Molecular Simulations Identify Binding Poses and Approximate

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Molecular Simulations Identify Binding Poses and Approximate Affinities of Stapled #-##Helical Peptides to MDM2 and MDMX Joseph A Morrone, Alberto Perez, Qiaolin Deng, Sookhee Nicole Ha, M. Katharine Holloway, Tom K. Sawyer, Bradley S. Sherborne, Frank K. Brown, and Ken A Dill J. Chem. Theory Comput., Just Accepted Manuscript • DOI: 10.1021/acs.jctc.6b00978 • Publication Date (Web): 02 Jan 2017 Downloaded from http://pubs.acs.org on January 3, 2017

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Journal of Chemical Theory and Computation

Molecular Simulations Identify Binding Poses and Approximate Affinities of Stapled a-Helical Peptides to MDM2 and MDMX

Joseph A. Morrone1,*, Alberto Perez1, Qiaolin Deng4, Sookhee N. Ha4, M. Katharine Holloway4, Tomi K. Sawyer5, Bradley S. Sherborne4, Frank K. Brown4, and Ken A. Dill1,2,3,**

 1 Laufer Center for Physical and Quantitative Biology, Stony Brook University 2 Department of Chemistry, Stony Brook University  3 Department of Physics and Astronomy, Stony Brook University 4 Structural Chemistry, MRL 5 Discovery Chemistry, MRL

*Present Address: IBM T.J. Watson Research Center, 1101 Kitchawan Road, Yorktown Heights, NY 10598, USA **Corresponding Author: [email protected]

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Abstract

Traditionally, computing the binding affinities of proteins to even relatively small and rigid ligands by free-energy methods has been challenging due to large computational costs and significant errors. Here, we apply a new molecular simulation acceleration method called MELD (Modeling by Employing Limited Data) to study the binding of stapled a-helical peptides to the MDM2 and MDMX proteins. We employ free energy based molecular dynamics simulations (MELD-MD) to identify binding poses and calculate binding affinities. Even though stapled peptides are larger and more complex than most protein ligands, the MELD-MD simulations can identify relevant binding poses and compute relative binding affinities. MELD-MD appears to be a promising method for computing the binding properties of peptide ligands with proteins.

Keywords: MELD, stapled a-helical peptide, MDM2, MDMX, Free energy prediction, protein-protein interaction


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1. Introduction Peptides are a promising class of drug that may competitively and unique fill the gap between small molecule pharmaceuticals and large biotherapeutics such as antibodies. Importantly, peptides can target so-called 'undruggable' sites1 that have otherwise been especially challenging to develop small-molecule ligands with high binding affinities2. Such undruggable sites are often observed at protein-protein interaction interfaces, where there is either no cleft or specific binding site for a small molecule or the area of the interface is perceived to be too large to disrupt with a small molecule.

A major opportunity for peptide drugs is in the regulation of the well-known oncogenic p53 pathway. More precisely, p53 is known to trigger cell apoptosis, and therefore the maintenance of p53 activity is a critical biological mechanism for tumor suppression. MDM2 and its homolog MDMX (also known as MDM4) bind to p53 and inhibit its function3,4. Overexpression of MDM2/X lowers the activity of p53 and contributes to cancer growth.

Identifying dual inhibitors of MDM2 and MDMX has been a competitive area of drug discovery research4,5. For example, small molecule drugs such as Nutlin6 have been developed to inhibit MDM2 but they are not potent binders of MDMX. Although work has been carried out to find small-molecule dual inhibitors7, it has been challenging to advance pharmacologically suitable compounds. The p53 epitope adopts an a-helical structure with three residues (Phe, Trp, and Leu) anchored into the hydrophobic


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binding pocket of both MDM2 and MDMX.

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Peptides have been designed which

preserve this binding mode and exhibit significant potency in order to serve as dual inhibitors of MDM2 and MDMX8-11.

