In Silico Modeling of PROTAC-Mediated Ternary Complexes

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In silico Modeling of PROTAC-Mediated Ternary Complexes: Validation and Application Michael L Drummond, and Chris Willaims J. Chem. Inf. Model., Just Accepted Manuscript • DOI: 10.1021/acs.jcim.8b00872 • Publication Date (Web): 04 Feb 2019 Downloaded from http://pubs.acs.org on February 5, 2019

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Journal of Chemical Information and Modeling

In silico Modeling of PROTAC-Mediated Ternary Complexes: Validation and Application Michael L. Drummond* and Christopher I. Williams Chemical Computing Group, Montreal, Quebec, H3A 2R7, Canada

ABSTRACT. In this work, four methods are described and validated for generating in silico ensembles of PROTAC-mediated ternary complexes. Filters based on characteristics of the proposed ternary complexes are developed to identify those that resemble known crystal structures. We then show how to use these modeling techniques a priori to discriminate the PROTACmediated degradation behavior of a mutant protein vs. its wild type, of three closely related targets, and among three different PROTAC molecules.

INTRODUCTION. Over the past decade, intense drug discovery research has focused on a class of bifunctional molecules commonly known as proteolysis-targeting chimeras, or PROTACs. These molecules, a subclass of the larger categories of molecular glues1 and chemical inducers of proximity,2 function by non-covalently linking a targeted protein to the substrate adaptor protein of an E3 ligase,3 which naturally functions to polyubiquitinate proteins in vivo; after being so tagged, the labeled target protein is then sent to the Ubiquitin Proteasome System (UPS), where it is degraded by the 26S proteasome.4 A PROTAC molecule consists of three parts: a moiety that

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binds to the target protein, a (generally flexible) linker, and a second moiety that binds and recruits the E3 ligase. While originally PROTACs used peptidic E3 ligase substrates,5 in 2010 Itoh et al. reported6 PROTACs constructed entirely from small molecules. This finding reignited interest in PROTAC research, as can be seen from numerous reviews,7–13 as well as a recent issue14 of J. Med. Chem. dedicated entirely to protein degradation as a therapeutic tool orthogonal to classic small molecule-driven inhibition. The reported advantages of the PROTAC modality are many, with potentially game-changing significance. Foremost is the potential to target proteins traditionally regarded as “undruggable,” such those that can only be targeted with weak binders15 or that only possess non-enzymatic functions (e.g., scaffolds16,17). PROTACs that act substoichiometrically – i.e., capable of labeling multiple proteins for degradation in a catalytic fashion – have also been reported,18 as have PROTACs that successfully cause degradation despite the occurrence of mutations known to be deleterious to small molecule inhibition.19 PROTAC-mediated degradation has also been shown to be effective even when traditional inhibition is not.20 Moreover, the multifaceted nature of PROTAC molecules often imparts a selectivity not observed in the original target binding moiety,21 due to the structure of the ternary (three-body) target-PROTAC-ligase complexes they engender. Despite these many advantages, at the time of this writing no PROTACs have yet proceeded to the clinic. Although issues pertaining to PROTAC properties, such as their bioavailability and metabolic stability, are certainly substantial (due to their beyond-rule-of-five nature),22 the initial discovery phase for these molecules is in and of itself particularly challenging. Multiple reports have shown that each of the constituent parts of a PROTAC can impact its efficacy. That is, not only do the identity of the E3 and target binding moieties impact activity, but so too does the linker,


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in particular with respect to its length, its chemical nature, its flexibility, and how and where it is covalently bonded to the two binding moieties.23–28 Moreover, analysis of the first crystal structure29 of a target-ligase-PROTAC ternary complex (of only two total reports to date30) revealed the existence of all combinations of possible interactions: between the linker and both proteins, as well as between the target binding moiety and the E3 ligase protein and between the ligase binding moiety and the target protein. Thus, optimization of any individual PROTAC component may impact the behavior of the other PROTAC components, ultimately leading to complicated SAR.24 Although further experimental work will surely add clarity, computational modeling can also be used to provide much needed insight into these systems. Indeed, computational tools have already been applied to PROTAC design, albeit at varying levels of rigor. The most common practice to date is to simply dock a PROTAC into the pocket of one of its two cognate proteins, select a docking pose with an extended conformation, and then superpose the second protein onto its corresponding solvent-exposed binder.19,31,32 Other techniques used to date include those based on protein-protein docking,30,33 molecular dynamics,15,29 and an analysis of the conformational ensemble of the PROTAC absent its proteins.34 Perhaps the most extensive work to date was detailed by researchers at Pfizer,35 who developed a computational workflow based on evaluating the steric compatibility between a PROTAC conformational ensemble and post hoc appended proteins; importantly, this work also utilized a simple scoring function – a count of the number of non-clashing poses – to judge the ability of a PROTAC to facilitate productive ternary complexes. Although PROTAC design has certainly already benefitted from these computational approaches, none of the published methods thus far have been demonstrated to be applicable across multiple targets, using different E3 ligases, different binding moieties, or different linkers. In this


