Insights into the Structural Dynamics of Nucleocytoplasmic Transport ...

Article

Insights into the Structural Dynamics of Nucleocytoplasmic Transport of tRNA by Exportin-t Asmita Gupta,1 Senthilkumar Kailasam,1 and Manju Bansal1,* 1

Molecular Biophysics Unit, Indian Institute of Science, Bangalore, Karnataka, India

ABSTRACT Exportin-t (Xpot) transports mature 50 - and 30 -end processed tRNA from the nucleus to the cytoplasm by associating with a small G-protein Ran (RAs-related nuclear protein), in the nucleus. The release of tRNA in cytoplasm involves RanGTP hydrolysis. Despite the availability of crystal structures of nuclear and cytosolic forms of Xpot, the molecular details regarding the sequential events leading to tRNA release and subsequent conformational changes occurring in Xpot remain unknown. We have performed a combination of classical all-atom and accelerated molecular dynamics simulations on a set of complexes involving Xpot to study a range of features including conformational flexibility of free and cargo-bound Xpot and functionally critical contacts between Xpot and its cargo. The systems investigated include free Xpot and its different complexes, bound either to Ran (GTP/GDP) or tRNA or both. This approach provided a statistically reliable estimate of structural dynamics of Xpot after cargo release. The mechanistic basis for Xpot opening after cargo release has been explained in terms of dynamic structural hinges, about which neighboring region could be displaced to facilitate the nuclear to cytosolic state transition. Post-RanGTP hydrolysis, a cascade of events including local conformational change in RanGTP and loss of critical contacts at Xpot/tRNA interface suggest factors responsible for eventual release of tRNA. The level of flexibility in different Xpot complexes varied depending on the arrangement of individual HEAT repeats. Current study provides one of the most comprehensive and robust analysis carried out on this protein using molecular dynamics schemes.

INTRODUCTION Transport of proteins, mRNA, tRNA, and various other regulatory and structural RNAs through nuclear pore complex is mediated by members of a family of proteins known as Karyopherins (1). These proteins bind to the molecules and transport them into as well as out of the nucleus (2). All Karyopherins share a similar structure composed of a consecutive arrangement of a structural motif called HEAT repeat (Huntingtin, elongation factor 3, PR65/A subunit of protein phosphatase 2A, lipid kinase TOR), which is composed of two antiparallel a-helices (A and B) connected by a short intrarepeat loop (3–5). These repeats pack against each other in a clockwise arrangement to generate extremely flexible, superhelical protein scaffolds, which can bind cargoes of different sizes to form large macromolecular transport complexes (6,7). The activity of Karyopherins and directionality associated with the trans-

Submitted August 3, 2015, and accepted for publication February 5, 2016. *Correspondence: [email protected] Senthilkumar Kailasam’s present address is Alberta RNA Research and Training Institute and Department of Chemistry and Biochemistry, University of Lethbridge, Alberta, Canada Editor: Nathan Baker. http://dx.doi.org/10.1016/j.bpj.2016.02.015 Ó 2016 Biophysical Society

1264 Biophysical Journal 110, 1264–1279, March 29, 2016

port is regulated by a small G-protein Ran (RAs-related nuclear protein), which shuttles between GTP and GDP (Guanosine 50 tri-/di-phosphate) nucleotide-bound states across nuclear pore complex (8). Exportin-t or Xpot (Los1p homolog in yeast) is one of the carrier proteins, belonging to the Karyopherin family, that specifically binds and exports mature 50 - and 30 -end processed tRNA molecules out of the nucleus in the presence of RanGTP (9–13). From a global perspective, Xpot shares similar architecture with other Importin-b (Impb) like proteins. It is composed of 19 HEAT repeats (Fig. 1) with the C-terminal HEAT 19 capped by three short helices (14). Crystallographic studies have illustrated that Xpot assumes a closed form in the nucleus and binds to RanGTP and tRNA and an open, extended conformation in the cytoplasmic cargo-free state. Although Xpot-Ran interactions are restricted to N-terminal repeats 1–4 and intrarepeat loops of HEATs 9 and 13, Xpot-tRNA interactions are extended from HEATs 5–18. All these interactions are limited to B-helices of these HEATs except in the case of HEAT H1 where certain residues in the A-helix contact RanGTP (Fig. 1, b and c). The general mechanism of tRNA export involves Xpot binding to adaptor protein RanGTP, which then primes it for binding to its cargo in