Despite their promise as drugs, peptides have the major disadvantages of backbone flexibility and susceptibility to in vivo degradation. A chemical 'staple' can be introduced into the peptide that restrains the backbone, thereby stabilizing the desired a-helical structure and facilitating the synthesis of improved candidates12,13. SAH-p538 was the prototype of a P53 hydrocarbon-stapled a-helical peptide inhibitor14, while more recently ATSP-7041 was designed from a phage display sequence and exemplifies the most potent and specific stapled a-helical peptide inhibitor of both MDM2 and MDMX with good pharmacological properties that has been reported to date15,16.

Computational free-energy methods based on physical principles have made significant advances towards the prediction of protein structure as well as the binding properties of small molecule ligands17,18. Computational studies have explored aspects of protein-peptide binding, including those that target the p53-MDM2 interaction19-23. As a result, we adapted MELD (Modeling by Employing Limited Data)24,25, an enhanced sampling technique developed to make protein-structure predictions, to find binding modes and assess binding affinities in protein-peptide complexes26. MELD has the unique ability to accelerate sampling in molecular dynamics simulations by using loose and vague information, rather than precise constraints. Here, the constraints we


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impose are hydrophobic interactions between the peptide and selected residues on the protein. However, unlike approaches that enforce specific contacts, MELD uses more general and less precise constraints, enforcing only a given number out of a set of possible contacts.

Here we apply MELD to the binding affinity prediction for stapled a-helical peptides previously described in the literature15,16 in order to benchmark the approach. We find that, where applicable as determined from structural information and binding affinity data, the experimentally observed binding mode can be recovered computationally. As the stapled a-helical peptide structure becomes more rigid, we obtain larger populations of the dominant binding modes than their linear counterparts26. We show that MELD is successful at predicting the binding modes and rank-ordering the binding affinities of these stapled a-helical peptides (i.e., classifying whether they will be weak vs. strong binders to MDM2 and MDMX).

2. The MELD Method We have adapted MELD, a free-energy based and information accelerated algorithm24,25 to uncover binding modes and estimate relative binding affinities of peptide-protein complexes.

MELD combines sparse, ambiguous information with

sampling driven by molecular dynamics simulation. Information is typified by ‘instructives’ such as “make a hydrophobic core.” This statement is parsed in MELD as a set of constraints that enforce on average one hydrophobic contact per hydrophobic residue. The crossing of barriers on the landscape (both arising from the


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force field and constraints) is facilitated by employing a Hamiltonian and temperature replica exchange approach where constraints are relaxed and the temperature is increased as the system ascends the replica ladder.

Presently, the system is comprised of one protein and two peptides (labeled A and B) interacting in implicit solvent. Our instructive is either “peptide A binds to the protein and peptide B is in a reference state B*” or “peptide B binds to the protein and peptide A is in a reference state A*.” The binding instructive is simply that peptide hydrophobic residues must make on average one contact with a chosen set of hydrophobic residues (see Section 3).

The initial configuration and the constraints do not

presuppose a specific binding mode. In the case of linear peptides binding to MDM2 or MDMX, we have shown in a companion work26 that this method is capable of successfully finding binding poses starting from a randomly defined unfolded conformation. Full details of the MELD technique for peptide-protein binding are also provided in Reference 26.

An assessment of relative binding affinities is, in principle, possible with this technique. The free energy associated with the ratio of populations of the peptide B to peptide A in the bound state, DGMELD, can be related to the relative free energy of binding DDGBA, by the following expression26: 𝑝 Δ𝐺MELD = −k b T ln 0 𝑝1 = ∆∆𝐺10 + ∆𝐺0∗ 0 − ∆𝐺1∗ 1 (1)


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The term in parentheses is the free energy difference between bringing peptides A and B from the free, purely solvated to the reference state. The reference state is chosen such that the peptide is confined in a spherical shell beyond the interaction radius with the other species. This confinement acts only on the center of mass and is independent of peptide identity. Therefore, the expression DGA*A-DGB*B is zero. Eqn. 1 will only yield precise free energies in the limit where the sampling is sufficient such that the optimal binding poses are captured and converged. Furthermore, the errors inherent in the choice of force field and solvation model will also impact the results and thus, in order to minimize this effect, peptides should be close in size in order to make a reliable comparison26.