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work, we describe a suite of in silico tools suitable for addressing the many varying facets inherent to PROTAC development, validated across multiple design scenarios. As will be shown, these tools not only successfully generate an ensemble of productive ternary complexes, but more importantly score the likelihood that a PROTAC can lead to effective protein degradation. MATERIALS AND METHODS. Due to the aforementioned tendency for the different components of a ternary complex to interact with one another,29 it is a priori unclear how a robust, diverse ensemble of putative ternary complexes should be generated. Conceptually, at one extreme, PROTAC conformations could be sampled entirely separately from their binding proteins; at the other extreme, all three pieces of a ternary complex would be present during sampling. In this work, rather than prejudge which approach is most appropriate, instead we present results for four different computational Methods covering these various scenarios. Full computational details are presented below, and are summarized graphically in Figure 1. Due to differences among the Methods, the details of scoring, i.e., the identification of potentially useful poses amidst the complete ensembles, differ across the four Methods, as will be discussed in the Results section. All Methods were written in the Scientific Vector Language (SVL) integrated into the MOE modeling package,36 and are freely available upon request.


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Figure 1. Graphical summary of the four Methods detailed in this paper. See text for full details. In brief, the four Methods can be summarized as follows: 

Method 1: The entire ternary complex is sampled all at once



Method 2: PROTAC conformations are sampled independently, followed by post

hoc addition of rigid-body proteins 

Method 3: The PROTAC is sampled in the context of one of the proteins, with the

second added afterwards 

Method 4: PROTAC conformations are sampled independently of the proteins (and,

indeed, a conformational database from Method 2 can be used, or vice versa), but possible ligase-target arrangements are provided via protein-protein docking. Method 1. For Method 1, it is first necessary to provide a starting orientation of each proteinbinding moiety in its respective protein, such as would be produced via docking or (ideally) a cocrystallized protein-binder complex; this binding moiety is referred to as an “anchor” below.


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With this input data, a starting conformation of the PROTAC is constructed first by attaching each anchor group (fixed in its protein-bound conformation) to the initial, user-provided conformation of the PROTAC’s linker group. The proteins are then positioned around their respective anchor groups so as to match the initially provided protein-anchor group orientations, thereby forming an initial configuration of the entire ternary complex. This initial configuration often has significant protein-ligand and protein-protein clashes, due to the separate starting environments for the two protein/anchor group complexes and the initial linker conformation. In order to minimize the production of sterically clashing ternary complexes, the PROTAC conformation is automatically adjusted to adopt an extended conformation. Rotatable bonds of the PROTAC are modified to values that minimize volume overlap, and then restricted to these values subsequent to PROTAC conformational sampling. These restricted rotatable bonds are shown in black in Figure 1. This procedure is repeated for the next adjacent rotatable bond. If the search encounters a rotatable bond where there is no overlap between the dihedral terminal atom and the protein at any dihedral angle, the dihedral angle is set to 180°, so as to generate a fully extended ligand conformation. These non-restricted rotatable bonds are shown in green in Figure 1. After generating the extended conformation of the PROTAC, additional putative conformations of the ternary complex are generated by holding one protein and its anchor group fixed in space while sampling the unrestricted rotatable bonds of the linker (green bonds in Figure 1). Only one rotatable bond, randomly chosen, is sampled at each individual step of the search, and the second protein is moved in concert with the motion of the anchor group connected to the sampled linker rotatable bond. The generated conformation of the ternary complex is then scored using a variety of metrics (see below), and the conformation is either retained or rejected based on user-specified


