Binding of Disordered Peptides to Kelch: Insights from Enhanced

Subscriber access provided by TUFTS UNIV

Article

Binding of disordered peptides to Kelch: Insights from enhanced sampling simulations Trang Nhu Do, Wing-Yiu Choy, and Mikko Karttunen J. Chem. Theory Comput., Just Accepted Manuscript • DOI: 10.1021/acs.jctc.5b00868 • Publication Date (Web): 01 Dec 2015 Downloaded from http://pubs.acs.org on December 9, 2015

Just Accepted “Just Accepted” manuscripts have been peer-reviewed and accepted for publication. They are posted online prior to technical editing, formatting for publication and author proofing. The American Chemical Society provides “Just Accepted” as a free service to the research community to expedite the dissemination of scientific material as soon as possible after acceptance. “Just Accepted” manuscripts appear in full in PDF format accompanied by an HTML abstract. “Just Accepted” manuscripts have been fully peer reviewed, but should not be considered the official version of record. They are accessible to all readers and citable by the Digital Object Identifier (DOI®). “Just Accepted” is an optional service offered to authors. Therefore, the “Just Accepted” Web site may not include all articles that will be published in the journal. After a manuscript is technically edited and formatted, it will be removed from the “Just Accepted” Web site and published as an ASAP article. Note that technical editing may introduce minor changes to the manuscript text and/or graphics which could affect content, and all legal disclaimers and ethical guidelines that apply to the journal pertain. ACS cannot be held responsible for errors or consequences arising from the use of information contained in these “Just Accepted” manuscripts.

Journal of Chemical Theory and Computation is published by the American Chemical Society. 1155 Sixteenth Street N.W., Washington, DC 20036 Published by American Chemical Society. Copyright © American Chemical Society. However, no copyright claim is made to original U.S. Government works, or works produced by employees of any Commonwealth realm Crown government in the course of their duties.

Page 1 of 34

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

Journal of Chemical Theory and Computation

Binding of disordered peptides to Kelch: Insights from enhanced sampling simulations Trang Nhu Do,† Wing-Yiu Choy,‡ and Mikko Karttunen∗,¶ Department of Chemistry and Waterloo Institute for Nanotechnology, University of Waterloo, 200 University Avenue West, Waterloo, ON, Canada N2L 3G1, Department of Biochemistry, University of Western Ontario, 1151 Richmond Street, London, ON, Canada N6A 3K7, and Department of Mathematics and Computer Science & Institute for Complex Molecular Systems, Eindhoven University of Technology, P.O. Box 513, MetaForum, 5600 MB, Eindhoven, The Netherlands E-mail: [email protected]

Abstract Keap1 protein plays an essential role in regulating cellular oxidative stress response and is a crucial binding hub for multiple proteins, several of which are intrinsically disordered proteins (IDP). Among Kelch’s IDP binding partners, NRF2 and PTMA are the two most interesting cases. They share a highly similar binding motif; however, NRF2 binds to Kelch with a binding affinity of approximately 100 fold higher than that of PTMA. In this study we perform an exhaustive sampling composed of ∗

To whom correspondence should be addressed Department of Chemistry and Waterloo Institute for Nanotechnology, University of Waterloo, 200 University Avenue West, Waterloo, ON, Canada N2L 3G1 ‡ Department of Biochemistry, University of Western Ontario, 1151 Richmond Street, London, ON, Canada N6A 3K7 ¶ Department of Mathematics and Computer Science & Institute for Complex Molecular Systems, Eindhoven University of Technology, P.O. Box 513, MetaForum, 5600 MB, Eindhoven, The Netherlands †

1 ACS Paragon Plus Environment


1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

6-µs well-tempered metadynamics and 2-µs unbiased molecular dynamics (MD) simulations aiming at characterizing the binding mechanisms and structural properties of these two peptides. Our results agree with previous experimental observations that PTMA is remarkably more disordered than NRF2 in both free and bound states. This explains PTMA’s lower binding affinity. Our extensive sampling also provides valuable insights into the vast conformational ensembles of both NRF2 and PTMA, supports the hypothesis of coupled folding-binding and confirms the essential role of linear motifs in IDP binding.

1

Introduction

Keap1 (Kelch-like ECH-associated protein 1) is part of the body’s “cellular rapid response team” in protecting the cells against electrophilic and oxidative stresses, both of which are associated with cancer, 1,2 neurodegenerative 3 and other diseases. 4 Together with NRF2 (Nuclear factor erythroid 2-related factor 2), Keap1 forms a key pathway in the cellular defense against such stresses. Under normal conditions, Keap1 suppresses NRF2 activity by targeting it for ubiquitination-mediated degradation. When the cell is under oxidative stress, Keap1 is modified and the subsequent conformational changes lead to the release of NRF2, allowing NRF2 to translocate to the nucleus and to trigger cytoprotective gene activations. 5–9 The binding of Keap1 to NRF2 is mediated by the C-terminal Kelch domain of Keap1 and the N-terminal Neh2 domain of NRF2. 10 Since this protein-protein interaction is crucial for regulating the cellular responses to oxidative stress, it is an attractive target for therapeutic development against inflammation and cancer. 11 The Kelch domain also binds to several other proteins, such as p62, 12 WTX, 13 FAC1, 14 PALB2, 15 and PTMA(Prothymosin alpha). 16–18 Most of these proteins directly interfere with the NRF2-Keap1 interaction through competitive binding to the Kelch domain of Keap1, thus promoting cytoprotective NRF2dependent gene transcription. Importantly, many of these interactions are convergence points 2 ACS Paragon Plus Environment

Page 2 of 34

Page 3 of 34

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


between NRF2 and other signaling pathways such as apoptosis and autophagy. 19 Interestingly, many of these targets of Keap1 are intrinsically disordered, making Keap1 a hub for disordered proteins in the protein-protein interaction network. Dissecting the mechanisms by which Keap1 binds to these disordered partners is paramount for understanding how this hub protein functions through recognizing different partners. Like NRF2, PTMA is an intrinsically disordered. In addition, like NRF2, PTMA binds competitively to the Kelch domain of Keap1. 18 Although not all functions of PTMA have been discovered, 20 it is known to be involved in apoptosis and cell growth. 16,21 IDPs differ from the textbook globular proteins in that they do not have a unique folded state but instead have a statistical ensemble of configurations with a varying number of binding motifs. 22–24 This imposes considerable difficulties for their experimental 25 and computational characterization. 26–29 In this work, we focus on the binding mechanisms of NRF2-Keap1 and PTMA-Keap1 and characterization of their conformational ensembles. The N-terminal Neh2 domain of NRF2 harbors two Keap1-binding motifs: the low-affinity DLG motif and the high-affinity ETGE motif. The ETGE motif of NRF2 shares a high sequence similarity with the Keap1-binding motif of PTMA, however, their affinities to Kelch somehow differ by ∼100 fold. Through the simulations performed, we seek to understand factors that govern the binding affinity of different partners to Keap1. It has been observed that IDPs can prefold upon approaching their binding partners. 30,31 This coupled folding and binding process is well-known to be widely employed in biology but still under-explained. 30–35 One of the reasons why coupled folding and binding is not thoroughly understood is that it is nearly impossible to experimentally characterize IDPs in their native unbound states. 36 When not binding to a target, IDPs exist as vast dynamic ensembles of conformations that cannot be determined from an ensemble average. 37,38 Molecular simulation can be used to complement experimental approaches and help achieving an explicit atomistic description of IDPs and their conformational transitions. However, com-