Our definition of “bound” is permissive and includes all possible binding modes of the peptide with the protein consistent with MELD instructives (see below). As our method allows the peptide to explore a wide region of conformation space, transient binding poses may be over-weighted due to limited sampling time.

Instead of considering all

possible bound states as in Eqn. 1, one can also consider only the binding in a specific pose, e.g. an experimentally known binding mode. In this case, the populations are scaled by the size of the population associated with a given binding mode.

3. Simulation Details MELD has been developed as a plugin to the OpenMM library27. It is optimized to run on GPU hardware using a replica exchange scheme where a single GPU is assigned to each replica system. 30 replicas are presently utilized, where MELD constraints are


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turned off as the temperature is raised from 300 K to 500 K. In order to prevent unfolding at high temperatures, the protein backbone is constrained during the course of the simulation, although it is allowed to fluctuate and side chains retain their flexibility. No restraints are placed on the peptide’s internal degrees of freedom.

The MDM2 structure was taken from chain A of PDB 1YCR28 and the MDMX structure was from chain A of 4N5T16. The internal structure of the stapled peptide was initialized based on chain B of PDB 4N5T, and the appropriate mutations were introduced to this structure. No internal restraints are placed on the peptides and during the simulations the internal peptide conformation folds and unfolds repeatedly (in the unbound state) as allowed by the staple. An illustration of the range of conformations sampled is given by Figure S1 and Movie S1 in the Supporting Information. The peptide-protein complex was initialized with the peptides placed in random positions far away from the binding site so that the initialization is unbiased with respect to choice of binding mode.

A reliable assessment of the peptide binding populations requires well-converged MELD simulations. We generate at least a one microsecond trajectory for all systems, however, the dominant binding modes can typically be identified within several hundred nanoseconds. In Supporting Information Figures S2 and S3, we show that our REMD protocols yield consistent bound structures within the replica ladder and exhibit sufficient mixing amongst replicas.


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Sampling of bound states is accelerated by the inclusion of information constraints within the MELD framework.

As the binding of p53 to MDM2/X is known to be

mediated by hydrophobic interactions within the context of the a-helical peptide sequence of p53 and its interface with its target protein, a constraint was designed which declares that a number of hydrophobic protein-peptide contacts equivalent to the number of hydrophobic residues on the peptides are enforced. Protein residues are considered in this interaction if they lie within 0.8 nm of the four residues (G58, D68, V75 and C77 from chain A of PDB 1YCR) that were shown experimentally to be critical for MDM2/p53 binding29. The residues chosen for MDMX are simply taken by an alignment with MDM2. Other choices (for example all hydrophobic residues at or near the surface of the protein structure) are also possible. A biasing potential is activated when the peptide-peptide center of mass distance is less than 3 nm to prevent aggregation. Further details, including the form of the restraint potentials and how they are scaled across the replica ladder, are provided in the Supporting Information of Reference 26.

Binding poses and their populations are typically extracted from the final 750 ns of the lowest temperature replica of the MELD simulation. Useful information can also be extracted from other replicas, particularly if schemes are utilized that reweigh higher temperature conformations30.

Clustering of the trajectory was performed using a

recently developed, density-based algorithm31 with a density cutoff of 2.0 Å.

The

distance metric used for clustering is the peptide RMSD of the protein-aligned


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conformation. The RMSD includes the alpha and beta carbon positions of the peptide residues in positions 19-26 (see Table 1).

A set of stapled peptides from the literature was studied15,16. The peptide names, along with their sequences are given in Table 1. The activity ranges from single digit nM to completely inactive to MDM2 or MDMX (see Table S1 in Supporting Information). As our method works best when assessing free energy differences between peptides of the same length26, our computations pair together peptides of the same length when computing the relative binding affinities. In the results presented in the main text, the reference peptide was chosen to A22 for binding to MDM2 and ASTP-4641 for MDMX for 12-residue peptides.

