshots of the apo MD simulations (for details of the Q(1), Q(10), Q(100) and Q(200) measures see Methods).

<p>Mean dRMSD dissimilarity between the ligand-bound conformations and the most similar, 10 most similar, 100 most similar and 200 most similar snapshots of the apo MD simulations (for details of the Q(1), Q(10), Q(100) and Q(200) measures see <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1002749#s3" target="_blank">Methods</a>).</p

Flexibility matrix calculated for an experimental ensemble of Dvl2 PDZ structures; apo (PDB code: 2rey), and 4 ligand-bound complexes (PDB codes: 3cbx, 3cby, 3cbz, 3cc0).

<p>Flexibility matrix calculated for an experimental ensemble of Dvl2 PDZ structures; apo (PDB code: 2rey), and 4 ligand-bound complexes (PDB codes: 3cbx, 3cby, 3cbz, 3cc0).</p

The architecture of a PDZ domain as illustrated by Dvl2 PDZ (A) shows that the peptide ligand binds (licorice representation) in between a cleft formed by a the α2-helix (except in Erbin where its called the α1) and the β2-strand.

<p>The multiple sequence alignment of the five PDZ domains used in this study are used to define the sequence regions of these two key secondary structure elements (B).</p

Multidimensional scaling analysis for Dvl2 PDZ colored by silhouette index values (A) and by cluster membership (B).

<p>Blue dots represent conformations that belong to cluster one; red dots those that belong to cluster two. The four crystal structures, pep-C1, pep-N1, pep-N2 and pep-N3 are represented by magenta, cyan, yellow and green dots, respectively. A comparison of the medoid conformation from cluster one to experimentally observed ligand-bound states (C) shows that medoid (blue) is closer to the conformation for pep-N3 (red) than pep-N2 (green). Conversely, the medoid conformation from cluster two (D) is closer to the conformation of pep-N2 (green) than pep-N3 (red). Multidimensional scaling analysis for the Erbin PDZ (E) reveals one major cluster (blue dots) with several outliers (red dots) defined as conformations that have dRMSD dissimilarity equal or larger than 0.8 Å from the medoid conformer.</p

Overall fluctuation measure, Θ, calculated for the five PDZ binding sites based on the conformational ensembles of the 200 ns MD trajectories.

<p>Overall fluctuation measure, Θ, calculated for the five PDZ binding sites based on the conformational ensembles of the 200 ns MD trajectories.</p

Fluctuation (A) and flexibility pattern (B) for GRIP1 PDZ7 showing that the binding site is conformationally restricted.

<p>Distribution (C) of the size of the binding cleft defined by a simple distance across the base of the cleft for equivalent residue pairs in all five PDZ domains studied here (Dvl2: R34 and E84, Erbin: G338 and H388, InaD: R34 and E84, PTP-BL: G31 and H78 and GRIP1 PDZ7: A41 and C84). Distances are taken from the MD simulations (discarding the first nanosecond).</p

Fluctuation pattern (A) and the flexibility pattern (B) for the PTP-BL PDZ2 domain.

<p>The multidimensional scaling analysis (C) of PTP-BL PDZ2. The conformer corresponding to the APC-bound structure is shown in yellow. Outliers are indicated in red and are defined as having a dRMSD larger or equal to 0.9 Å from the medoid conformer. The mean absolute difference distance matrix (Δ) pattern (D) calculated between the APC peptide-bound conformation of PTP-BL PDZ2 and the 100 most similar MD simulation snapshots.</p