We first sought to establish whether the blebbistatin pocket is open in any blebbistatin-free myosin experimental structures or if it is a ‘cryptic’ site. Cryptic pockets are cavities that open and close as a protein fluctuates in solution but are typically closed and therefore hidden in experimental structures.

To assess whether the blebbistatin-binding site is cryptic, we queried the Protein Data Bank (PDB) for an exhaustive set of myosin motor domains, yielding 124 structures that were not crystallized with blebbistatin or blebbistatin derivatives (see Materials and methods). We then assessed the degree of pocket opening at the blebbistatin binding site using the LIGSITE pocket detection algorithm (Hendlich et al., 1997). Briefly, LIGSITE finds concavities on a protein surface by identifying grid points that are surrounded by protein but not in contact with protein atoms (see Materials and methods). Those LIGSITE grid points that were within 5 Å of an aligned blebbistatin molecule were considered part of the blebbistatin pocket. The pocket was considered open if its volume matched or exceeded that of the blebbistatin-bound, or holo, structure (PDB: 1YV3).

We find that all known blebbistatin-free experimental structures of the myosin motor domain have a closed blebbistatin pocket (Figure 2A). Not a single blebbistatin-free experimental structure reaches the holo pocket volume, and most blebbistatin-free structures have less than half of the holo pocket volume. In blebbistatin-free myosin experimental structures, a leucine residue in the U50 linker (Allingham et al., 2005), a highly conserved loop in the upper 50 kDa domain, always points into the blebbistatin pocket, creating a steric impediment to binding (Figure 2A inset). While the blebbistatin binding site has previously been annotated as a cryptic pocket, previous analyses were restricted to a subset of blebbistatin-free myosin experimental structures that matched the holo structure’s sequence exactly (Cimermancic et al., 2016). Here, we have shown that all available blebbistatin-free experimental structures of the myosin family lack an open blebbistatin pocket.

Even if the blebbistatin pocket is closed in blebbistatin-free experimental structures, we reasoned that the pocket might open in excited states accessible in all-atom molecular dynamics (MD) simulations. Recent work demonstrates that in solution myosins sample a broad range of conformations driven by thermal fluctuations (Porter et al., 2020; Muretta et al., 2015), even though all myosin motor domains share a common fold (Robert-Paganin et al., 2020). To assess whether the blebbistatin pocket opens in solution without blebbistatin present, we used molecular dynamics to simulate the motor domain of human fast skeletal myosin IIA (MYH2). Fast skeletal myosin is potently inhibited by blebbistatin (average IC50: 0.3 μM Limouze et al., 2004; Várkuti et al., 2016; Radnai, 2021), so we hypothesized that its blebbistatin pocket would open extensively in simulation.

We constructed Markov State Models (MSMs) from over 80 microseconds (Table 3) of simulations of the actin-free, ADP-phosphate-bound fast skeletal myosin motor domain. MSMs of molecular simulations are network models of free energy landscapes composed of many conformational states and the probabilities of transitioning between these states (Bowman et al., 2014). We constructed MSMs of the conformations seen in the blebbistatin pocket by clustering structures in a kinetically relevant projection of backbone and sidechain dihedral angles (see Materials and methods). To measure blebbistatin pocket opening, we measured pocket volumes at the blebbistatin binding site using the LIGSITE algorithm as described above. We then quantified the probability of pocket opening based on the probability of each structure in the MSM (see Materials and methods).

In contrast to myosin crystal structures, we find that simulations reveal extensive opening of the blebbistatin pocket. The distribution of pocket volumes from simulation is substantially right-shifted relative to the distribution seen in crystal structures (Figure 2B). Even though our simulations were started from a closed blebbistatin-free conformation, all 8 long (>500 nanosecond) independent simulations of fast skeletal myosin IIA exceed the volume seen in the blebbistatin-bound crystal structure (Figure 2—figure supplement 1). Indeed, the blebbistatin pocket is open in about one-third of conformations at equilibrium (p_open = 0.31). When we visually inspected structures from simulation that had reached the holo pocket volume (Figure 2C), we find that the blebbistatin pocket geometry closely matches that of the blebbistatin-bound crystal structure (the R.M.S.D. for the structure depicted in Figure 2C was 0.55 Å when considering the backbone heavy atom and Cβ positions of residues in contact with blebbistatin). While the leucine (L270 in skeletal muscle myosin IIA) in the U50 linker always points into the blebbistatin pocket in blebbistatin-free crystal structures, in simulations this leucine residue rotates toward its blebbistatin-bound position (Figure 2C). Thus, we find that simulations can capture blebbistatin cryptic pocket opening that is not seen in myosin crystal structures and that MSMs can be used to quantify the probability of blebbistatin pocket opening.