1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

puter simulation is also facing significant challenges when describing IDPs. Two of the major challenges are force-fields and their accuracy and limitations in comprehensive sampling the conformational ensemble. 38 Quality of a folding simulation is essentially governed by the quality of the force field. 39 Significant improvements of force field accuracy has been made yielding a proper balance between different secondary structures. 40–45 An recent extensive force field comparison carried out by some of us for the case of IDPs 46 together with other studies 43,47 show that the combination of the atomistic AMBER ff99SB force field 48 and recent corrections, namely the “ILDN” side-chain torsion reparametrization 49 and the helix-coil transition balance optimization, 40 provide a good agreement with experimental data. Since IDPs’ sequences are more dominant by polar and/or charged residues, 50 the ionic solution is expected to have important impacts on the conformational ensembles and state transitions of IDPs; 51 an explicit solvent representation is crucial for accurately describing IDPs’ conformational changes. The high complexity of the conformational space of IDPs poses a significant challenge to conventional unbiased MD simulation of affordable timescales. To alleviate this sampling challenge, several acceleration methods have been introduced and they have led to important progress in enhancing the sampling of the conformational space of proteins. 52–62 Among these, metadynamics technique 62 appears as a suitable enhanced sampling method to explore the conformational space of IDPs because it does not require prior knowledge of the end states or transition paths. Another strength of metadynamics is its ability to bias on more than one collective variables (CV), thus allowing a multi-dimensional reconstruction of the underlying free-energy profile. A recent metadynamics variant, the socalled well-tempered metadynamics, 63 can potentially provide a powerful exploration of the free energy landscape by allowing tuning the probability of barrier crossing and controlling the convergence of the simulations. The conformational ensembles obtained from our well-tempered metadynamics are carefully validated against the available X-ray crystallography data. 16,64 Our simulations repro-


Page 4 of 34

Page 5 of 34

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


duce the X-ray bound state configurations of the NRF2-Kelch and PTMA-Kelch complexes and provide good agreement with our previous NMR data. 65,67 Our results render support to the hypothesis of coupled folding-binding in the case of NRF2 and confirm the essential role played by the IDP linear motifs.

2

Materials and methods

2.1

Computational setup

Well-tempered metadynamics simulations of the two systems (NRF2-Kelch and PTMAKelch) starting from the unbound structures were carried out to characterize the binding energetics and structural properties. In the starting structures, NRF2 and PTMA peptides encoding the Kelch-binding motif were placed in a random orientation with their center of mass at 3.5 nm away from the binding pocket of Kelch. The starting configuration of NRF2 was retrieved from one of our previously published enhanced sampling simulations. 68 We chose the configuration with the lowest RMSD (0.2 ˚ A) with respect to the human NRF2 bound-state-like X-ray structure (PDB code: 2FLU). 64 The starting configuration of PTMA was retrieved similarly with the RMSD of 0.3 ˚ A with respect to the mouse PTMA boundstate-like X-ray structure (PDB code: 2Z32). 16 Each system was simulated by well-tempered metadynamics for 3 µs, during which several binding and unbinding events occurred. The structures with the smallest RMSD with respect to the X-ray structures were then used as starting configurations for the unbiased MD simulations, aiming at examining the stability of the bound-state-like configurations obtained from previous metadynamics runs. Each complex was simulated for 1 µs, during which the peptides remained bound to the Kelch protein. Each system was solvated in a dodecahedral box of explicit TIP3P 69 water in which the distance from any atom of the molecules to the box boundary is always greater than 2.3 nm. The large simulation box ensures that the protein and the peptides can be separated 5 ACS Paragon Plus Environment


1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

with a distance beyond 5.5 nm. Each simulation box contained ≈ 100, 000 atoms. An excess ion concentration of 150 mM was set to reproduce physiological conditions, resulting in 93 Cl− anions and 101 (104) Na+ cations for the system of NRF2-Kelch (PTMA-Kelch). The ff99SB*-ILDN force field 43 (a combination of the AMBER ff99SB force field 48 with recent corrections of the “ILDN” side-chain torsion parameters 49 and the helix-coil transition balance optimizations 40 ) was used to simulate the Kelch protein, and the NRF2 and PTMA peptides. This combination of force field improvements has been shown to work well for a large variety of proteins including IDPs. 43,46,47,68 The ff99SB*-ILDN force field corrected by a recent ion reparametrization 70 was used for the Na+ and Cl− ions to avoid imbalanced cation-anion interactions and ion crystallization at high concentrations. 71–73 All simulations were performed with GROMACS 4.6. 74 The PLUMED 1.3 plug-in 75 was used in well-tempered metadynamics simulations. The Particle-Mesh Ewald method 76,77 was employed with a real space cutoff of 1.2 nm. The same cutoff was also used to treat van der Waals interactions. The simulation time-step was 2 fs and temperature was kept constant at 310 K using the v-rescale algorithm. 78 Pressure was kept at 1 atm in NpT-ensemble simulations using the Parrinello-Rahman barostat. 79

2.2

Collective variables

Three CVs were employed in each metadynamics simulation for the binding of NRF2 and PTMA peptides to Kelch: (i) the distance between the center of mass of the peptide and the experimental binding pocket of Kelch. 16,64 (ii) the contact map, defined as the number of specific contacts, which in this case are the experimental hydrogen bonds between the peptide and Kelch. Taking into consideration structural fluctuations, we count “weak” hydrogen bonds with a maximum donor-acceptor distance of 4 ˚ A in the X-ray structures 16,64 and find 14 (9) hydrogen


