Computational and Experimental Evaluation of Designed Î²-Cap

Article pubs.acs.org/jcim

Computational and Experimental Evaluation of Designed β‑Cap Hairpins Using Molecular Simulations and Kinetic Network Models Yunhui Ge,† Brandon L. Kier,‡ Niels H. Andersen,‡ and Vincent A. Voelz*,† †

Department of Chemistry, Temple University, Philadelphia, Pennsylvania 19122, United States Department of Chemistry, University of Washington, Seattle, Washington 98195, United States

‡

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S Supporting Information *

ABSTRACT: Molecular simulation has been used to model the detailed folding properties of peptides, yet prospective computational peptide design by such approaches remains challenging and nontrivial. To test the accuracy of simulationbased hairpin design, we characterized the folding properties of a series of so-called β-cap hairpin peptides designed to mimic a conserved hairpin of LapD, a bacterial intracellular signaling protein, both experimentally by NMR spectroscopy and computationally by implicit-solvent replica-exchange molecular dynamics using three different AMBER force fields (ff96, ff99sb-ildn, and ff99sb-ildn-NMR). A unique challenge presented by these designs is the presence of both a terminal Trp-Trp capping motif and a conserved GWxQ motif in the hairpin turn required for binding to LapG. Consistent with previous studies, we found AMBER ff96 to be the most accurate when used with the OBC GBSA implicit solvent model, despite its known bias toward β-sheet conformations when used in explicit-solvent simulations. To gain microscopic insight into the folding landscape of the hairpin designs, we additionally performed parallel simulations on the Folding@home distributed computing platform using AMBER ff99sb-ildn-NMR with TIP3P explicit solvent. Markov state models (MSMs) built from trajectory data reveal a number of non-native interactions between Trp and other amino acid side chains, creating potential problems in achieving wellfolded hairpin structures in solution.

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A target system for computationally designed β-hairpin mimics is the LapG periplasmic protease involved in bacterial biofilm formation. LapG cleaves the N-terminus of a cellsurface adhesion protein called LapA, resulting in the loss of biofilm formation.14,15 In the presence of high cytoplasmic levels of cyclic-di-GMP, another protein called LapD is recruited to sequester LapG via direct protein−protein interaction, preventing it from cleaving LapA and promoting biofilm formation.16−18 Disrupting the LapG−LapD interaction is thus an attractive strategy to disperse bacterial biofilms, a major source of antibiotic resistance in biofilm-forming pathogens.17,19,20 A recent high-resolution crystal structure of the periplasmic domain of LapD bound to LapG reveals that a highly conserved hairpin epitope from LapD binds to a pocket of LapG.19,21 The LapD hairpin contains a highly conserved GW126xQ motif that is absolutely required for binding. In its unbound state, W126 protrudes into solvent; thus, its burial in this pocket upon binding with LapG is expected to contribute significantly to the binding affinity with LapG (Figure 1A). Can we design small peptide mimics of the LapD hairpin that are stably folded in solution? A key design challenge to overcome is the difficulty of maintaining a solvent-exposed tryptophan in the hairpin turn that is required for function.

INTRODUCTION The development of peptidomimeticsmolecules that can mimic the structure and binding motifs of proteinshas enabled new approaches to disrupt protein−protein interactions. Unlike small-molecule drugs that typically target an active-site binding pocket, structural scaffolds mimicking αhelices and β-hairpins have been used to target relatively large protein interaction surfaces. Examples include chemically modified peptides (stapled peptides, cyclic peptides, Nmethylated peptides) as well as nonbiological scaffolds such as β-peptides, peptoids, spiroligomers, and oligo(oxopiperazines), many of which have been discovered and/ or improved through computational screening efforts.1,2 A key challenge in the computational design of peptidomimetics is controlling their foldameric properties, as enhancing conformational preorganization in the unbound state should result in higher binding affinity.3−5 Despite many several successful examples of engineered β-hairpin mimics,6−11 many more examples of α-helix peptidomimetics exist, perhaps reflecting key obstacles in designing well-folded β-hairpins. Unlike α-helices, β-hairpin mimics can have numerous alternative hydrogen-bonded configurations12 and folding times in the microsecond range.13 Because of this, a complete understanding of β-hairpin folding propertiesand application of this information to in silico design of preorganized β-hairpin mimicsmay likely require physics-based simulation models. © 2017 American Chemical Society