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thresholds in ligand strain energy and protein-protein volume overlap. In Monte Carlo style, if a conformation is rejected, the search reverts to the last accepted conformation and then proceeds by sampling a new, randomly chosen rotatable bond. If instead a generated conformation is accepted, the sampling continues starting from this acceptable conformation. The search terminates when either the total number of sampling steps exceeds a user-defined maximum threshold, or if a new conformation is not produced after a user-specified maximum number of contiguous failures. Method 2. As in Method 1, it is necessary to provide two protein-ligand complexes, such as produced via X-ray crystallography or through docking, consisting of each protein with its binding moiety. Additionally, a core for each ligand is specified via SMILES (or through selection of the atoms using the MOE GUI, from which a SMILES string can be automatically extracted). The full PROTAC must also be provided via a MOE database (MDB), which can contain multiple PROTACs and can optionally contain pre-generated conformations for each PROTAC. If conformations are not provided, they are generated on-the-fly using any of the conformational generating methods in MOE (LowModeMD, Conformation Import, Stochastic, or Systematic search). The Potential settings (forcefield, implicit solvation model, dielectric settings, etc.) are taken from the main MOE settings. After a PROTAC conformational ensemble is either generated on-the-fly or read in, the two original protein/ligand complexes are then superposed onto their respective binding moieties for each conformation. During the conformational search, the userspecified core may deform from its cocrystallized position. A Core RMSD threshold is set (default: 2.5 Å, determined through visual inspection), and if either binding moiety in a free PROTAC conformation differs from the originally provided cocrystallized binder geometry by more than this threshold (after rigid body superposition), the conformation is rejected. Benchmarking data


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(not shown) suggests that a core should be generally small and rigid, as larger cores result in more rejected poses and lower hit rates. Because the PROTAC sampling occurs in the absence of any proteins, there is often significant overlap after the proteins are reintroduced (if, say, a PROTAC conformation is especially collapsed, for example). In the interface, it is possible to pre-filter dramatically clashing conformations, based on all-atom protein-protein volume overlaps (default: 10000 Å3) and backbone-backbone volume overlaps (default: 2500 Å3). These settings are applied before the subsequent filter S_all described later in the main text of this article. Any generated ternary complexes that pass the specified filters are written to a MOE database, along with automatically calculated information that may prove useful for identifying competent ternary complexes (energetic criteria, PROTAC strain, patch-based descriptors, volume overlaps, etc.). Method 3. In Method 3, only one of the proteins is included in the conformational sampling phase; including the smaller of the two is more computationally efficient. Additionally, the entirety of the PROTAC molecule, with one end bound to the pocket of the included protein, is required. As in Method 2, the user defines two (generally rigid) cores. The core belonging to the binding moiety of the protein included in the sampling is tethered during the conformational sampling, so that it cannot dissociate from the binding pocket. The core that corresponds to the protein not included in the conformational search is instead defined as a rigid body, to prevent the deformation of the binding moiety during conformational sampling that sometimes occurred in Method 2. Additionally, protein residues near the first core are tethered during the conformational search, to afford some flexibility to the protein-ligand binding pocket. The remainder of the PROTAC molecule, i.e., the (generally flexible) linker and any non-rigid portions of either binding moiety, are unconstrained during the conformational search.


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The conformational search in Method 3 uses LowModeMD exclusively – settings for this algorithm are exposed to the user, but by default are set to be expansive, so as to robustly sample potential protein-PROTAC conformations. After the search completes, the second protein is superposed onto its respective binder, which was held rigid during the conformational search. As with Method 2, poses can be rejected at this phase based on protein-protein all atom or backbonebackbone overlaps. All poses that pass these filtering criteria are written to an output MDB, where patch-based descriptors are then calculated. Method 4. As with Method 2, the input required for Method 4 is two protein-ligand structures, where the ligand is the corresponding PROTAC binder moiety. Also similar to Method 2, cores for each binding moiety are defined by the user, and will be used as described later. As with the preceding Methods, the ideal starting inputs for Method 4 are two separate protein-binder crystal structures, although future work will demonstrate that useful predictions can still be generated if small-molecule docking is used to place the actual target binding moiety of the PROTAC into the protein pocket taken from a crystal structure of the target cocrystallized with a similar (but not necessarily identical) target binder. Once the input is provided, protein-protein docking of these two protein-ligand complexes is the first phase of Method 4. Protein sidechains are automatically repacked during this procedure, and putative arrangements of the two protein-ligand complexes are also subjected to rigid-body minimization. It is also possible to specify a database with pregenerated docking poses, such as can be produced with an earlier run of Method 4, or as a standalone MOE calculation, or as imported from a third-party software. It was decided early in the construction of Method 4 to dock two protein-ligand complexes (rather than two apo proteins), as it is known that certain PROTAC binding moieties, particularly the immunomodulatory imide drugs (IMiDs) such as lenalidomide