Page 6 of 34

Page 7 of 34

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


bonds for NRF2-Kelch (PTMA-Kelch) complex (see Table 1). It is worth noting that these hydrogen bonds are identified using the g hbond tool of GROMACS with the presence of explicit solvent, which results in less hydrogen bonds than other tools or servers purely based on geometries may show. (iii) antibetarmsd (PLUMED nomenclature 75 ), counting the number of pairs of the 3-residue segments in the peptide that are similar to an ideal antiparallel β block. 80 The ideal antiparallel β block is defined by the average structure of all antiparallel structures from the PDB database. The details of how this CV is calculated can be found in Ref. 80 One of our recent studies used this CV to gain important structural knowledge of the NRF2 peptide. 68 Table 1: “Weak” hydrogen bonds with a maximum donor-acceptor distance of 4 ˚ A in the 16,64 X-ray structures for NRF2-Kelch and PTMA-Kelch complexes. Common Kelch residues forming contacts with both NRF2 and PTMA are marked in red. 1 2 3 4 5 6 7 8 9 10 11 12 13 14

NRF2 GLU8-O GLU9-O GLU9-OE GLU9-OE GLU9-OE GLU9-OE GLU9-OE THR10-O GLU12-OE GLU12-OE GLU12-OE GLU12-OE PHE13-O PHE13-N

Kelch GLN530-NE SER555-OG SER507-OG ARG415-NH1 ARG415-NH2 ARG483-NH ARG483-NE SER602-OG ASN382-ND ARG380-NH ARG380-NE SER363-OG ASN 382-ND TYR334-OH

PTMA ASN7-N GLU9-OE GLU9-O GLU9-O GLY11-N GLU12-OE GLU12-OE GLN13-N GLN13-O

Kelch TYR572-OH TYR572-N ASP573-N HIS575-N TYR572-OH ARG380-NH ASN382-ND TYR334-OH ASN382-ND

Gaussian potentials with an initial height of 2 kJ/mol were added to the time-dependent potential every 5 ps. The bias factor for rescaling the Gaussian height following the welltempered metadynamics scheme was 15. The Gaussian widths were 0.1, 0.5, and 0.5 for the CVs 1-3, respectively. 7 ACS Paragon Plus Environment


1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

3

Results and discussions

3.1

Convergence of metadynamics simulations

In theory, if a well-tempered metadynamics simulation is sufficiently long, the fluctuations of the free-energy difference between any two metastable states become progressively damped toward the correct value. 63 This is how well-tempered metadynamics is more advantageous than standard metadynamics in controlling convergence. Figure 1 shows the time evolution of the free-energy difference between the unbound states and the experimental bound states of the two complexes, NRF2-Kelch (solid green line) and PTMA-Kelch (dashed blue line). The unbound states are simply defined as having no contacts between the peptides and Kelch. Defining the experimental bound states is trickier. The peptides are highly disordered even in their bound states and the experimental structures only represent one configuration in a large unknown bound-state ensemble. Thus, demanding the peptides to exhibit all of the reference contacts to be considered being in the experimental bound states is too strict a criterion. This can result in a severe underestimation of the experimentally observed bound states. We thus deem the peptides to be in the X-ray-like bound states as long as at least one X-ray contact (second CV) is present. This ensures that the peptides fall into the X-ray binding pocket and yet tolerate their structural plasticity as their intrinsic nature, which in turn generates a relevant bound-state-like ensemble to be further assessed. The criterion of differentiation also ensures that the unbound states and experimental bound states are mutually exclusive. After 1.5 µs, the free-energy difference converges to −12.98 ± 0.04 kcal/mol and −3.82 ± 0.03 kcal/mol for the NRF2-Kelch and PTMA-Kelch complexes, respectively. The errors are calculated as the standard error of the mean. Quantitatively, the estimated binding free energies are different from the available experimental values, which are −10.40 ± 0.03 and −7.60 ± 0.03 kcal/mol for NRF2 and PTMA, respectively. 65 Starting from completely unbound configurations and carrying on for more than 1.5 µs after the free-energy difference reaches convergence in both simulations, we are


Page 8 of 34

Page 9 of 34

thus confident that our well-tempered metadynamics simulations provide sufficient statistical data for the dissociation/association events to be further analyzed. 40

NRF2-Kelch PTMA-Kelch

30 Free energy (kcal/mol)

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


20 10 0 -10 -20 -30 0

0.5

1

1.5 Time (µs)

2

2.5

3

Figure 1: Free-energy difference between unbound and experimental bound states as a function of time of complexes NRF2-Kelch (solid green line) and PTMA-Kelch (dashed blue line). After 1.5 µs, the free-energy difference converges to a stable value for both complexes.

3.2

Structural features of the disordered peptides

Figure 2 shows the free energy as a function of the number of antibetarmsd (CV3) given different conditions: unbound states, bound states, and experimental bound states. The unbound and experimental bound states are defined as mentioned in the previous section. The bound states contain structures with at least one contact between the peptides and Kelch regardless of the contacts being experimentally observed contacts or not. When not bound to Kelch, NRF2 slightly prefers folding into a short hairpin (antibetarmsd ≈ 1) rather than staying in a disordered state (antibetarmsd < 1). On the contrary, PTMA prefers being disordered in its unbound configuration. The experimental bound states of NRF2 are observed to be less disordered than its general bound states, while for PTMA, the general bound states and experimental bound states are nearly identical in terms of secondary structure distribution. These observations agree with previous findings that PTMA is more disordered than NRF2 in both bound and unbound states. 65,67 9 ACS Paragon Plus Environment


1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

Figure 2: Free energy as a function of the number of antibetarmsd (CV3) given different conditions: unbound states, bound states, and experimental bound states plotted for the NRF2-Kelch (a) and PTMA-Kelch (b) systems.


Page 10 of 34

Page 11 of 34

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


Figure 3 presents the normalized RMSD distributions with respect to the corresponding X-ray structures 16,64 of NRF2 (green line) and PTMA (dotted blue line) in their experimental bound states (a) and unbound states (b). The RMSD distribution is properly reweighted to remove the bias effect using the reweighting technique introduced in Ref. 81 During welltempered metadynamics simulations, RMSD distributions of NRF2 do not show significant differences between unbound and experimental bound states. In contrast, the RMSD distributions of PTMA span through different large regions in two states. This suggests that PTMA exhibits more configurational changes than NRF2 in both unbound and experimental states. Figure 3 reveals another important feature: both peptides statistically adopt structures with lower RMSD (with respect to their experimental reference structures) in their unbound states than in their experimental bound states. This is interesting but not surprising and can be explained by the fact that the peptides are more free to explore a wider range of configurations in their unbound states, and thus have a better chance to frequently visit a certain configuration than in their bound states although this configuration is supposed to be a bound-state-like configuration. The result is not contradictory with previous findings 16,64,65,68 and further suggests that the X-ray bound structure may be a highly populated structure also in the unbound states. Both peptides are intrinsically disordered and have not been fully characterized in either unbound or bound states. Therefore, this observation provides a valuable contribution to the reconstruction of the configurational space of both peptides.