Nucleocytoplasmic Export of tRNA

FIGURE 1 (a) Xpot structure in nuclear cargobound state. Ran and tRNA are omitted. HEAT repeats from N to C terminal are colored in a gradient from beige to black. (b and c) Distribution of residues on Xpot that are involved in interacting tRNA (magenta balls) and with Ran (blue balls). (d) Transport pathway of tRNA from nucleus to cytoplasm. (e) Overview of this study. Nomenclature used for the simulated systems has been indicated. Different starting complexes and their corresponding cartoon representations are shown: Xpot, gray; Ran, blue; GTP/GDP, green circle and box, respectively; tRNA, magenta. To see this figure in color, go online.

the nucleus followed by transport through nuclear membrane to cytoplasm (15). Once in the cytoplasm, GTPase activating proteins (RanGAP1) hydrolyze RanGTP to RanGDP, which initiates a cascade of structural changes and rearrangements, leading to tRNA release by Xpot (Fig. 1 d). Studies illustrating the critical residues in tRNA involved in identification and subsequent contact by Xpot/ Los1p provide valuable information about the kind of interactions that exist between Xpot and tRNA (13,16). Apart from Xpot, Exportin-5 (Exp-5) has also been reported to transport tRNA (17,18). Exp-5 is a bonafide transport receptor dedicated to pre-miRNA nucleocytoplasmic transport, whereas Xpot is dedicated solely for transporting tRNA. Despite the availability of crystal structures of nuclear and cytosolic states of Xpot, the molecular details regarding the events after GTP hydrolysis leading to tRNA release

from Xpot remain elusive. The immediate molecular imprints, resulting from RanGTP hydrolysis in the cytoplasm on Xpot-RanGTP-tRNA ternary complex, have not been studied until now. On the other hand, the physical aspects of the structural flexibility of cargo-free Xpot/Los1p and other Karyopherins like Cse1p have been investigated by combining different techniques such as coarse-grained modeling and elastic network models (ENMs), which suggest that C-terminal HEATs 16–19 are more flexible compared to N-terminal and central repeats (19–21). Although ENMs reveal the flexibility of Xpot and Cse1p, this knowledge is limited to a highly coarse-grained representation of these proteins. The molecular determinants that are responsible for imparting the inherent flexibility to Xpot after tRNA release exist within the spatial arrangement of individual HEAT repeats. These arrangements and

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atomic scale dynamics cannot be captured reliably by ENM-based models. The structural information related to the dynamics of binary Xpot forms bound either to Ran or tRNA alone is also not available until now. This information could provide us the knowledge about the relatively different interaction patterns in the Xpot binary and ternary forms and an insight into the hierarchy of complex formation in the export-bound Xpot-RanGTP-tRNA complex. These unresolved issues can be addressed by all-atom molecular dynamics simulation (cMD) and its variants, such as accelerated MD methods (aMD). The aMD approach enormously enhances the conformational sampling space of a biomolecule and provides reliable estimates of dominant modes of motion and conformational clusters (22,23). Structural studies carried out on Karyopherin proteins using MD schemes until now are mostly done on proteins like Impb and CRM1 at very restricted timescales with a limited focus on fewer aspects of their conformational dynamics (24,25) In this study, a combination of microseconds long cMD and aMD techniques has been used to understand the structural dynamics of Xpot during nuclear to cytosolic state transition after cargo release. We addressed the issue of relative flexibility of Xpot in different complexed states and the contribution of individual HEAT repeat toward it. An emphasis was laid on characterizing the distinct structural and functional roles played by A- and B-helices of Xpot and the role that this distinction might play in quality control of the tRNA transport/biogenesis cycle (26). Structural features on the surface of cargo-bound Xpot were highlighted, which were thought to be involved in this quality control step and could act as potential binding sites for phenylalanine-glycine (FG)-rich nucleoporins. We further compared the changes in key interactions at Xpot/Ran and Xpot/ tRNA interface occurring after RanGTP hydrolysis and presented a model of the chain of events, which could lead to tRNA release from Xpot. The conformational space explored by all the Xpot complexes was comprehensive and provided statistically reliable estimates of various observables.