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and pomalidomide,37,38 “fill in” gaps on the surface of cereblon, thereby expanding its degradation profile beyond its native substrate.1 As will be discussed in the Results section below, multiple protein-protein docking runs can optionally be performed, where each simulation is biased to match up a hydrophobic protein patch near (i.e., within 10 Å) the PROTAC binding pocket against a similar hydrophobic patch on the other protein, in an all-against-all fashion. A similar approach has been used to generate initial positions prior to generating trajectories, in a molecular dynamics context.15 If this biasing option is selected, all docking poses are collated, and then poses that are within 4 Å Cα RMSD of an already observed pose are discarded as duplicates. After the final protein-protein docking database is constructed, protein patch-based descriptors (see Results) are (optionally, but on by default and highly recommended) calculated. Only protein-protein docked poses with patch-patch contact surface areas above those specified by the user are considered for superposition against the PROTAC conformational ensemble (see below). The next phase involves generation of the conformational ensemble of the specified PROTAC molecule, which proceeds in a fashion identical to that described above for Method 2. (Indeed, the PROTAC conformational databases generated with either Method are interchangeable). After this database is generated, an initial check is performed to evaluate whether an individual proteinprotein docked pose is roughly compatible with an individual PROTAC conformation. The pocketpocket distance for all protein-protein docked poses is calculated, taken as the distance between the centroid of the two user-specified cores. Similarly, for each PROTAC conformation, the centroid-centroid distance, again based on the provided core definitions, is calculated. If the interpocket distance for a protein-protein pose is within 5 Å of the intraligand distance, then the two separate conformations are deemed roughly compatible, and they are combined. This pre-


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screen prevents grossly distinct conformations from being combined, a necessity given that, if unscreened all-against-all matching were attempted, ~10,000 PROTAC conformations would be combined with 100-1000 protein-protein docking poses, resulting in ~10,000,000 putative combinations, the vast majority of which would be incompatible. If a PROTAC conformation is deemed compatible with a protein-protein docking pose, then the two are combined in the following manner, graphically summarized in Figure 2. First, the PROTAC conformation is brought into the frame of reference of one of the cores in the proteinprotein docked pose by rigidly moving the entire PROTAC, based on superposing it onto its match in the protein-protein docked pose. Next, the other end of the PROTAC conformation, as defined by its user-specified core, is rigidly superposed onto its counterpart in the protein-protein docked pose, but the remainder of the PROTAC is left in its position defined by the first superposition. As a result, there is some strain in the PROTAC, which is then relieved by minimization (performed absent any proteins). To gauge the amount of strain, first the initial doubly-superposed conformation is stored. Next, three minimizations are performed. First, the matching core atoms are fixed, and the remaining atoms are strongly tethered, followed by minimization. Next, the core atoms are unfixed, all atoms are weakly tethered, followed by minimization. Finally, all tethers are removed, and the PROTAC is relaxed without constraints. This three-part minimization scheme reduces the effect of bonds unduly stretched by the double-core-superposition procedure described above. After the final, fully unconstrained minimization, the coordinates of the minimized PROTAC are compared to the coordinates after the double-superposition but before the three-part minimization scheme. This all-atom RMSD is termed Core RMSD in the main text. A low value (say, < 1 Å) indicates that the independently sampled PROTAC conformation was a close match


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for the accompanying protein-protein docked pose, and there was minimal distortion as the PROTAC conformation was superposed onto the protein-protein docked pose.