Received: March 2, 2017 Published: June 14, 2017 1609

DOI: 10.1021/acs.jcim.7b00132 J. Chem. Inf. Model. 2017, 57, 1609−1620

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Journal of Chemical Information and Modeling

structure, the GWxQ motif is found in the hairpin turn, with the Gly and Trp residues forming a type II′ two-residue β-turn. Because Gly-Trp is a marginal sequence for enforcing this turn type, we incorporated as many fold-stabilizing features as possible into the final designs. Residues outside the GWxQ motif are not strictly conserved and are not crucial to the integrity of the folded hairpin structure. We verified this in each of our designs by modeling residue substitutions with ROSETTA FoldIt Standalone26,27 and PyMOL,28 whereby every position in the hairpin was mutated to all other nonproline amino acids in the context of the conformation bound to LapG. We examined possible clashing interactions and/or backbone hydrogen-bond disruption and found that all positions allowed most substitutions. A total of six β-cap peptides were designed and synthesized, each a result of iterated rounds of experimental assays to test folding and binding properties (Table 1). This family of peptides is named “TrpLoop”, as all of the designs contain the essential Trp residue in the β-hairpin turn. TrpLoop-1a was designed to balance solubility and β-sheet propensity, with a mix of mostly Thr (T), Arg (R), and Lys (K) residues. In addition, it incorporated an essential terminal β-cap motif consisting of RW···WE (with Trp located, crucially, at the non-hydrogen-bonding positions nearest the termini). TrpLoop-2a was designed as an improvement on TrpLoop1a, which was found by chemical shift deviations (see below) to be only ∼47% folded at 298 K. Improved cross-strand hydrophobic interactions were achieved by introduction of Ile and Val residues and a C-terminal Tyr, resulting in a construct with significantly higher folded population (∼96% at 280 K by NMR). This peptide was prone to aggregation at neutral and basic pH, a side effect of high hydrophobicity and exposed βstrands. TrpLoop-2b represented an efficient stability-forsolubility trade-off. Replacing the Val-Thr (VT) sequence with Lys-Lys (KK) significantly reduced aggregation while maintaining acceptable fold stability. TrpLoop-3 was a cysteine-cyclized peptide with significantly lower fold stability ( 6.8 for all three force fields. This large difference is surprising considering that TrpLoop-4b differs from TrpLoop4a by only a single mutation, T4M. To better understand how this mutation could cause such different behavior in REMD simulations, we used MSMBuilder3 to perform k-centers clustering of the combined trajectory data (TrpLoop-4a and TrpLoop-4b) using the intermolecular backbone + Cβ RMSD as the distance metric. Examination of the relative cluster populations for TrpLoop4a versus TrpLoop-4b provided a way to analyze how the T4M mutation affects the folding landscape of TrpLoop-4b. Projection of population clusters onto an RMSD-to-native observable shows three main families of conformations: a folded state (RMSD < 2 Å), an unfolded state (RMSD > 4 Å),

crystal structure peaked at 0.94 Å and the RMSD of the turn residues peaked at 0.78 Å (Figure 3). Different force fields,

Figure 3. (A) Histograms of turn-residue RMSDs for TrpLoop-2a simulated with the AMBER ff96, ff99sb-ildn, and ff99sb-ildn-NMR force fields. (B) Histograms of all-residue RMSDs for TrpLoop-2a. (C, D) Representative (C) folded and (D) unfolded structures from the simulated ensemble using AMBER ff96, chosen randomly from each region.

however, predict different folded-state populations. The AMBER ff96, AMBER ff99sb-ildn, and AMBER ff99sb-ildnNMR force fields, when used with GBSA, predict folded populations of 93%, 65%, and 64%, respectively. A similar force field trend was found in our analysis of βsheet content as a function of temperature for all of the peptide designs. Except for very high temperatures, AMBER ff96 + GBSA consistently had the highest β-sheet content, while AMBER ff99sb-ildn-NMR + GBSA had the lowest (Figure 4). The melting temperature of each peptide in each force field was estimated from the maximum negative slope of the plot of folded population versus temperature (Figure S4). Not surprisingly, the simulated melting temperature is severely overestimated (375−450 K for all three force fields) because the GBSA model lacks any description of temperaturedependent solvation and also lacks dispersion forces contributed by the solvent.62 That said, the AMBER ff96 force field does the best job of capturing the experimental trend in melting temperature for each design (Table S2). Next, we compared experimental versus simulated folded populations predicted by different force fields for each peptide design. Calculated linear correlation coefficients (i.e., R2 values) 1613