MATERIALS AND METHODS Description of initial molecular systems and MD protocol: cMD The initial atomic coordinates for all the systems except Xpot free (cyto˚ resolution x-ray crystal structure. solic/open form) were taken from 3.2 A (Protein Data Bank (PDB): 3ICQ). For the Xpot free (open) system, coor˚ resolution structure (PDB: 3IBV). All the dinates were taken from 3.1 A missing loop residues in Xpot closed and open state crystal structures were added using the FALC-Loop server (27,28). The crystal structure of ternary Xpot-RanGTP-tRNA complex lacks the anticodon stem-loop region from residues 29 to 41 in the tRNA chain and Ran’s C-terminal region (residues 180–219) (14). For modeling the missing anticodon loop, we superposed the tRNA chain from PDB: 3ICQ onto the high-resolution crystal ˚ resolution). Missing structure of Phe-tRNA from yeast (PDB: 1EHZ, 1.93 A coordinates were then substituted using PDB: 1EHZ anticodon loop seg-


ments. For setting up the simulation of Xpot-RanGDP binary and ternary complexes, the g-phosphate group was deleted from GTP to generate a corresponding GDP molecule. After modeling the missing regions, a short 200 steps vacuum minimization with mild restraints on all nonhydrogen atoms was done to remove any steric clashes generated during manual addition of these missing atoms. Subsequently, different files were extracted from this minimized complex for generating starting coordinates for various molecular complexes. Fig. 1 e and Table 1 show the details of all the systems investigated in this study and the nomenclature used for them. The total time for which free Xpot forms were subjected to MD was 2.2 ms (cMDþaMD, excluding bound forms), yielding an extensive conformational ensemble. We used the AMBER12 (29) suite of programs with an ff12SB set of force fields (30) for all the simulations. Parameters for modified tRNA nucleotides were taken from (31). All hydrogen atoms were added using the LEaP module of AMBER. All the systems were first neutralized using minimal counterion conditions with Naþ ions and explicit Naþ and Cl counter ions were further added to maintain an overall salt concentration of 0.2 M. Water molecules based on TIP3P (32) model were added to solvate the systems. For Xponuc, XpoGTPbin, and XpoGDPbin, coordinates were collected at an interval of 1 ps. For remaining systems, coordinates were collected at each 2 ps time interval. Details of cMD protocol are provided in the Supporting Material.

aMD simulations The details of different aMD simulations carried out are given in Table 1 and the Supporting Material. We carried out aMD simulations for free Xpot forms as well as only Ran or tRNA bound binary forms in GTP liganded state. Values of different acceleration boost parameters are provided in the Supporting Material. A population-based reweighing method as described in (33) was used on principal components 1 and 2, although, we also tested reweighing based on Boltzmann distribution of boost potentials (34). Results from Maclaurin series expansion up to the 10th order were found to reproduce PC profiles closer to those obtained from population-based reweighting; however, due to large noise, we have represented data from population-based reweighing methods. Although two different replicate simulations were run for Xponuc aMD simulation, data only from replicate 2 was considered for subsequent

TABLE 1 Study No.

Description of Various Systems Established for the

System

Simulation

1 2 3 4 5 6 7 8

Xponuc Xpocyt XpoGTPbin XpoGDPbin XpoGTPter XpoGDPter Xpot-tRNAbin Xponuc

cMD cMD cMD cMD cMD cMD cMD aMD

9

Xponuc

aMD

10

Xpocyt

aMD

11

Xpocyt

aMD

12 13

XpoGTPbin Xpot-tRNAbin

aMD aMD

Starting Structure (PDB ID) x-ray (3ICQ) x-ray (3IBV) x-ray (3ICQ) x-ray (3ICQ) x-ray (3ICQ) x-ray (3ICQ) x-ray (3ICQ) final structure of 1 (replicate 1) structure from end of equilibration phase of 1 (replicate 2) final structure of 2 (replicate 1) structure from end of equilibration phase of 2 (replicate 2) final structure of 3 final structure of 7

Duration (ns) 600 400 300 300 200 200 200 300 300

300 300

200 200

Nucleocytoplasmic Export of tRNA principal component analysis (PCA), while for Xpocyt, both the replicates 1 and 2 were considered.

but only two orientation angles (q1 and q2) sufficed to describe changes in their spatial geometry.