Figure 2. Graphical summary of the procedure used to combine conformations from the sampling of the independent PROTAC with protein-protein docked poses of two protein-binding moiety complexes. a) The starting point is a single docked complex of the ligase-binder (cyan ribbons & atoms) and target-binder (green ribbons & atoms). From there, the proteins are removed, and a single conformation of the PROTAC is introduced (gold carbon atoms), leading to b). The userspecified Core 1 of the PROTAC conformation (gray atoms) is superposed onto the corresponding positions of Core 1 in the protein-protein docked position (black atoms). The remainder of the PROTAC is superposed as well, generating c). The user-specified Core 2 of the PROTAC conformation (magenta atoms) is superposed onto the corresponding positions of Core 2 in the protein-protein docked positions (purple atoms). Unlike the previous superposition, the remainder


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of the PROTAC is not rototranslated, resulting in d). The protein-protein docked atoms are then removed to form distorted structure in e), the isolated PROTAC with its two cores in their positions in the protein-protein docked complex. From there, three minimizations with differing levels of PROTAC restraints (see text) are applied, generating the final predicted PROTAC conformation in f). The Core RMSD metric used throughout this work is between the distorted PROTAC structure and e) and the final, smoothly minimized structure in f). If the fully formed ternary complex has passed both the patch-based threshold and the Core RMSD threshold, it is written to the final database. However, there may still be significant clashing between the proteins and the PROTAC, as they were sampled independently. (Note that, unlike Method 2, there is no appreciable protein-protein clashing, as a result of the protein-protein docking procedure). Optionally, to alleviate this clashing, a restrained minimization protocol may be performed on the final ternary complexes. The backbone atoms of each of the two proteins are defined as independent rigid bodies, while all sidechain atoms and the entirety of the PROTAC are unconstrained, followed by minimization. This protocol generally keeps the overall configuration of the ternary complex intact, but allows for movement of sidechains and/or the PROTAC to alleviate clashing. Occasionally, strands of atoms interpenetrate between the PROTAC and one or both of the proteins, and thus minimization is unable to relieve the clashing. These poses can readily be identified via a large, positive total interaction energies, or through PROTAC-protein overlap values, which are also calculated. It is likely that a subsequent repacking step would fully ameliorate these interpenetrating structures, but this improvement has not yet been implemented.


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RESULTS. Validation: Generation and identification of crystal-like ternary complexes. The first crystal structure of a ligase-PROTAC-target ternary complex was reported in 2017,29 where the E3 ligase was the von Hippel-Landau (VHL); the target was the second bromodomain of Brd4, a member of the bromo- and extra-terminal (BET) family; and the PROTAC molecule was MZ1,21 built from JQ1,39 a pan-BET inhibitor, linked via three ethylene glycol monomers to VH032,40 a small molecule ligand of VHL. This crystal structure, deposited as PDB code 5T35 (resolution 2.7 Å), contains two additional chains, Elongin B & C, which are part of the larger E3 ligase structure and were manually removed prior to all modeling, to focus on the most parsimonious view of a ternary complex. The definition of a “crystal-like” pose used in this Validation phase is an in silico pose with a Cα RMSD of ≤10Å after rigid body superposition onto the coordinates of 5T35; this definition is commonly used as the upper limit for an “acceptable” pose in protein-protein docking contexts.41 Each of the four Methods were applied to generate ensembles of possible ternary complexes. The various Methods generate different numbers of possible conformations of ternary complexes: the sampling in Methods 1-3 is capped (by default) at 10,000 conformations, whereas the independent PROTAC and protein-protein samplings in Method 4, when combined, can generate hundreds of thousands of conformations. Ensembles of these magnitudes are impractical for downstream analysis, and thus it is critical to score these initial ensembles, to a priori filter out ternary complexes that are far from the crystallographically observed configuration. It may also be useful to cluster generated ternary complexes and use, e.g. cluster size and cluster diversity as additional scoring metrics. Efforts along these lines are currently underway, but will not be further discussed below in this current work.