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Figure 4. β-Sheet content vs temperature for designed hairpin peptides simulated using AMBER ff96, ff99sb-ildn, and ff99sb-ildn-NMR.

interaction in our REMD simulations is likely exaggerated by the GBSA implicit solvent model, resulting in poorer agreement with experiemental observables. Markov State Models of Hairpin Peptide Dynamics in Explicit Solvent. We performed massively parallel explicitsolvent simulations at 300 K on the Folding@home distributed computing platform for all five hairpin designs using the AMBER ff99sb-ildn-NMR force field, a potential previously shown to yield excellent quantitative predictions of experimental folding properties.40,66 Our purpose was twofold. First, we aimed to use the resulting trajectory data as a “gold standard” against which to compare the results of implicitsolvent REMD simulations. Second, by constructing MSMs from the resulting trajectory data, we sought to gain microscopic insight into both thermodynamic and kinetic mechanisms of hairpin folding that could potentially be useful for de novo design. As described in Materials and Methods, we used pairwise atomic distance coordinates (all backbone + Cβ atoms) as input to the tICA algorithm to discover the low-dimensional subspace corresponding to the slowest kinetic degrees of freedom. Conformational clustering in this subspace was then used to identify kinetically metastable states. To better compare designs with similar sequences, we analyzed the combined trajectory data of TrpLoop-2a and TrpLoop-2b and the combined data of TrpLoop-4a and TrpLoop-4b to generate MSMs of 18 and 25 states, respectively. These numbers of states yielded MSMs fine-grained enough to be Markovian with well-separated implied time scales (see Figure S3) yet coarsegrained enough to be human-understandable. Projections of the TrpLoop-2a/2b and TrpLoop-4a/4b explicit-solvent trajectory data onto the two largest tICA components show folding landscapes with features consistent with two-state hairpin folding but also the existence of many long-lived kinetic intermediates (Figure 8). For both sets of

Figure 5. Comparison of experimental (x axis) and simulated folded populations (y axis) for AMBER ff96, ff99sb-ildn, and ff99sb-ildnNMR when used with OBC GBSA implicit solvent. Shown for each is the RMS error agreement for fold populations calculated using allresidue RMSD cutoffs (the value in parentheses is the RMS error agreement when the TrpLoop-4b outlier is removed).

and a misfolded state (RMSD ≈ 3 Å) (Figure 7). Whereas TrpLoop-4a and -4b have roughly equal native-state populations, the misfolded state is populated by TrpLoop-4b in a 9:1 ratio with respect to TrpLoop-4a and is predicted to account for 17.4% of the total population for TrpLoop-4b. Inspection of the misfolded state reveals a misregistered hairpin with a close interaction between Met4 and Trp11. While methionine−aromatic interactions are known to have stabilizing effects on proteins,63−65 the strong stability of this 1614


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Figure 6. Comparison of experimental and predicted HN (left) and Hα (right) chemical shifts for TrpLoop-4a in (A) ff96 (B) ff99sb-ildn (C) ff99sb-ildn-NMR. The NMR structure used for these comparisons was obtained using the same methods as for TrpLoop-2a in Figure 2 (see Materials and Methods). A visualization of the NMR-validated structure for each design can be found in Figure S10.

Table 2. Summary of χ2 Values for All of the Designs with Different AMBER Force Fields TrpLoop-1 TrpLoop-2a TrpLoop-2b TrpLoop-4a TrpLoop-4b

ff96

ff99sb-ildn

ff99sb-ildn-NMR

2.933 4.573 2.742 2.558 6.856

4.676 8.968 2.991 3.267 8.666

6.688 19.244 12.187 5.296 10.119

the tICA landscape indicates relaxation time scales similar to the folding time scale. The non-native “trap” states are stabilized by alternative backbone hydrogen-bonding registrations and competing hydrophobic interactions. While some of the competing non-native states are structurally well-defined (e.g., a “flipped” hairpin conformation of TrpLoop-4a/4b in which the strand−strand plane is inverted), others are diffuse. One such misfolded state in the TrpLoop-4a/4b system, state 12, is highly populated (see Figure S12). To characterize the structural ensemble of this state, we computed differences in average residue−residue distances from the folded state (Figure 9). The results reveal specific non-native interactions between residues such as Arg3− Trp7 and Gln9−Tyr12. We performed similar calculations for other non-native states (Figures S13 and S14) and found

designs, the unfolded state (a collection of unstructured conformations near the center of the tICA landscape) and the folded state are well-separated along the tIC1 component, corresponding to the slowest conformational transitions. Nearly orthogonal to the folding reaction, however, are multiple longlived kinetic “traps” whose distance from the unfolded state in 1615