Analysis of trajectories All trajectories were analyzed using the cpptraj module of AMBER and structures were visualized in PyMOL. The residue numbering in Ran in this work corresponds to the numbering in the crystal structure (PDB: 3ICQ). For estimating global motions, parameters distance (d), angle (q), and dihedral angle (d) were defined as follows: d, distance between center of mass (CoM) of HEAT repeats 1 and 19; q, angle formed by CoMs of HEATs 1, 10, and 19; d, virtual dihedral angle formed by CoMs of HEATs1–2, HEATs6–7, HEATs12–13, and HEATs18–19. Principal components and q-d histograms were calculated on the data from both cMD and aMD trajectories after applying suitable reweighting methods (33). The ConSurf (35,36) program was used for calculating residue-wise conservation in Xpot. Dynamic cross-correlation matrices (DCCM) (37) were calculated for backbone Ca, N, and C atoms and a residue-wise averaging was done, after fitting the last 40 ns trajectories onto the average structure from this time frame. Distance cut-off for identifying hydrogen bonds and salt bridges was kept ˚ , respectively. The lifetimes of various contacts between at 3.5 and 4 A Xpot-Ran and Xpot-tRNA were calculated after removing initial 10 ns trajectories. Only those interactions that were observed for >60% during the remaining simulation time were considered significant for further studies.

PCA Xpot experiences significant conformational dynamics in free form, which makes it a difficult system for PCA. The Cartesian coordinatebased PCA requires fitting of the trajectory on an average structure before covariance matrix could be calculated, whereas an analogous dihedral PCA approach alleviates this necessity (38). The 600 ns Xponuc cMD was aimed toward observing the structural change from nuclear to cytosolic form, whereas Xpocyt simulation was carried out to further characterize the dynamics. Hence, these two systems were complementary to each other. This provided us with an opportunity to perform both Cartesian and dihedral PCA for cMD and aMD trajectories of Xponuc/cyt simulations and look for overlap between different sampled regions. For Cartesian PCA, we calculated root mean-square deviation (RMSD) profiles for Ca atoms in Xponuc/cyt systems with respect to crystal structure of Xpot in cytosolic form (PDB: 3IBV) and extracted snapshots from those regions where the two profiles converged with each other (Fig S1). This structure was selected as a reference for fitting the two Xponuc and Xpocyt trajectories before calculation of covariance matrix based on only Ca atoms. The principal components PC1 and PC2 accounted for >60% coverage of all collective modes. This analysis was carried out for different segments of the simulations. The dihedral PCA analysis was performed using Gromacs (39).

Identification of structural hinges The structural hinges (see Fig. 3 a) were identified by calculating the angles formed between every three successive centers of mass of all individual HEAT repeats. See Fig. 3 c for a description of the analysis of lateral bending, curvature, and twist, which was done following the procedure described by Forwood et al. (40). Principal axes x, y, and z for each HEAT repeat were calculated using ptraj in AMBER taking only Ca atoms into consideration. Lateral bending is defined as the angle between axis x of repeats n and n þ 1, projected on the plane made by axes x and z. Curvature is between axis y, projected on the y-x plane and twist is between axis z, projected on the y-z plane. Angles between intra-HEAT helices A/B and all inter-HEAT A/A and B/B helices were calculated using a similar method