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The filters proposed below depend on the Method and were determined empirically. In identifying possible filters, first each computationally generated ternary complex was superposed as a rigid-body onto a reference crystal structure, such that the target protein was perfectly superposed, followed by evaluation of the Cα RMSD for the ligase. Next, various properties were evaluated – a full listing is reported as Supporting Information, but in general includes interaction energies between the various parts of each ternary complex, properties of the independent PROTAC conformations, and metrics based on both volume and protein patch-overlap (see below). Correlation plots between Cα RMSD and all calculated properties were investigated, and any property that separated the 24 106 44 86 1467 1660 BD2 Brd3 > Brd3 MZ2 34 88 70 75 1034 1595 Brd2 Brd2BD2 MZ2 25 98 54 67 542 745 Agreement?** N N N N Y Y BD2 Brd4 MZ3 Brd4 > 37 133 62 93 2008 2427 Brd3 ~ Brd3BD2 MZ3 40 145 94 127 1418 2213 BD2 Brd2 Brd2 MZ3 34 125 67 82 631 950 Agreement?** N N N N ~ ~ *Qualitative assessment of the Western blots in Figure 2a of Ref. 21 (BRD4 long was used for the Brd4 data). **Qualitative assessment of whether the modeled results agree with the experimental data. A tilde indicates “somewhat.” As can be seen in Table 4, often the number of ternary complexes generated with Method 4 correlates quite well with the experimental ordering, particularly for the E filter set (with some ambiguity resulting due to a degree of subjectivity). However, more disappointing is the fact that, across the three PROTAC molecules, although there is a clear trend in the experimental data for MZ1 to be most effective at enabling protein degradation, followed by MZ2, and then MZ3, for the computational results only the A filter set consistently ranks MZ1 as the most effective PROTAC irrespective of the target BET. This discrepancy may be a consequence of differing properties (e.g., cell permeability, chemical stability, etc.) between MZ1, MZ2, and MZ3, a complication beyond the scope of any of the Methods in this work to account for. However, it should be noted that only two Method 4 simulations were performed for this analysis: one with a single protein-protein docking run, and a second with multiple protein-protein docking runs biased to favor matching hydrophobic patches across the two proteins. From there, filtering is a simple post-processing operation: corresponding filters were applied to the former simulation to generate data for 4A and 4C, and to the latter to generate the Biased datasets (4B, 4D, 4E, and 4F). Thus, while


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more simulations are needed to confirm or refine these findings, if it indeed holds true that the E set of filtering criteria is better at comparing different targets and the A criteria is better at comparing different PROTACs, only two variants of Method 4 need be run, which is not an undue computational burden. DISCUSSION AND CONCLUSIONS. In both of the publications to date that report29,30 crystal structures of PROTAC-mediated ternary complexes, the authors used the structure – along with computational modeling – to rationally propose new PROTAC derivatives with enhanced selectivity, thereby decisively demonstrating the power of this paired structure-based/modeling approach to PROTAC design. While further crystal structures will surely follow, we have demonstrated that the Methods in this work, particularly the protein-protein docking-based Method 4, are accurate enough to complement ongoing structural campaigns, offering reasonable, crystallike geometries for ternary complexes, which can serve as starting points for testing hypotheses and refining principles of PROTAC design. Moreover, after applying judicious filters – which were determined based on reproducing known X-ray crystal structures – computationally generated ensembles of ternary complexes can be scored via a simple count of acceptable poses. This count was shown to be able to discriminate the degradation tendencies for a mutant vs. its wild type, amongst closely related targets, and to a lesser degree among different PROTAC molecules as well. It is also noteworthy that the non-crystal-like “false positives” generated by Methods 1-4 are not necessarily wrong, for as noted by Nowak et al.,30 there is a certain amount of plasticity in the arrangements of PROTAC-mediated ternary complexes, i.e., multiple low-energy configurations may exist. In that sense, ternary complexes that differ from the observed crystal structure but otherwise pass the filtering criteria may instead be merely thus far unobserved.