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Figure 7. (A) RMSD-to-native distributions sampled in REMD simulations of TrpLoop-4a and TrpLoop-4b. Clustering of the joint trajectory was used to identify clusters corresponding to the folded (black), unfolded (blue), and misfolded (red) states. (B−D) Population ratios between TrpLoop-4a and TrpLoop-4b for the (B) folded, (C) misfolded, and (D) unfolded states. A typical structure from the misfolded state is shown in Figure S11. (E, F) Differences in average residue−residue contact probabilities between (E) the misfolded and folded states and (F) the misfolded and unfolded states. Red squares correspond to average distances that are closer in the misfolded state, while blue squares denote average distances that are closer in the other state. The upper and lower diagonals of each plot show results for Cα and Cβ distances, respectively. Circled regions highlight non-native interactions between Met4 and Trp11.

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DISCUSSION AND CONCLUSION It is somewhat surprising that AMBER ff96, now two decades old, so accurately predicts hairpin folding properties and NMR observables when coupled with GBSA implicit solvent, even outperforming AMBER ff99sb-ildn-NMR in TIP3P explicit solvent, a force field parametrized to successfully reproduce experimental NMR observables for peptides.32,66 Our force field comparisons suggest that key to the success of AMBER ff96 + GBSA, at least for β-hairpins, is its ability to stabilize βsheet conformations, counteracting the secondary structure bias toward α-helical conformations typical of implicit solvation models. Having an accurate implicit solvent model of β-hairpin folding in hand raises the prospect of extremely fast screening and selection of hairpin designs using GPU-accelerated molecular dynamics algorithms. Our results suggest proceeding with some caution, however, as some interactions besides salt bridges like Met−Trp may be artificially stabilized in these models, especially with respect to aryl/aryl interactions, which may be generally understabilized in fixed-charge force fields.40,69 Apart from force field testing and validation, we have also learned valuable lessons about the feasibility of designing β-cap hairpins to disrupt protein−protein interactions. Four of the six peptide designs (TrpLoop-1a, -2a, -4a, and -4b) were later assayed for binding; although these designs were well-folded in solution according to NMR and CD spectroscopy, UV crosslinking studies showed that they could not specifically bind the LapG target and induced substantial precipitate upon mixing at high peptide concentrations (Cooley and Sondermann, personal communication). It is likely that a folded hairpin structure alone is not enough to ensure binding and that maximizing

several kinds of non-native interactions, including (1) hydrophobic interactions between aromatic Trp and Tyr residues, (2) interactions of Trp with Arg and Lys cationic side chains, and (3) Trp−Met interactions, as found in our implicit-solvent simulations. These observations underscore the importance of negative design for improving engineered β-cap hairpins, i.e., considering all of the possible conformers accessible to a given designed sequence in an attempt to eliminate undesirable “traps”. Interestingly, we note that these competing interactions, as well as the β-cap Trp−Trp interaction, might be modeled more accurately in the future by polarizable force field models.67 In previous work,40 we found that explicit-solvent AMBER ff99sbildn-NMR simulations of tryptophan zipper (trpzip) hairpins do not fully predict the high folded population (>93%) of trpzip4,68 perhaps because of understabilization of highly cooperative face-to-edge Trp−Trp interactions. Comparison of Predicted Chemical Shifts from Implicit- and Explicit-Solvent Simulations. We calculated HN and Hα chemical shifts from the explicit-solvent trajectory data using the predictied equilibrium populations of each MSM state, i.e., as a population-weighted average of the ensembleaveraged chemical shift values calculated for each MSM state. To compare these results to those of the implicit-solvent simulations, we calculated the overall RMS deviation of predicted chemical shifts from the experimental values for TrpLoop-2a, -2b, -4a, and -4b (Figure 10). The results show that while AMBER ff99sb-ildn-NMR performsas expected considerably better in explicit solvent than implicit solvent, AMBER ff96 + GBSA is the most accurate of the force field combinations tested. 1616


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Figure 8. Projections of explicit-solvent simulation data onto the two largest tICA components for (A) TrpLoop-2a/2b and (B) TrpLoop-4a/4b. Red dots (indexed by arbitrary state numbers) denote the centers of metastable states used to construct the MSM, obtained through k-centers clustering.