RESULTS AND DISCUSSION Cargo binding controls the conformational flexibility of Xpot Starting from a nuclear, closed state Xpot gradually assumed a completely open conformation through large movements in C-terminal HEATs 16–19 from residues 736 to 922 and N-terminal HEATs 1–2 (Fig. 2, a and b). A major transition from nuclear to cytosolic form in Xponuc was achieved during the initial 50 ns itself where the RMSD ˚ to 13.5 A ˚ . After of Ca atoms rose rapidly from initial 0.5 A ˚ 80 ns the deviation maintained an average value of 16 A ˚ (SD 5 0.9 A) (Fig. S2). C-terminal repeats H16–H19 contributed significantly to this opening because after removing residues 731 to 974, the RMSD profile was much more stable and uniform with an average value of ˚ (SD 5 2A ˚ ). Root mean-square fluctuations (RMSFs) 6.7 A were least for the middle region comprising HEAT repeats H7–H15 and progressively increased for repeats H16– H19, with highest RMSFs displayed by the three C-terminal capping helices. In the Xpocyt simulation, the structure displayed a sharp deviation from its starting geometry during initial 50 ns of the trajectory in which the RMSD value ˚ to 10.4 A ˚ and Xpot opened further increased from 0.6 A than observed in cytosolic state x-ray structure (Fig. S2). In this system, fluctuations in repeats H1–H5 were slightly lower than that observed in the corresponding region in the Xponuc simulation. The residue fluctuations in C-terminal repeats H16–H19 were also significantly reduced in Xpocyt. These observations illustrate that after cargo release, Xpot can experience significant motions and disorder both in N- and C-terminal regions. Cargo binding thus facilitates in stabilizing the geometry and conformation of the export protein, suggesting that the crystal structure of Xpot in cytosolic state may not represent a true picture of the global conformation of Xpot after cargo release. Instead, the cargo free form might consist of an equilibrium structure, representing distinct conformational ensembles. The C-terminal region of Xpot in XpoGTP/GDPbin complexes showed higher RMSF as compared to N-terminal. This was in accordance with the fact that Ran protein binds Xpot extensively through HEATs H1–H4, which results in a stable N-terminal region. This was in contrast to XpottRNA binary complex where repeats 1–3 showed larger fluctuations than C-terminal repeats. Presence of Ran also caused a more gradual opening of Xpot in XpoGTP/GDPbin simulations than Xponuc. In XpoGTP/GDPter forms, both Xpot and Ran protein remained stable and per residue fluctuations were least (Figs. 2 a and S2). The anticodon arm of tRNA does not bind to either Xpot or Ran and remains solvent exposed; hence, tRNA nucleotides in this region


Gupta et al.

FIGURE 2 Global conformational changes associated with Xpot in different complexes. (a) RMSFs in Xpot in different simulated systems. Residues constituting HEAT repeats are indicated at top in green boxes. (Inset) Distribution of RMSD values among different systems. (b) Superposition of Xpot structure in nuclear, closed conformation (gray) on the final open structure (rainbow). (c) Schematic representation of angle and dihedral parameters. (d) Temporal evolution of angle (q) and dihedral(d) in cMD and aMD trajectories of free Xpot simulations. Dashed horizontal line indicates their crystal structure values. (e) 2D histograms of angle(q)-dihedral(d) indicating the conformational space explored by free Xpot in cMD and aMD simulations. Color map is on log scale. To see this figure in color, go online.

displayed higher RMSD. The difference in the conformational mobility of Xpot in various states was clearly highlighted from an RMSD histogram where a large fraction of structures from bound Xpot simulations fell mostly in the ˚ , whereas the structures from Xponuc/cyt RMSD bins 500 residues and our results provide an extremely good approximation of canonical ensemble behavior. Our aMD trajectories also emulated the features of the canonical trajectories. These simulations successfully captured the wide conformational space available to Xpot in completely free or Ran/tRNA bound forms alone. Although our XpoGTP/GDPbin simulations showed stable Xpot-Ran/tRNA binding and partial Xpot closing, transport requires the formation of a closed, tight Xpot geometry, which could be achieved only when both Ran and tRNA are bound (internal salt bridges formation in Xpot (HEATs H17/H4–H5 linker helix). Hydrolysis of RanGTP leads to increased RMSFs in switch-I region, which forms interactions with intrarepeat insertion of H17. Increased fluctuations in switch-I result in loss of these interactions, which in turn affect H17-tRNA (D-loop contacts) and probably provide the starting point for cargo dissociation. Loss of H17-D-loop contacts affects neighboring interactions between the TjC loop and Xpot (H16) as well. A second point from where tRNA release might be propagated is the junction formed by Ran helix A3, Xpot H4H5 linker helix, and intrarepeat insertion of H19 and tRNA acceptor stem, where the residence times of several hydrogen bonds and salt bridges were found to be reduced as a result of GTP hydrolysis. This work not only presents a comprehensive analysis on the conformational dynamics of Xpot, done at full atomic