3.3

Contacts at the interfaces

On the surface of Kelch in Figure 4, green and blue mark the regions where Kelch does not have any hydrogen bonds with NRF2 and PTMA, respectively. The color scale from white to red represents the number of hydrogen bonds formed between Kelch and the peptides ranging from 0 to 3.5. The number of hydrogen bonds was carefully reweighted to remove the 11 ACS Paragon Plus Environment


1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

Figure 3: Normalized reweighted RMSD distributions with respect to the X-ray structures of NRF2 (solid green line) and PTMA (dotted blue line) in their experimental bound states (a) and unbound states (b).


Page 12 of 34

Page 13 of 34

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


effect of bias from well-tempered metadynamics simulations using the reweighting technique introduced in Ref. 81 During multiple unbinding and binding events, the peptides dissociate from Kelch and then re-associate with different surface residues of Kelch. According to our analysis, no binding occurred at the bottom of Kelch, some binding occur on the sides of Kelch (panels b, c, e, and f in Figure 4), and most binding sites were found around the pocket at the top of Kelch, which is also the experimental binding pocket (panels a and d). The top view panels show that upon binding to Kelch, NRF2 (a) interacts with fewer surface residues than PTMA (d). Fewer contacts together with lower binding free energy (Figure 1) suggests that NRF2 has not only a higher binding affinity but also a better defined interface with Kelch compared to PTMA. The bars in Figure 5 indicate the average number of hydrogen bonds per residue of NRF2 (green) and PTMA (blue) upon binding to Kelch together with the standard errors. Highlighted with red strokes in the x-axis show the linear motif, i.e., the “DEETGE” and “NEENGE” sequences of NRF2 and PTMA, respectively. IDPs are observed to bind to their targets using a short sequence typically containing 6 amino acids. This consecutive sequence is called the linear motif and is a distinct binding feature of IDPs in contrast to well-structured proteins. 65,66,82,83 Figure 5 shows that the most active binding sites of NRF2 and PTMA are located at the residues 8 and 9, respectively. Both of them are glutamic acids (E) and belong to the linear motif. The results of PTMA agrees well with our experimental observation that GLU9 of PTMA is essential for Kelch binding. 67 We also observe a general trend that NRF2 has stronger contacts than PTMA in the first half of the sequence while PTMA appears to bind better than NRF2 in the second half. It is worth noting that NRF2 exhibits lower errors than does PTMA. This again confirms that the interface of NRF2 with Kelch is better defined than that of PTMA.



1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

Figure 4: Different views of Kelch surface in case of NRF2 binding (green) and PTMA binding (blue). The color scale represents the number of hydrogen bonds between Kelch and the peptides.


Page 14 of 34

Page 15 of 34

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


Figure 5: Average number of hydrogen bonds per residue of NRF2 (green) and PTMA (blue) formed with Kelch. The bars are accompanied with standard errors. Lower errors in NRF2 confirms that NRF2 forms a better defined interface with Kelch.

3.4

Assessing the stability of the complexes

From each metadynamics trajectory the configuration with the lowest RMSD with respect to the X-ray structures 16,64 was retrieved and used as the starting structure for an unbiased MD simulation. The RMSD of the selected structures was 0.24 nm for both the NRF2-Kelch and PTMA-Kelch complexes. The unbiased MD simulations are used to investigate the stability of the X-ray-like configurations obtained from the well-tempered metadynamics simulations and to further analyze the structural as well as the binding properties of each peptide. Figure 6 shows the RMSD distribution of each peptide with respect to its available X-ray bound-state-like structure. Compared to NRF2, PTMA is more flexible and exhibits more changes from its initial X-ray-like configuration. The result is consistent with our previous experimental finding, 67 which demonstrates that PTMA retains a high degree of flexibility upon binding to Kelch. Figure 7 shows the calculated B-factor per residue of NRF2 and PTMA using surface and color representations. B-factor indicates the relative fluctuation of the structure. Higher Bfactor atoms fluctuate more and generally belong to the more flexible regions. In the bound states, both peptides lose a considerable amount of their structural flexibility compared to their free states. NRF2 appears to be more rigid than PTMA in the binding region. This 15 ACS Paragon Plus Environment


0.009

NRF2 PTMA

0.008 0.007 0.006 Density

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

0.005 0.004 0.003 0.002 0.001 0 0

0.2

0.4

0.6

0.8

1

1.2

RMSD (nm)

Figure 6: RMSD distribution of NRF2 (solid green line) and PTMA (dotted blue line) with respect to their X-ray structures in their bound states calculated from unbiased MD simulations. again confirms the β-hairpin conformation of NRF2 upon binding to Kelch and suggests that PTMA still behaves like a disordered peptide with some features of a short β-hairpin in its bound state. The result provides further evidence that PTMA forms a fuzzy complex with Kelch as we suggested previously. 67 To compare the configurations of the linear motifs of the two peptides, the [φ,ψ] normalized probability distribution of each residues in the motif was calculated from the unbiased MD simulations and then subtracted from each other. The differences in the [φ,ψ] probability distributions of the 6 pairs of the linear motifs of NRF2 and PTMA are plotted in Figure 8. The red and blue scales represent the probability distributions for NRF2 and PTMA, respectively. This plot again shows a higher structural flexibility of PTMA compared to NRF2. Indeed, while all residues from NRF2’s motif feature a single sharp peak on the [φ,ψ] plane, each residue from PTMA’s motif exhibit more than one broadened peak. Residues 8 and 10 from the two peptides have considerably similar [φ,ψ] distributions, suggesting that these two residues are important for the stability of the hairpin configurations of the peptides. Figure 9 shows the map of backbone hydrogen bonds of NRF2 (green, panel (a)) and PTMA (blue, panel (b)). The colorbar represents the average number of hydrogen bonds throughout the unbiased MD simulations and the yellow square marks the linear motif of the 16 ACS Paragon Plus Environment

Page 16 of 34

Page 17 of 34

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


Figure 7: B-factor per residue of NRF2 in the bound state (a) and free state (b). B-factor per residue of PTMA in the bound state (c) and free state (d). The peptides are shown in a surface representation. The color scale indicates how flexible the residues of each peptide are.



1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

Figure 8: The differences in [φ,ψ] probability distribution of each residue in the linear motif of NRF2 (red) and PTMA (blue). NRF2’s distributions have single sharp peaks while PTMA’s distributions are more broadened and less intensive.


