Structural and Functional Impact of Parkinson Disease-Associated Mutations in the E3 Ubiquitin Ligase Parkin.

Mutations in the PARKIN/PARK2 gene that result in loss-of-function of the encoded, neuroprotective E3 ubiquitin ligase Parkin cause recessive, familial early-onset Parkinson disease. As an increasing number of rare Parkin sequence variants with unclear pathogenicity are identified, structure-function analyses will be critical to determine their disease relevance. Depending on the specific amino acids affected, several distinct pathomechanisms can result in loss of Parkin function. These include disruption of overall Parkin folding, decreased solubility, and protein aggregation. However pathogenic effects can also result from misregulation of Parkin autoinhibition and of its enzymatic functions. In addition, interference of binding to coenzymes, substrates, and adaptor proteins can affect its catalytic activity too. Herein, we have performed a comprehensive structural and functional analysis of 21 PARK2 missense mutations distributed across the individual protein domains. Using this combined approach, we were able to pinpoint some of the pathogenic mechanisms of individual sequence variants. Similar analyses will be critical in gaining a complete understanding of the complex regulations and enzymatic functions of Parkin. These studies will not only highlight the important residues, but will also help to develop novel therapeutics aimed at activating and preserving an active, neuroprotective form of Parkin.

Phosphorylation by PINK1 releases the UBL domain and initializes the conformational opening of the E3 ubiquitin ligase Parkin.

Loss-of-function mutations in PINK1 or PARKIN are the most common causes of autosomal recessive Parkinson's disease. Both gene products, the Ser/Thr kinase PINK1 and the E3 Ubiquitin ligase Parkin, functionally cooperate in a mitochondrial quality control pathway. Upon stress, PINK1 activates Parkin and enables its translocation to and ubiquitination of damaged mitochondria to facilitate their clearance from the cell. Though PINK1-dependent phosphorylation of Ser65 is an important initial step, the molecular mechanisms underlying the activation of Parkin's enzymatic functions remain unclear. Using molecular modeling, we generated a complete structural model of human Parkin at all atom resolution. At steady state, the Ub ligase is maintained inactive in a closed, auto-inhibited conformation that results from intra-molecular interactions. Evidently, Parkin has to undergo major structural rearrangements in order to unleash its catalytic activity. As a spark, we have modeled PINK1-dependent Ser65 phosphorylation in silico and provide the first molecular dynamics simulation of Parkin conformations along a sequential unfolding pathway that could release its intertwined domains and enable its catalytic activity. We combined free (unbiased) molecular dynamics simulation, Monte Carlo algorithms, and minimal-biasing methods with cell-based high content imaging and biochemical assays. Phosphorylation of Ser65 results in widening of a newly defined cleft and dissociation of the regulatory N-terminal UBL domain. This motion propagates through further opening conformations that allow binding of an Ub-loaded E2 co-enzyme. Subsequent spatial reorientation of the catalytic centers of both enzymes might facilitate the transfer of the Ub moiety to charge Parkin. Our structure-function study provides the basis to elucidate regulatory mechanisms and activity of the neuroprotective Parkin. This may open up new avenues for the development of small molecule Parkin activators through targeted drug design.

Multiscale modeling of macromolecular biosystems.

In this article, we review the recent progress in multiresolution modeling of structure and dynamics of protein, RNA and their complexes. Many approaches using both physics-based and knowledge-based potentials have been developed at multiple granularities to model both protein and RNA. Coarse graining can be achieved not only in the length, but also in the time domain using discrete time and discrete state kinetic network models. Models with different resolutions can be combined either in a sequential or parallel fashion. Similarly, the modeling of assemblies is also often achieved using multiple granularities. The progress shows that a multiresolution approach has considerable potential to continue extending the length and time scales of macromolecular modeling.

Predicting protein ligand binding motions with the conformation explorer.

BACKGROUND: Knowledge of the structure of proteins bound to known or potential ligands is crucial for biological understanding and drug design. Often the 3D structure of the protein is available in some conformation, but binding the ligand of interest may involve a large scale conformational change which is difficult to predict with existing methods. RESULTS: We describe how to generate ligand binding conformations of proteins that move by hinge bending, the largest class of motions. First, we predict the location of the hinge between domains. Second, we apply an Euler rotation to one of the domains about the hinge point. Third, we compute a short-time dynamical trajectory using Molecular Dynamics to equilibrate the protein and ligand and correct unnatural atomic positions. Fourth, we score the generated structures using a novel fitness function which favors closed or holo structures. By iterating the second through fourth steps we systematically minimize the fitness function, thus predicting the conformational change required for small ligand binding for five well studied proteins. CONCLUSIONS: We demonstrate that the method in most cases successfully predicts the holo conformation given only an apo structure.

HingeMaster: normal mode hinge prediction approach and integration of complementary predictors.