Gupta et al.

scale; it also provides computationally and experimentally testable models of the chain of events that lead to cargo release after RanGTP hydrolysis. The series of interactions that are proposed to be critical at Xpot/tRNA and Xpot/Ran interface and are lost after GTP hydrolysis can be verified by using alanine scanning experiments, whereas an analogous hydrophile scanning (47) would shed light on weak electrostatic contacts between Xpot and tRNA. Fluorescence resonance energy transfer experiments can explain the hierarchy of Xpot-RanGTP-tRNA complex formation as well as monitoring the global conformational change in Xpot during open/closed state transition.

8. Moore, M. S. 1998. Ran and nuclear transport. J. Biol. Chem. 273:22857–22860. 9. Kutay, U., G. Lipowsky, ., D. Go¨rlich. 1998. Identification of a tRNAspecific nuclear export receptor. Mol. Cell. 1:359–369. 10. Arts, G. J., M. Fornerod, and I. W. Mattaj. 1998. Identification of a nuclear export receptor for tRNA. Curr. Biol. 8:305–314. 11. Hellmuth, K., D. M. Lau, ., G. Simos. 1998. Yeast Los1p has properties of an exportin-like nucleocytoplasmic transport factor for tRNA. Mol. Cell. Biol. 18:6374–6386. 12. Sarkar, S., and A. K. Hopper. 1998. tRNA nuclear export in Saccharomyces cerevisiae: in situ hybridization analysis. Mol. Biol. Cell. 9:3041–3055. 13. Arts, G. J., S. Kuersten, ., I. W. Mattaj. 1998. The role of exportin-t in selective nuclear export of mature tRNAs. EMBO J. 17:7430–7441. 14. Cook, A. G., N. Fukuhara, ., E. Conti. 2009. Structures of the tRNA export factor in the nuclear and cytosolic states. Nature. 461:60–65.

SUPPORTING MATERIAL Supporting Materials and Methods, thirteen figures, and one movie are available at http://www.biophysj.org/biophysj/supplemental/S0006-3495(16) 00161-2.

AUTHOR CONTRIBUTIONS S.K. and A.G. designed the project and S.K. modeled the starting structure. A.G. performed and analyzed the final simulation results. A.G., S.K., and M.B. wrote the article with inputs from all the authors. M.B. supervised the project and edited the manuscript.

15. Kuersten, S., G. J. Arts, ., I. W. Mattaj. 2002. Steady-state nuclear localization of exportin-t involves RanGTP binding and two distinct nuclear pore complex interaction domains. Mol. Cell. Biol. 22:5708– 5720. 16. Cleary, J. D., and D. Mangroo. 2000. Nucleotides of the tRNA D-stem that play an important role in nuclear-tRNA export in Saccharomyces cerevisiae. Biochem. J. 347:115–122. 17. Okada, C., E. Yamashita, ., T. Tsukihara. 2009. A high-resolution structure of the pre-microRNA nuclear export machinery. Science. 326:1275–1279. 18. Wang, X., X. Xu, ., Y. Wang. 2011. Dynamic mechanisms for premiRNA binding and export by Exportin-5. RNA. 17:1511–1528. 19. Hu, M., and B. Kim. 2011. Flexibility of the exportins Cse1p and Xpot depicted by elastic network model. J. Mol. Model. 17:1735–1741.

ACKNOWLEDGMENTS A.G. would like to acknowledge Prof. Jade K. Forwood for giving his consent for reproducing schematic used in Fig. 4 and Aditya Kumar for manuscript reading and suggestions. We thank Supercomputer Education and Research Center, IISc for providing the computing resources and Department of Biotechnology (DBT, India) for funding. M.B. is a recipient of the J. C. Bose National Fellowship of Department of Science and Technology (DST), India.

SUPPORTING CITATIONS References (48–53) appear in the Supporting Material.