Page 18 of 34

Page 19 of 34

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


peptides. Since we are interested in analyzing the hairpin configurations that both peptides retain throughout the unbiased MD simulation, we only consider the hydrogen bonds between backbone atoms, i.e., N-H and O=C. Contacts within the same amino acids and between adjacent amino acids are discarded to avoid confusion. NRF2 exhibits a clear and strong hydrogen bond pattern of a β-hairpin with a turn at the linear motif while PTMA is much less structured with a nearly random hydrogen bond pattern.

Figure 9: Map of backbone hydrogen bonds of NRF2 (a) and PTMA (b). The colorbar represents the average number of hydrogen bonds during the unbiased MD simulations. The yellow square marks the linear motif of the peptides. NRF2 shows a clear β-hairpin structural content while PTMA is mostly disordered.



1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

Figures 10 and 11 show a binding heatmap on the top-viewed surface of Kelch (panel (b)), a bar representation of the number of hydrogen bonds per residue of Kelch (panel (a)) and the peptide (panel (d)), and a matrix representation of the number of intermolecular hydrogen bonds (panel (c)) between Kelch (x-axis) and the peptide (y-axis). Green and blue represent NRF2 and PTMA, respectively. The data were calculated from the unbiased MD simulations. Figure 10 shows that GLU9 of NRF2 is the residue with the strongest binding to Kelch. GLU9 forms hydrogen bonds with ARG483, ARG415, and SER508 of Kelch in a descending order of strength. In addition to strong hydrogen bonds at GLU9, we also observe other hydrogen bonds in the linear motif of NRF2, especially at GLU8, THR10, and GLU12. Notably, GLU9, THR10, and GLU12 of NRF2 are frequently mutated in cancer patients, suggesting that these residues play critical roles in the function of NRF2. 84 In Figure 11, the linear motif of PTMA also appears to form most of the intermolecular contacts with Kelch. Both GLU8 and GLU12 of PTMA strongly interact with ARG380 and ARG415 of Kelch. GLU9 forms strong contacts with ARG483. The ARG residues 415 and 483 of Kelch consistently appear as the binding hot spots for the linear motifs of both NRF2 and PTMA. This results agree well with the experimental observations. 16,64

4

Discussions and Conclusions

We have investigated the binding of the Kelch domain of Keap1 to two different binding partners, NRF2 and PTMA using 3-µs well-tempered metadynamics simulations. NRF2 is well known for its strong binding affinity to Kelch and is the key regulator of the cellular oxidative stress response pathway through the interaction with Kelch. 5,85 PTMA is a less examined peptide with much weaker binding affinity to Kelch (100-fold less than that of NRF2) and is believed to help transport Kelch to the nucleus. 17 Interestingly, these two peptides share a highly similar binding motif consisting of 6 polar and charged residues, i.e., DEETGE in NRF2 and NEENGE in PTMA. However, the cause of the large difference in


Page 20 of 34

Page 21 of 34

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


Figure 10: Top-viewed binding surface of Kelch (b), number of hydrogen bonds per residue of Kelch (a) and NRF2 (d), and map of intermolecular hydrogen bonds between Kelch (x-axis) and NRF2 (y-axis) (c). The axes of (a), (c), and (d) are properly aligned. The color scales of (b) and (c) are identical.



1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

Figure 11: Top-viewed binding surface of Kelch (b), number of hydrogen bonds per residue of Kelch (a) and PTMA (d), and map of intermolecular hydrogen bonds between Kelch (xaxis) and PTMA (y-axis) (c). The axes of (a), (c), and (d) are properly aligned. The color scales of (b) and (c) are identical.


Page 22 of 34

Page 23 of 34

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


the binding affinity of these two peptides to Kelch remains unexplained. Our 3-µs simulations provide extensive conformational sampling of the complexes. Starting from completely unbound configurations, the simulations drive multiple association and dissociation events, leading to converged binding free energies of −12.98 ± 0.04 kcal/mol and −3.82 ± 0.03 kcal/mol for the NRF2-Kelch and PTMA-Kelch complexes, respectively. The binding free energies agree with experiments in the fact that NRF2 binds stronger than PTMA to the common binding partner Kelch. From the exhaustive configurational ensembles, we find that PTMA is much more disordered than NRF2 in both bound and unbound states. The higher structural plasticity probably explains the weaker binding of PTMA. We also observe an important feature in the structural properties of NRF2: when not bound to Kelch, NRF2 has a slight preference for adopting short hairpin structures rather than staying in highly disordered states like PTMA. This appears to support the hypothesis of coupled folding and binding. Indeed, the unbound conformational ensemble of NRF2 found in this and in our previous enhanced sampling 68 study contains the extended β-hairpin structures resembling the NRF2 bound Xray structure. 64 These hairpin conformations feature a turn of 6 polar and charged residues composing the linear motif that facilitates Kelch binding. Within the linear motif running from residues 7 to 12 of NRF2, residues GLU8, GLU9, THR10, and GLU12 form the key interactions with Kelch. Despite the similarity in linear motif sequences, NRF2 and PTMA exhibit distinct linear motif conformations. Although residues GLU8, GLU9, and GLU12 of PTMA’s linear motif also form consistent hydrogen bonds with the same binding pocket of Kelch, PTMA remains highly disordered when approaching Kelch. Its structural flexibility works against achieving a stable complex with Kelch. Both the pre-folded behavior and the facilitating linear motif of NRF2 contribute toward its strong association with Kelch. In conclusion, to the best of our knowledge, this is the first extensive enhanced sampling yielding a comprehensive description of the conformational ensembles of the complexes formed by Kelch and its binding partners, NRF2 and PTMA. Our findings support the hy-



1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

pothesis of coupled folding and binding in the case of NRF2 and suggest that following the prefolded structure, the linear motif plays the second important role in NRF2-Kelch binding.

Acknowledgments We thank Compute Canada for computational resources. Financial support was provided by the University of Waterloo, and Natural Sciences and Engineering Research Council of Canada (NSERC).

References (1) Sporn, M. B.; Liby, K. T. NRF2 and cancer: the good, the bad and the importance of context. Nat Rev Cancer 2012, 12, 564–571. (2) Yamadori, T.; Ishii, Y.; Homma, S.; Morishima, Y.; Kurishima, K.; Itoh, K.; Yamamoto, M.; Minami, Y.; Noguchi, M.; Hizawa, N. Molecular mechanisms for the regulation of Nrf2-mediated cell proliferation in non-small-cell lung cancers. Oncogene 2012, 31, 4768–4777. (3) Gan, L.; Johnson, J. A. Oxidative damage and the Nrf2-ARE pathway in neurodegenerative diseases. Biochim Biophys Acta 2014, 1842, 1208–1218. (4) Wakabayashi, N.; Itoh, K.; Wakabayashi, J.; Motohashi, H.; Noda, S.; Takahashi, S.; Imakado, S.; Kotsuji, T.; Otsuka, F.; Roop, D. R.; Harada, T.; Engel, J. D.; Yamamoto, M. Keap1-null mutation leads to postnatal lethality due to constitutive Nrf2 activation. Nat Genet 2003, 35, 238–245. (5) Itoh, K.; Wakabayashi, N.; Katoh, Y.; Ishii, T.; Igarashi, K.; Engel, J. D.; Yamamoto, M. Keap1 represses nuclear activation of antioxidant responsive elements by Nrf2 through binding to the amino-terminal Neh2 domain. Genes Dev 1999, 13, 76–86.