Protein motion is often the link between structure and function and a substantial fraction of proteins move through a domain hinge bending mechanism. Predicting the location of the hinge from a single structure is thus a logical first step towards predicting motion. Here, we describe ways to predict the hinge location by grouping residues with correlated normal-mode motions. We benchmarked our normal-mode based predictor against a gold standard set of carefully annotated hinge locations taken from the Database of Macromolecular Motions. We then compared it with three existing structure-based hinge predictors (TLSMD, StoneHinge, and FlexOracle), plus HingeSeq, a sequence-based hinge predictor. Each of these methods predicts hinges using very different sources of information-normal modes, experimental thermal factors, bond constraint networks, energetics, and sequence, respectively. Thus it is logical that using these algorithms together would improve predictions. We integrated all the methods into a combined predictor using a weighted voting scheme. Finally, we encapsulated all our results in a web tool which can be used to run all the predictors on submitted proteins and visualize the results.

FlexOracle: predicting flexible hinges by identification of stable domains.

BACKGROUND: Protein motions play an essential role in catalysis and protein-ligand interactions, but are difficult to observe directly. A substantial fraction of protein motions involve hinge bending. For these proteins, the accurate identification of flexible hinges connecting rigid domains would provide significant insight into motion. Programs such as GNM and FIRST have made global flexibility predictions available at low computational cost, but are not designed specifically for finding hinge points. RESULTS: Here we present the novel FlexOracle hinge prediction approach based on the ideas that energetic interactions are stronger within structural domains than between them, and that fragments generated by cleaving the protein at the hinge site are independently stable. We implement this as a tool within the Database of Macromolecular Motions, MolMovDB.org. For a given structure, we generate pairs of fragments based on scanning all possible cleavage points on the protein chain, compute the energy of the fragments compared with the undivided protein, and predict hinges where this quantity is minimal. We present three specific implementations of this approach. In the first, we consider only pairs of fragments generated by cutting at a single location on the protein chain and then use a standard molecular mechanics force field to calculate the enthalpies of the two fragments. In the second, we generate fragments in the same way but instead compute their free energies using a knowledge based force field. In the third, we generate fragment pairs by cutting at two points on the protein chain and then calculate their free energies. CONCLUSION: Quantitative results demonstrate our method's ability to predict known hinges from the Database of Macromolecular Motions.

Hinge Atlas: relating protein sequence to sites of structural flexibility.

BACKGROUND: Relating features of protein sequences to structural hinges is important for identifying domain boundaries, understanding structure-function relationships, and designing flexibility into proteins. Efforts in this field have been hampered by the lack of a proper dataset for studying characteristics of hinges. RESULTS: Using the Molecular Motions Database we have created a Hinge Atlas of manually annotated hinges and a statistical formalism for calculating the enrichment of various types of residues in these hinges. CONCLUSION: We found various correlations between hinges and sequence features. Some of these are expected; for instance, we found that hinges tend to occur on the surface and in coils and turns and to be enriched with small and hydrophilic residues. Others are less obvious and intuitive. In particular, we found that hinges tend to coincide with active sites, but unlike the latter they are not at all conserved in evolution. We evaluate the potential for hinge prediction based on sequence. Motions play an important role in catalysis and protein-ligand interactions. Hinge bending motions comprise the largest class of known motions. Therefore it is important to relate the hinge location to sequence features such as residue type, physicochemical class, secondary structure, solvent exposure, evolutionary conservation, and proximity to active sites. To do this, we first generated the Hinge Atlas, a set of protein motions with the hinge locations manually annotated, and then studied the coincidence of these features with the hinge location. We found that all of the features have bearing on the hinge location. Most interestingly, we found that hinges tend to occur at or near active sites and yet unlike the latter are not conserved. Less surprisingly, we found that hinge residues tend to be small, not hydrophobic or aliphatic, and occur in turns and random coils on the surface. A functional sequence based hinge predictor was made which uses some of the data generated in this study. The Hinge Atlas is made available to the community for further flexibility studies.

Dissecting the mechanism of Epac activation via hydrogen-deuterium exchange FT-IR and structural modeling.

Exchange proteins directly activated by cAMP (Epac) make up a family of cAMP binding domain-containing proteins that play important roles in mediating the effects of cAMP through the activation of downstream small GTPases, Ras-proximate proteins. To delineate the mechanism of Epac activation, we probed the conformation and structural dynamics of Epac using amide hydrogen-deuterium (H-D) exchange coupled with Fourier transform infrared spectroscopy (FT-IR) and structural modeling. Our studies show that unlike that of cAMP-dependent protein kinase (PKA), the classic intracellular cAMP receptor, binding of cAMP to Epac does not induce significant changes in overall secondary structure and structural dynamics, as measured by FT-IR and the rate of H-D exchange, respectively. These results suggest that Epac activation does not involve significant changes in the amount of exposed surface areas as in the case of PKA activation, and conformational changes induced by cAMP in Epac are most likely confined to small local regions. Homology modeling and comparative structural analyses of the CBDs of Epac and PKA lead us to propose a model of Epac activation. On the basis of our model, Epac activation by cAMP employs the same underlying structural principal utilized by PKA, although the detailed structural and conformational changes associated with Epac and PKA activation are significantly different. In addition, we predict that during Epac activation the first beta-strand of the switchboard switches its conformation to a alpha-helix, which folds back to the beta-barrel core of the CBD and interacts directly with cAMP to form the base of the cAMP-binding pocket.