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However, even in the most successful cases, where the Methods of this paper produced filtered databases of ternary complexes with ~40% crystal-like poses, it is still difficult to a priori identify which 40% of the filtered output is crystal-like. Efforts to address this remaining challenge by applying various clustering algorithms and thresholds are currently underway in our lab, and of course further work by us and others to develop even more effective means to score or rank productive ternary complexes can continue. Particularly insightful will be the application of the Methods of this paper to additional systems, affording further refinement of the filtering criteria proposed in this initial work. In the meantime, it seems reasonable to state that if the ultimate goal is to use modeling techniques such as those detailed in this work as a surrogate for solving X-ray structures of ternary complexes, then this goal will likely be unmet for the foreseeable future – analogous to how computational protein-protein docking in a generic, non-PROTAC context cannot yet routinely generate crystal-like poses as the single best scoring answer,43 likely due to the typical inaccuracy of these scoring functions.44 Indeed, MOE’s scoring function for the docked target-binder/ligase-binder complexes was unable to reliably identify crystal-like poses when it was applied as a putative filter. Nonetheless, even with this limitation, there is still value in applying the ternary complex modeling algorithms of this work. Hypothetical structural arrangements of target-ligase complexes can be proposed in atomistic detail, which can then be confirmed or rejected via experimental techniques such as structure-guided site-directed mutatgenesis. Moreover, the data presented in this work indicates that using these tools in a relative context is informative. For example, the modeled QVK mutant leads to fewer “reasonable” ternary complexes vis-à-vis the wild type. Specific questions about whether the WT is 1.5x more active (based on the data for Method 4A [51 WT/34 QVK] from Table 3), or 2.02x more active (4B), etc. seem beyond the precision possible


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with Method 4. (Indeed, achieving such quantitative precision is also a challenge for the experimental, qualitative blotting techniques.) However, despite this imprecision, the overall result of Method 4 is still valuable, insofar as the QVK mutant is generally less capable of forming known (i.e., crystal-like) ternary complexes, and thus it is predicted to be less susceptible to degradation via a PROTAC-mediated mechanism – in line with experiment. With respect to the design of the PROTAC molecules themselves, in a recent paper, Qin et al. reported25 an exquisitely potent (single-digit picomolar) PROTAC, QCA570, targeting the same BET family targeted by the PROTACs discussed in this current work. When comparing QCA570 to the other BET-targeting PROTACs noted above, it is striking how much more rigid QCA570 is, particularly due to the use of two alkynyl spacers. QCA570 was developed through traditional (and very labor-intensive) medicinal chemistry optimization, but our hope is that tools such as Method 4 can suggest likely arrangements of ternary complexes. From there, the parent PROTAC linker can be modeled using well-known tools such as scaffold hopping or another recent tool, also implemented in MOE, that maps out the shortest path along receptor surfaces.45 When paired with these techniques, Method 4 should facilitate the targeting of novel or allosteric pockets – even on the E3 ligases themselves46 – and can be applied to new E3 ligases3,28 beyond those most commonly utilized (VHL and cereblon). Finally, there is another consideration to UPS-driven protein degradation that has not yet been addressed: the necessity for an accessible lysine on a putative ternary complex that can be ubiquitinated. General characteristics of these lysines have been elucidated using bioinformatics approaches,47 and it has also been demonstrated that multiple lysines can be ubiquitinated in a PROTAC-bridged ternary complex.28,29 A deeper analysis of the characteristics of these lysines can potentially add further precision to the modeling results detailed here, as of course will further


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simulations using Method 4 to test and refine the filtering criteria described above. In the meantime, structure-based modeling of PROTAC-bridged ternary complexes, such as enabled via the Methods in this work, should facilitate rational PROTAC design, alongside new advancements in real-time kinetic profiling48 and continued efforts in experimental structural characterization. ASSOCIATED CONTENT Supporting

Information.

The

following

files

are

available

free

of

charge:

Calculated properties of ternary complexes investigated for their ability to identify crystal-like predictions from larger ensembles; Example of a calculated property of ternary complexes that can identify crystal-like poses; Full filtering possibilities for Method 4 for the VHL-MZ1-Brd4BD2 system; Pairwise Cα RMSD matrix for the crystal-like poses produced by Method 4B for the VHLMZ1-Brd4BD2 system. AUTHOR INFORMATION Corresponding Author *Correspondence to: [email protected] ACKNOWLEDGMENT The authors would like to acknowledge Yilin Meng and Ye Che of Pfizer for helpful discussions. ABBREVIATIONS PROTAC, Proteolysis-Targeting Chimera; UPS, Ubiquitin Proteasome System; SAR, Structure Activity Relationship; SVL, Scientific Vector Language; MOE, Molecular Operating Environment; MD, Molecular Dynamics; VHL, von Hippel-Landau; BET, Bromo- and ExtraTerminal; RMSD, Root-Mean-Square Deviation


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For Table of Contents use only In silico Modeling of PROTAC-Mediated Ternary Complexes: Validation and Application Michael L. Drummond and Christopher I. Williams


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In Silico Modeling of PROTAC-Mediated Ternary Complexes

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