Page 24 of 34

Page 25 of 34

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


(6) Itoh, K.; Wakabayashi, N.; Katoh, Y.; Ishii, T.; O’Connor, T.; Yamamoto, M. Keap1 regulates both cytoplasmic-nuclear shuttling and degradation of Nrf2 in response to electrophiles. Genes Cells 2003, 8, 379–391. (7) McMahon, M.; Itoh, K.; Yamamoto, M.; Hayes, J. D. Keap1-dependent proteasomal degradation of transcription factor Nrf2 contributes to the negative regulation of antioxidant response element-driven gene expression. J Biol Chem 2003, 278, 21592–21600. (8) Cullinan, S. B.; Gordan, J. D.; Jin, J.; Harper, J. W.; Diehl, J. A. The Keap1-BTB protein is an adaptor that bridges Nrf2 to a Cul3-based E3 ligase: oxidative stress sensing by a Cul3-Keap1 ligase. Mol Cell Biol 2004, 24, 8477–8486. (9) Kobayashi, A.; Kang, M.-I.; Okawa, H.; Ohtsuji, M.; Zenke, Y.; Chiba, T.; Igarashi, K.; Yamamoto, M. Oxidative stress sensor Keap1 functions as an adaptor for Cul3-based E3 ligase to regulate proteasomal degradation of Nrf2. Mol Cell Biol 2004, 24, 7130–7139. (10) Tong, K. I.; Katoh, Y.; Kusunoki, H.; Itoh, K.; Tanaka, T.; Yamamoto, M. Keap1 Recruits Neh2 through Binding to ETGE and DLG Motifs: Characterization of the Two-Site Molecular Recognition Model. Mol Cell Biol 2006, 26, 2887–2900. (11) Suzuki, T.; Motohashi, H.; Yamamoto, M. Toward clinical application of the Keap1Nrf2 pathway. Trends Pharmacol Sci 2013, 34, 340–346. (12) Komatsu, M.; Kurokawa, H.; Waguri, S.; Taguchi, K.; Kobayashi, A.; Ichimura, Y.; Sou, Y.-S.; Ueno, I.; Sakamoto, A.; Tong, K. I.; Kim, M.; Nishito, Y.; Iemura, S.-i.; Natsume, T.; Ueno, T.; Kominami, E.; Motohashi, H.; Tanaka, K.; Yamamoto, M. The selective autophagy substrate p62 activates the stress responsive transcription factor Nrf2 through inactivation of Keap1. Nat Cell Biol 2010, 12, 213–223. (13) Camp, N. D.; James, R. G.; Dawson, D. W.; Yan, F.; Davison, J. M.; Houck, S. A.; Tang, X.; Zheng, N.; Major, M. B.; Moon, R. T. Wilms tumor gene on X chromosome



1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

(WTX) inhibits degradation of NRF2 protein through competitive binding to KEAP1 protein. J Biol Chem 2012, 287, 6539–6550. (14) Strachan, G. D.; Morgan, K. L.; Otis, L. L.; Caltagarone, J.; Gittis, A.; Bowser, R.; Jordan-Sciutto, K. L. Fetal Alz-50 clone 1 interacts with the human orthologue of the Kelch-like Ech-associated protein. Biochemistry 2004, 43, 12113–12122. (15) Ma, J.; Cai, H.; Wu, T.; Sobhian, B.; Huo, Y.; Alcivar, A.; Mehta, M.; Cheung, K. L.; Ganesan, S.; Kong, A.-N. T.; Zhang, D. D.; Xia, B. PALB2 interacts with KEAP1 to promote NRF2 nuclear accumulation and function. Mol Cell Biol 2012, 32, 1506–1517. (16) Padmanabhan, B.; Nakamura, Y.; Yokoyama, S. Structural analysis of the complex of Keap1 with a prothymosin alpha peptide. Acta Crystallogr Sect F Struct Biol Cryst Commun 2008, 64, 233–238. (17) Niture, S. K.; Jaiswal, A. K. Prothymosin-alpha mediates nuclear import of the INrf2/Cul3 Rbx1 complex to degrade nuclear Nrf2. J Biol Chem 2009, 284, 13856– 13868. (18) Karapetian, R. N.; Evstafieva, A. G.; Abaeva, I. S.; Chichkova, N. V.; Filonov, G. S.; Rubtsov, Y. P.; Sukhacheva, E. A.; Melnikov, S. V.; Schneider, U.; Wanker, E. E.; Vartapetian, A. B. Nuclear oncoprotein prothymosin alpha is a partner of Keap1: implications for expression of oxidative stress-protecting genes. Mol Cell Biol 2005, 25, 1089–1099. (19) Stepkowski, T. M.; Kruszewski, M. K. Molecular cross-talk between the NRF2/KEAP1 signaling pathway, autophagy, and apoptosis. Free Radic Biol Med 2011, 50, 1186– 1195. (20) Ioannou, K.; Samara, P.; Livaniou, E.; Derhovanessian, E.; Tsitsilonis, O. E. Prothymosin alpha: a ubiquitous polypeptide with potential use in cancer diagnosis and therapy. Cancer Immunol Immunother 2012, 61, 599–614. 26 ACS Paragon Plus Environment