Strategies for articulated multibody-based adaptive coarse grain simulation of RNA.

Efficient modeling approaches are necessary to accurately predict large-scale structural behavior of biomolecular systems like RNA (ribonucleic acid). Coarse-grained approximations of such complex systems can significantly reduce the computational costs of the simulation while maintaining sufficient fidelity to capture the biologically significant motions. However, given the coupling and nonlinearity of RNA systems (and effectively all biopolymers), it is expected that different parameters such as geometric and dynamic boundary conditions, and applied forces will affect the system's dynamic behavior. Consequently, static coarse-grained models (i.e., models for which the coarse graining is time invariant) are not always able to adequately sample the conformational space of the molecule. We introduce here the concept of adaptive coarse-grained molecular dynamics of RNA, which automatically adjusts the coarseness of the model, in an effort to more optimally increase simulation speed, while maintaining accuracy. Adaptivity requires two basic algorithmic developments: first, a set of integrators that seamlessly allow transitions between higher and lower fidelity models while preserving the laws of motion. Second, we propose and validate metrics for determining when and where more or less fidelity needs to be integrated into the model to allow sufficiently accurate dynamics simulation. Given the central role that multibody dynamics plays in the proposed framework, and the nominally large number of dynamic degrees of freedom being considered in these applications, a computationally efficient multibody method which lends itself well to adaptivity is essential to the success of this effort. A suite of divide-and-conquer algorithm (DCA)-based approaches is employed to this end. These algorithms have been selected and refined for this purpose because they offer a good combination of computational efficiency and modular structure.

Predicting RNA structure by multiple template homology modeling.

Despite the importance of 3D structure to understand the myriad functions of RNAs in cells, most RNA molecules remain out of reach of crystallographic and NMR methods. However, certain structural information such as base pairing and some tertiary contacts can be determined readily for many RNAs by bioinformatics or relatively low cost experiments. Further, because RNA structure is highly modular, it is possible to deduce local 3D structure from the solved structures of evolutionarily related RNAs or even unrelated RNAs that share the same module. RNABuilder is a software package that generates model RNA structures by treating the kinematics and forces at separate, multiple levels of resolution. Kinematically, bonds in bases, certain stretches of residues, and some entire molecules are rigid while other bonds remain flexible. Forces act on the rigid bases and selected individual atoms. Here we use RNABuilder to predict the structure of the 200-nucleotide Azoarcus group I intron by homology modeling against fragments of the distantly-related Twort and Tetrahymena group I introns and by incorporating base pairing forces where necessary. In the absence of any information from the solved Azoarcus intron crystal structure, the model accurately depicts the global topology, secondary and tertiary connections, and gives an overall RMSD value of 4.6 A relative to the crystal structure. The accuracy of the model is even higher in the intron core (RMSD = 3.5 A), whereas deviations are modestly larger for peripheral regions that differ more substantially between the different introns. These results lay the groundwork for using this approach for larger and more diverse group I introns, as well for still larger RNAs and RNA-protein complexes such as group II introns and the ribosomal subunits.

StoneHinge: hinge prediction by network analysis of individual protein structures.

Hinge motions are important for molecular recognition, and knowledge of their location can guide the sampling of protein conformations for docking. Predicting domains and intervening hinges is also important for identifying structurally self-determinate units and anticipating the influence of mutations on protein flexibility and stability. Here we present StoneHinge, a novel approach for predicting hinges between domains using input from two complementary analyses of noncovalent bond networks: StoneHingeP, which identifies domain-hinge-domain signatures in ProFlex constraint counting results, and StoneHingeD, which does the same for DomDecomp Gaussian network analyses. Predictions for the two methods are compared to hinges defined in the literature and by visual inspection of interpolated motions between conformations in a series of proteins. For StoneHingeP, all the predicted hinges agree with hinge sites reported in the literature or observed visually, although some predictions include extra residues. Furthermore, no hinges are predicted in six hinge-free proteins. On the other hand, StoneHingeD tends to overpredict the number of hinges, while accurately pinpointing hinge locations. By determining the consensus of their results, StoneHinge improves the specificity, predicting 11 of 13 hinges found both visually and in the literature for nine different open protein structures, and making no false-positive predictions. By comparison, a popular hinge detection method that requires knowledge of both the open and closed conformations finds 10 of the 13 known hinges, while predicting four additional, false hinges.