Results when the peptide that is the basis for the alanine

scan performed by Guerlavas et al.15 is chosen as the reference are given in the Supporting Information. Simulations for the pair of 14-residue peptides ATSP-7342 and ATSP-7041 were also performed, and an additional run was performed to extract the binding modes of the 16-residue SAH-p53-8.

MELD for peptide binding is currently formulated for use with implicit solvent models, using the Amber FF12sb force field32 in combination with the GBNeck2 solvation model33. Force field parameters for non-standard amino acid residues (labeled R8, S5 and Cba in Table 1) are taken from the GAFF potential34 where charges are computed using the AM1 model35.


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4. Results of Predicting Binding Poses and Affinities 4.1 MELD can recover experimentally determined binding modes Figure 1 shows that we recover crystallographically determined binding poses for SAHp53-8 to MDM2 (PDB 3V3B14) and ATSP-7041 to MDMX (PDB 4N5T16). As discussed below, the depicted structures represent the cluster center of the most populated structure bound to the peptide that is extracted from the MELD trajectory.

The cluster corresponding to the experimentally observed binding mode is centered less than 1.5 Å from the experimental mode and its size is considered the population of the experimental binding mode. Figure 2 shows the populations of this crystal-like pose for all stapled peptides that we have studied. The crystal-like pose (that is where side-chains at positions Phe19, Trp23 and Leu26 are buried into the hydrophobic binding pocket) forms the most populated cluster for the majority of the peptides we have studied. The exceptions are when one of the anchor residues is mutated to alanine as in peptides A19, A23, A26, and ATSP-7342. For peptides A22, ATSP-3900, and ATSP-4641 which were used as the reference in the peptide pair and therefore run in 10 separate simulations, the standard error of the mean in the populations is ≈5%. These results underscore the utility of MELD as a tool to sample binding poses by searching out low free energy binding modes.

Alternate binding modes persist in our calculations. The next-most populated pose for binding to MDM2 is depicted in Figure 3A. The populations corresponding to this pose are shown in Figure S4 in the Supporting Information. It can be seen that the C-


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terminal leucine and the staple itself are buried in the binding pocket. This indicates that the hydrocarbon staple has some affinity for the binding pocket, likely due to its hydrophobic character. An analogous mode is also present in the MDMX binding results. The persistence of such conformations may be an artifact of a combination of the force field and solvation model or may be alleviated by extended simulation time. Furthermore, such conformations may complicate the evaluation of the populations of different peptide binders and the relative binding free energies, as discussed below. 4.2 MELD can estimate relative binding free energies Now that we have shown that MELD can recover peptide-protein binding poses, let us consider its capability to compute relative free energies of binding. In Figure 4, we show the relative binding free energy of the stapled a-helical peptide to MDM2 and MDMX extracted from our simulations using Eqn. 1 as compared with experimental values15,16. As noted in recent work outlining this method of studying protein-peptide binding, converging populations to the point where free energies can be evaluated requires good statistics and long simulations26.

In the current dataset the typical

standard error of the mean in the population (pA or pB) is < 5%, although some simulations exhibit longer equilibration times.

In the present dataset, the overall

agreement is quite reasonable as the majority of runs show a deviation of less than around 1 kcal/mol from experiment. In Figure 4, for 12-residue peptides, we chose the experimentally-known, moderately-binding peptide A22 and ATSP-464115 as the reference peptides for binding to MDM2 and MDMX, respectively. If the stronger-


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binding peptide on which the alanine scan was based is chosen (ATSP-3900), the results are also reasonable (see Figure S5 of the Supporting Information).

Overall, the most prevalent discrepancies arise from the over-binding of weak binders. The two points labeled "X" in Figure 4 denote the relative affinities of the strongest binder (ATSP-7041) with respect to the weakest binder (ATSP-7342) for targets MDM2 and MDMX. The experiments predict that the population of the weaker binder should be

Molecular Simulations Identify Binding Poses and Approximate

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