Page 26 of 34

Page 27 of 34

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


(21) Fujita, R.; Ueda, H. Prothymosin-alpha1 prevents necrosis and apoptosis following stroke. Cell Death Differ 2007, 14, 1839–1842. (22) Fuxreiter, M.; Tompa, P.; Simon, I.; Uversky, V. N.; Hansen, J. C.; Asturias, F. J. Malleable machines take shape in eukaryotic transcriptional regulation. Nat Chem Biol 2008, 4, 728–737. (23) Tompa, P. Intrinsically disordered proteins: a 10-year recap. Trends Biochem Sci 2012, 37, 509–516. (24) Uversky, V. N. A decade and a half of protein intrinsic disorder: biology still waits for physics. Protein Sci 2013, 22, 693–724. (25) Habchi, J.; Tompa, P.; Longhi, S.; Uversky, V. N. Introducing protein intrinsic disorder. Chem Rev 2014, 114, 6561–6588. (26) Fisher, C. K.; Huang, A.; Stultz, C. M. Modeling intrinsically disordered proteins with bayesian statistics. J Am Chem Soc 2010, 132, 14919–14927. (27) Maisuradze, G. G.; Liwo, A.; Senet, P.; Scheraga, H. A. Local vs global motions in protein folding. J Chem Theory Comput 2013, 9, 2907–2921. (28) Palazzesi, F.; Prakash, M. K.; Bonomi, M.; Barducci, A. Accuracy of Current All-Atom Force-Fields in Modeling Protein Disordered States. J Chem Theory Comput 2015, 11, 2–7. (29) Stanley, N.; Esteban-Mart´ın, S.; De Fabritiis, G. Progress in studying intrinsically disordered proteins with atomistic simulations. Prog Biophys Mol Biol 2015, 119, 47– 52. (30) Dunker, A. K.; Garner, E.; Guilliot, S.; Romero, P.; Albrecht, K.; Hart, J.; Obradovic, Z.; Kissinger, C.; Villafranca, J. E. Protein disorder and the evolution



1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

of molecular recognition: theory, predictions and observations. Pac Symp Biocomput 1998, 473–484. (31) Gunasekaran, K.; Tsai, C.-J.; Kumar, S.; Zanuy, D.; Nussinov, R. Extended disordered proteins: targeting function with less scaffold. Trends Biochem Sci 2003, 28, 81–85. (32) Shoemaker, B. A.; Portman, J. J.; Wolynes, P. G. Speeding molecular recognition by using the folding funnel: The fly-casting mechanism. Proc Natl Acad Sci U S A 2000, 97, 8868–8873. (33) Hilser, V. J.; Thompson, E. B. Intrinsic disorder as a mechanism to optimize allosteric coupling in proteins. Proc Natl Acad Sci U S A 2007, 104, 8311–8315. (34) Huang, Y.; Liu, Z. Kinetic Advantage of Intrinsically Disordered Proteins in Coupled Folding - Binding Process: A Critical Assessment of the “Fly-Casting” Mechanism. J Mol Biol 2009, 393, 1143–1159. (35) Uversky, V. N. Unusual biophysics of intrinsically disordered proteins. Biochim Biophys Acta - Proteins Proteom 2013, 1834, 932–951. (36) Uversky, V. N. Natively unfolded proteins: a point where biology waits for physics. Protein Sci 2002, 11, 739–756. (37) Chen, J. Towards the physical basis of how intrinsic disorder mediates protein function. Arch Biochem Biophys 2012, 524, 123–131. (38) Baker, C. M.; Best, R. B. Insights into the binding of intrinsically disordered proteins from molecular dynamics simulation. Wiley Interdiscip Rev Comput Mol Sci 2014, 4, 182–198. (39) Best, R. B. Atomistic molecular simulations of protein folding. Curr Opin Struct Biol 2012, 22, 52–61.


Page 28 of 34

Page 29 of 34

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


(40) Best, R. B.; Hummer, G. Optimized Molecular Dynamics Force Fields Applied to the Helix-Coil Transition of Polypeptides. J Phys Chem B 2009, 113, 9004–9015. (41) Mittal, J.; Best, R. B. Tackling Force-Field Bias in Protein Folding Simulations: Folding of Villin HP35 and Pin WW Domains in Explicit Water. Biophys J 2010, 99, L26–L28. (42) Best, R. B.; Mittal, J. Balance between alpha and beta structures in ab initio protein folding. J Phys Chem B 2010, 114, 8790–8798. (43) Piana, S.; Lindorff-Larsen, K.; Shaw, D. E. How Robust Are Protein Folding Simulations with Respect to Force Field Parameterization? Biophys J 2011, 100, L47–L49. (44) Lindorff-Larsen, K.; Piana, S.; Dror, R. O.; Shaw, D. E. How fast-folding proteins fold. Science 2011, 334, 517–520. (45) Beauchamp, K. A.; Lin, Y.-S.; Das, R.; Pande, V. S. Are Protein Force Fields Getting Better? A Systematic Benchmark on 524 Diverse NMR Measurements. J Chem Theory Comput 2012, 8, 1409–1414. (46) Cino, E. A.; Choy, W.-Y.; Karttunen, M. Comparison of Secondary Structure Formation Using 10 Different Force Fields in Microsecond Molecular Dynamics Simulations. J Chem Theory Comput 2012, 8, 2725–2740. (47) Lindorff-Larsen, K.; Maragakis, P.; Piana, S.; Eastwood, M. P.; Dror, R. O.; Shaw, D. E. Systematic Validation of Protein Force Fields against Experimental Data. PLoS ONE 2012, 7, e32131. (48) Hornak, V.; Abel, R.; Okur, A.; Strockbine, B.; Roitberg, A.; Simmerling, C. Comparison of multiple Amber force fields and development of improved protein backbone parameters. Proteins: Struct , Funct , Bioinf 2006, 65, 712–725. (49) Lindorff-Larsen, K.; Piana, S.; Palmo, K.; Maragakis, P.; Klepeis, J. L.; Dror, R. O.;



1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

Shaw, D. E. Improved side-chain torsion potentials for the Amber ff99SB protein force field. Proteins: Struct , Funct , Bioinf 2010, 78, 1950–1958. (50) Mao, A. H.; Crick, S. L.; Vitalis, A.; Chicoine, C. L.; Pappu, R. V. Net charge per residue modulates conformational ensembles of intrinsically disordered proteins. Proc Natl Acad Sci U S A 2010, 107, 8183–8188. (51) Das, R. K.; Pappu, R. V. Conformations of intrinsically disordered proteins are influenced by linear sequence distributions of oppositely charged residues. Proc Natl Acad Sci U S A 2013, 110, 13392–13397. (52) Bolhuis, P. G.; Chandler, D.; Dellago, C.; Geissler, P. L. Transition path sampling: throwing ropes over rough mountain passes, in the dark. Annu Rev Phys Chem 2002, 53, 291–318. (53) van Erp, T. S.; Bolhuis, P. G. Elaborating transition interface sampling methods. J Comput Phys 2005, 205, 157–181. (54) Hummer, G. From transition paths to transition states and rate coefficients. J Chem Phys 2004, 120, 516–523. (55) Best, R. B.; Hummer, G. Reaction coordinates and rates from transition paths. Proc Natl Acad Sci U S A 2005, 102, 6732–6737. (56) E, W.; Ren, W.; Vanden-Eijnden, E. Finite temperature string method for the study of rare events. J Phys Chem B 2005, 109, 6688–6693. (57) Faccioli, P.; Sega, M.; Pederiva, F.; Orland, H. Dominant Pathways in Protein Folding. Phys Rev Lett 2006, 97, 108101. (58) Torrie, G.; Valleau, J. Nonphysical sampling distributions in Monte Carlo free-energy estimation: Umbrella sampling. J Comput Phys 1977, 23, 187–199.