20. Hu, M. W., C. F. Hsu, and B. Kim. 2014. The intrinsic dynamics of Cse1p and Xpot elucidated by coarse-grained models. Comput. Biol. Chem. 48:45–54. 21. Nozawa, K., R. Ishitani, ., O. Nureki. 2013. Crystal structure of Cex1p reveals the mechanism of tRNA trafficking between nucleus and cytoplasm. Nucleic Acids Res. 41:3901–3914. 22. Hamelberg, D., J. Mongan, and J. A. McCammon. 2004. Accelerated molecular dynamics: a promising and efficient simulation method for biomolecules. J. Chem. Phys. 120:11919–11929. 23. Pierce, L. C. T., R. Salomon-Ferrer, ., R. C. Walker. 2012. Routine access to millisecond time scale events with accelerated molecular dynamics. J. Chem. Theory Comput. 8:2997–3002. 24. Zhao, C. L., S. H. Mahboobi, ., M. R. K. Mofrad. 2014. The interaction of CRM1 and the nuclear pore protein Tpr. PLoS One. 9:e93709. 25. Zachariae, U., and H. Grubmu¨ller. 2008. Importin-b: structural and dynamic determinants of a molecular spring. Structure. 16:906–915.

REFERENCES

26. Lipowsky, G., F. R. Bischoff, ., D. Go¨rlich. 1999. Coordination of tRNA nuclear export with processing of tRNA. RNA. 5:539–549.

1. Ko¨hler, A., and E. Hurt. 2007. Exporting RNA from the nucleus to the cytoplasm. Nat. Rev. Mol. Cell Biol. 8:761–773.

27. Lee, J., D. Lee, ., C. Seok. 2010. Protein loop modeling by using fragment assembly and analytical loop closure. Proteins. 78:3428–3436.

2. Mattaj, I. W., and L. Englmeier. 1998. Nucleocytoplasmic transport: the soluble phase. Annu. Rev. Biochem. 67:265–306.

28. Ko, J., D. Lee, ., C. Seok. 2011. The FALC-Loop web server for protein loop modeling. Nucleic Acids Res. 39:W210–W214.

3. Groves, M. R., N. Hanlon, ., D. Barford. 1999. The structure of the protein phosphatase 2A PR65/A subunit reveals the conformation of its 15 tandemly repeated HEAT motifs. Cell. 96:99–110.

29. Pearlman, D. A., D. A. Case, ., P. Kollman. 1995. AMBER, a package of computer programs for applying molecular mechanics, normal mode analysis, molecular dynamics and free energy calculations to simulate the structural and energetic properties of molecules. Comput. Phys. Commun. 91:1–41.

4. Chook, Y. M., and G. Blobel. 1999. Structure of the nuclear transport complex karyopherin-beta2-Ran GppNHp. Nature. 399:230–237. 5. Cingolani, G., C. Petosa, ., C. W. Mu¨ller. 1999. Structure of importinbeta bound to the IBB domain of importin-alpha. Nature. 399:221–229.

30. Hornak, V., R. Abel, ., C. Simmerling. 2006. Comparison of multiple Amber force fields and development of improved protein backbone parameters. Proteins. 65:712–725.

6. Neuwald, A. F., and T. Hirano. 2000. HEAT repeats associated with condensins, cohesins, and other complexes involved in chromosomerelated functions. Genome Res. 10:1445–1452.

31. Bryce, R. A. AMBER Parameter Database. http://www.pharmacy. manchester.ac.uk/bryce/amber. Accessed June 20, 2014.

7. Andrade, M. A., C. Petosa, ., P. Bork. 2001. Comparison of ARM and HEAT protein repeats. J. Mol. Biol. 309:1–18.


32. Jorgensen, W. L., J. Chandrasekhar, ., M. L. Klein. 1983. Comparison of simple potential functions for simulating liquid water. J. Chem. Phys. 79:926–935.

Nucleocytoplasmic Export of tRNA 33. Sinko, W., Y. Miao, ., J. A. McCammon. 2013. Population based reweighting of scaled molecular dynamics. J. Phys. Chem. B. 117:12759–12768.