Figure 9. Differences in average residue−residue contact probabilities between non-native MSM state 12 and the native folded state for simulations of (A) TrpLoop4a and (B) TrpLoop4b. Blue squares correspond to average distances that are closer in the folded state, while red squares denote average distances that are closer in state 12. The upper and lower diagonals of each plot show results for the Cα and Cβ distances, respectively.

instead, most successful binders are cyclized in some way, such as cysteine disulfides or DPro-LPro capping groups.6 Cyclization can reduce the entropy loss upon binding, a fact that has motivated the design of peptide macrocycles that can bind protein interfaces.71 In the case of our disulfide-cyclized TrpLoop-3 design, however, its linear counterparts TrpLoop-

peripheral binding surface interactions will result in improved hairpin designs. A recent study of cyclic β-hairpin mimics designed to bind to the MDM2 receptor supports this idea: the authors found that the design with the highest binding affinity was also the one with the lowest propensity to form a β-hairpin in solution.70 It also worth noting that very few examples exist of linear β-hairpin peptides that can bind protein surfaces; 1617


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Figure 10. Comparison of RMS errors of chemical shift results for (A) HN and (B) Hα in simulations with explicit solvent (green), implicit solvent with AMBER ff96 (red), implicit solvent with AMBER ff99-ildn (blue), and implicit solvent with AMBER ff99sb-ildn-NMR (yellow).

affinity peptidomimetics. New methods to construct MSMs from biased ensembles77 and efficiently predict how mutations and chemical modifications will perturb binding kinetics50,78 will likely make these approaches possible in the next few years.

4a and -4b are much better folded, suggesting that cyclization alone may not be a reliable design strategy. Another lesson from this work is a recognition of the unique challenge of designing well-folded β-cap hairpins with a functional GWxQ motif requirement. The inclusion of no less than three Trp residues in the hairpin sequence requires that much of the remaining sequence available for optimization be used to achieve solubility. Our MSM analysis suggests that non-native conformational states can be stabilized by competing hydrophobic interactions. Indeed, the Andersen group recently examined the contributions of various aryl−aryl interactions (Trp, Tyr, and Phe) to the fold stability of designed hairpins and found such interactions to provide significant stabilization, although not to the same extent as the Trp-Trp β-cap motif.24 Of the many successful β-cap hairpin designs synthesized and characterized by the Andersen group, few have aromatic residues in the region between the two tryptophan capping groups. The designs we have explored in this work contain at least one Trp residue in the turn and multiple intervening hydrophobic groups. Finally, this work suggests new ways that molecular simulation methods can work alongside experiments to screen and select peptides with desired folding properties. We were able to perform and analyze implicit-solvent REMD simulations in about 1 week of real-world time. It took nearly the same amount of time to synthesize the peptides and characterize their folding by NMR spectroscopy. Thus, besides being sufficiently accurate, our simulation-based computation predictions were competitive with the time scale of experiments; such calculations, performed in parallel, would provide higher throughput than experimental screening. Moreover, our MSM approach offers an attractive method for estimating folding thermodynamics and kinetics and the importance of off-target conformational traps. The tICA projection provides a particularly clear way to visualize and understand otherwise complicated folding and misfolding mechanisms. While for the moment MSM approaches are more expensive (especially when used with explicit solvent), we expect that in the future MSM adaptive sampling techniques,50,72 ever-faster molecular simulation hardware (e.g., GPUs), and improved force field potentials will make these approaches accurate and efficient for the purpose of design. With the recent emergence of MSMs studies that explicitly model protein−ligand binding,73−75 another exciting application of MSMs will be to use models of coupled folding and binding76 to guide the design of high-

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ASSOCIATED CONTENT

S Supporting Information *

The Supporting Information is available free of charge on the ACS Publications website at DOI: 10.1021/acs.jcim.7b00132. Figures S1−S14 and Tables S1 and S2 (PDF)

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AUTHOR INFORMATION

Corresponding Author

*E-mail: [email protected]. ORCID

Yunhui Ge: 0000-0002-3946-1440 Vincent A. Voelz: 0000-0002-1054-2124 Funding

This research was supported in part by the National Science Foundation through Major Research Instrumentation Grant CNS-09-58854 and MCB-1412508. Notes

The authors declare no competing financial interest.

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ACKNOWLEDGMENTS We thank the participants of Folding@home, without whom this work would not have been possible. ABBREVIATIONS MSM, Markov state model; REMD, replica-exchange molecular dynamics; NMR, nuclear magnetic resonance; DMSO, dimethyl sulfoxide

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REFERENCES

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