Page 30 of 34

Page 31 of 34

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


(59) Sugita, Y.; Okamoto, Y. Replica-exchange molecular dynamics method for protein folding. Chem Phys Lett 1999, 314, 141–151. (60) Grubm¨ uller, H.; Heymann, B.; Tavan, P. Ligand binding: molecular mechanics calculation of the streptavidin-biotin rupture force. Science 1996, 271, 997–999. (61) Sotomayor, M.; Schulten, K. Single-molecule experiments in vitro and in silico. Science 2007, 316, 1144–1148. (62) Laio, A.; Parrinello, M. Escaping free-energy minima. Proc Natl Acad Sci U S A 2002, 99, 12562–12566. (63) Barducci, A.; Bussi, G.; Parrinello, M. Well-tempered metadynamics: A smoothly converging and tunable free-energy method. Phys Rev Lett 2008, 100, 020603. (64) Lo, S.-C.; Li, X.; Henzl, M. T.; Beamer, L. J.; Hannink, M. Structure of the Keap1:Nrf2 interface provides mechanistic insight into Nrf2 signaling. EMBO J 2006, 25, 3605– 3617. (65) Cino, E. A.; Killoran, R. C.; Karttunen, M.; Choy, W.-Y. Binding of disordered proteins to a protein hub. Sci Rep 2013, 3, 1–8. (66) Cino, E. A.; Choy, W.-Y.; Karttunen, M. Conformational Biases of Linear Motifs. J Phys Chem B 2013, 117, 15943–15957. (67) Khan, H.; Cino, E. A.; Brickenden, A.; Fan, J.; Yang, D.; Choy, W.-Y. Fuzzy complex formation between the intrinsically disordered prothymosin α and the Kelch domain of Keap1 involved in the oxidative stress response. J Mol Biol 2013, 425, 1011–1027. (68) Do, T. N.; Choy, W.-Y.; Karttunen, M. Accelerating the Conformational Sampling of Intrinsically Disordered Proteins. J Chem Theory Comput 2014, 10, 5081–5094.



1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

(69) Jorgensen, W. L.; Chandrasekhar, J.; Madura, J. D.; Impey, R. W.; Klein, M. L. Comparison of simple potential functions for simulating liquid water. J Chem Phys 1983, 79, 926–935. (70) Joung, I. S.; Cheatham, T. E. I. Determination of alkali and halide monovalent ion parameters for use in explicitly solvated biomolecular simulations. J Phys Chem B 2008, 112, 9020–9041. (71) Patra, M.; Karttunen, M. Systematic comparison of force fields for microscopic simulations of NaCl in aqueous solutions: diffusion, free energy of hydration, and structural properties. J Comput Chem 2004, 25, 678–689. (72) Auffinger, P.; Cheatham, T. E.; Vaiana, A. C. Spontaneous Formation of KCl Aggregates in Biomolecular Simulations: A Force Field Issue? J Chem Theory Comput 2007, 3, 1851–1859. (73) Chen, A. A.; Pappu, R. V. Parameters of Monovalent Ions in the AMBER-99 Forcefield: Assessment of Inaccuracies and Proposed Improvements. J Phys Chem B 2007, 111, 11884–11887. (74) Hess, B.; Kutzner, C.; van der Spoel, D.; Lindahl, E. GROMACS 4: Algorithms for Highly Efficient, Load-Balanced, and Scalable Molecular Simulation. J Chem Theory Comput 2008, 4, 435–447. (75) Bonomi, M.; Branduardi, D.; Bussi, G.; Camilloni, C.; Provasi, D.; Raiteri, P.; Donadio, D.; Marinelli, F.; Pietrucci, F.; Broglia, R. A.; Parrinello, M. PLUMED: A portable plugin for free-energy calculations with molecular dynamics. Comput Phys Commun 2009, 180, 1961–1972. (76) Darden, T.; York, D.; Pedersen, L. Particle mesh Ewald: An Nlog(N) method for Ewald sums in large systems. J Chem Phys 1993, 98, 10089–10092.


Page 32 of 34

Page 33 of 34

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


(77) Essmann, U.; Perera, L.; Berkowitz, M. L.; Darden, T.; Lee, H.; Pedersen, L. G. A smooth particle mesh Ewald method. J Chem Phys 1995, 103, 8577–8593. (78) Bussi, G.; Donadio, D.; Parrinello, M. Canonical sampling through velocity rescaling. J Chem Phys 2007, 126, 014101. (79) Parrinello, M.; Rahman, A. Polymorphic transitions in single crystals: A new molecular dynamics method. J Appl Phys 1981, 52, 7182–7190. (80) Pietrucci, F.; Laio, A. A Collective Variable for the Efficient Exploration of Protein Beta-Sheet Structures: Application to SH3 and GB1. J Chem Theory Comput 2009, 5, 2197–2201. (81) Branduardi, D.; Bussi, G.; Parrinello, M. Metadynamics with adaptive Gaussians. J Chem Theory Comput 2012, 8, 2247–2254. (82) Das, R. K.; Mao, A. H.; Pappu, R. V. Unmasking functional motifs within disordered regions of proteins. Sci Signal 2012, 5, pe17. (83) Davey, N. E.; Van Roey, K.; Weatheritt, R. J.; Toedt, G.; Uyar, B.; Altenberg, B.; Budd, A.; Diella, F.; Dinkel, H.; Gibson, T. J. Attributes of short linear motifs. Mol Biosyst 2012, 8, 268–281. (84) Hayes, J. D.; McMahon, M. NRF2 and KEAP1 mutations: permanent activation of an adaptive response in cancer. Trends Biochem Sci 2009, 34, 176–188. (85) Itoh, K.; Chiba, T.; Takahashi, S.; Ishii, T.; Igarashi, K.; Katoh, Y.; Oyake, T.; Hayashi, N.; Satoh, K.; Hatayama, I.; Yamamoto, M.; Nabeshima, Y. An Nrf2/small Maf heterodimer mediates the induction of phase II detoxifying enzyme genes through antioxidant response elements. Biochem Biophys Res Commun 1997, 236, 313–322.



1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

For Table of Contents Only

Association of NRF2 to Kelch explored by well-tempered metadynamics reproduces the experimental binding pocket and features a large conformational ensemble of NRF2.


Page 34 of 34

Binding of Disordered Peptides to Kelch: Insights from Enhanced

Recommend Documents