44. Partridge, J. R., and T. U. Schwartz. 2009. Crystallographic and biochemical analysis of the Ran-binding zinc finger domain. J. Mol. Biol. 391:375–389.

34. Miao, Y., W. Sinko, ., J. A. McCammon. 2014. Improved reweighting of accelerated molecular dynamics simulations for free energy calculation. J. Chem. Theory Comput. 10:2677–2689. 35. Landau, M., I. Mayrose, ., N. Ben-Tal. 2005. ConSurf 2005: the projection of evolutionary conservation scores of residues on protein structures. Nucleic Acids Res. 33:W299–W302.

45. Nilsson, J., P. Askjaer, and J. Kjems. 2001. A role for the basic patch and the C-terminus of RanGTP in regulating the dynamic interactions with importin beta, CRM1 and RanBP1. J. Mol. Biol. 305:231–243. 46. Vetter, I. R., A. Arndt, ., A. Wittinghofer. 1999. Structural view of the Ran-Importin beta interaction at 2.3 A resolution. Cell. 97:635–646.

36. Ashkenazy, H., E. Erez, ., N. Ben-Tal. 2010. ConSurf 2010: calculating evolutionary conservation in sequence and structure of proteins and nucleic acids. Nucleic Acids Res. 38:W529–W533.

47. Boersma, M. D., J. D. Sadowsky, ., S. H. Gellman. 2008. Hydrophile scanning as a complement to alanine scanning for exploring and manipulating protein-protein recognition: application to the Bim BH3 domain. Protein Sci. 17:1232–1240.

37. Hu¨nenberger, P. H., A. E. Mark, and W. F. van Gunsteren. 1995. Fluctuation and cross-correlation analysis of protein motions observed in nanosecond molecular dynamics simulations. J. Mol. Biol. 252:492–503.

48. Miao, Y., A. D. Caliman, and J. A. McCammon. 2015. Allosteric effects of sodium ion binding on activation of the m3 muscarinic g-protein-coupled receptor. Biophys. J. 108:1796–1806.

38. Altis, A., P. H. Nguyen, ., G. Stock. 2007. Dihedral angle principal component analysis of molecular dynamics simulations. J. Chem. Phys. 126:244111. 39. Pronk, S., S. Pa´ll, ., E. Lindahl. 2013. GROMACS 4.5: a highthroughput and highly parallel open source molecular simulation toolkit. Bioinformatics. 29:845–854. 40. Forwood, J. K., A. Lange, ., B. Kobe. 2010. Quantitative structural analysis of importin-b flexibility: paradigm for solenoid protein structures. Structure. 18:1171–1183. 41. Cansizoglu, A. E., and Y. M. Chook. 2007. Conformational heterogeneity of karyopherin beta2 is segmental. Structure. 15:1431–1441. 42. Isgro, T. A., and K. Schulten. 2005. Binding dynamics of isolated nucleoporin repeat regions to importin-beta. Structure. 13:1869–1879. 43. Isgro, T. A., and K. Schulten. 2007. Cse1p-binding dynamics reveal a binding pattern for FG-repeat nucleoporins on transport receptors. Structure. 15:977–991.

49. Miao, Y., S. E. Nichols, ., J. A. McCammon. 2013. Activation and dynamic network of the M2 muscarinic receptor. Proc. Natl. Acad. Sci. USA. 110:10982–10987. 50. Reyes, C. M., and P. A. Kollman. 1999. Molecular dynamics studies of U1A-RNA complexes. RNA. 5:235–244. 51. Reyes, C. M., and P. A. Kollman. 2000. Structure and thermodynamics of RNA-protein binding: using molecular dynamics and free energy analyses to calculate the free energies of binding and conformational change. J. Mol. Biol. 297:1145–1158. 52. Darden, T., D. York, and L. Pedersen. 1993. Particle mesh Ewald: an N [center-dot] log(N) method for Ewald sums in large systems. J. Chem. Phys. 98:10089–10092. 53. Ryckaert, J.-P., G. Ciccotti, and H. J. C. Berendsen. 1977. Numerical integration of the Cartesian equations of motion of a system with constraints: molecular dynamics of n-alkanes. J. Comput. Phys. 23:327–341.