Given that blebbistatin’s cryptic pocket opens in simulation, we wondered if pocket opening was dependent on the nucleotide present in the myosin active site. Biochemical experiments have shown that blebbistatin binds to rabbit fast skeletal muscle myosin with ~10-fold greater affinity when myosin is bound to ADP and phosphate instead of ATP (Kovács et al., 2004). Hence, we hypothesized that blebbistatin pocket opening would be more likely in simulations of myosin bound to ADP*Pi than it would be in simulation of myosin bound to ATP.

To test this hypothesis, we ran long simulations of human fast skeletal muscle myosin IIA (MYH2) from a homology model of a post-rigor crystal structure with ATP in its active site (PDB ID: 6FSA) (Robert-Paganin et al., 2018) and assessed opening with LIGSITE as described above. We then quantified the probability of blebbistatin pocket opening in a MSM of the blebbistatin pocket in the ATP-bound state.

We find that while blebbistatin pocket opening occurs in both nucleotide states, it is substantially more probable in simulations of the ADP*Pi state than in the ATP state (Figure 3A). In the ADP*Pi state, the equilibrium probability of blebbistatin pocket opening is 0.31. In the ATP state, it is only 0.08 (Figure 3B). This finding is consistent with the experimental results showing that blebbistatin is more likely to bind to a myosin bound to ADP and phosphate, trapping it in this state of the mechanochemical cycle (Ramamurthy et al., 2004). Furthermore, this dependence between the nucleotide state in the active site and blebbistatin pocket opening indicates that simulations are capturing the allosteric coupling between these two parts of the myosin molecule.

We reasoned that an important determinant of how potently blebbistatin inhibits a myosin isoform is how likely the blebbistatin pocket opens. If pocket opening is more likely in one isoform, then stabilizing the open state should be easier than in an isoform where pocket opening is less probable. Thus, we hypothesized that the blebbistatin pocket would be more likely to open in myosin isoforms more potently inhibited by blebbistatin (i.e. those with lower IC50s).

We first assessed whether pocket opening probabilities could distinguish blebbistatin-sensitive isoforms (IC50 <100 μM) from blebbistatin-insensitive isoforms (IC50 beyond detectable limit). To compare sensitive and insensitive isoforms, we calculated pocket volumes at the blebbistatin binding site in an existing simulation dataset of blebbistatin-free and nucleotide-free myosin motor equilibrium fluctuations (see Materials and methods) (Porter et al., 2020). While blebbistatin binds with reduced affinity to nucleotide-free myosins like the ones in these simulations (Kovács et al., 2004), our previous work demonstrated that simulations can capture pocket opening in excited states even in the absence of relevant binding partners. We utilized the LIGSITE pocket detection algorithm to assign a blebbistatin pocket volume to each state in the myosin motor MSMs following the same procedure as described above. We then defined compatible states as those conformations where the blebbistatin pocket volume reached or exceeded its volume in a blebbistatin-bound crystal structure of D.d. myosin-II (PDB: 1YV3) (Allingham et al., 2005).

We find that the probability of adopting a pocket conformation compatible with blebbistatin binding is higher among myosin-II isoforms compared to other isoforms. We observe large differences in the blebbistatin pocket volume distributions between MSMs of myosin-IIs and non-myosin-IIs (Figure 4). In simulations of unconventional myosin-V and myosin-Ib, the blebbistatin pocket stays entirely closed despite almost 300 microseconds of aggregate simulation time per isoform. In contrast, all myosin-IIs sample conformations with pocket volumes that exceed the volume of a blebbistatin-bound crystal structure. Interestingly, among myosin-IIs the probability of pocket opening (smooth muscle myosin <nonmuscle myosin IIb < β-cardiac myosin) correctly predicts the rank order of IC50 values (Figure 4—figure supplement 1).

Thus, blebbistatin pocket opening differs between divergent myosin isoforms when there are large sequence differences at the binding site. While myosin-IIs contain small sidechains at the A466 position (skeletal muscle myosin numbering), other myosin families contain aromatic sidechains which point into the pocket, reducing the pocket volume available for blebbistatin binding (Figure 1B and D). Moreover, while myosin-II isoforms have a conserved pocket that opens during simulations of nucleotide-free motor domains, the probability of opening appears to correlate with blebbistatin’s potency.

To relate the probability of pocket opening more precisely to blebbistatin potency, we ran simulations of several sensitive myosin-II isoforms (β-cardiac myosin, nonmuscle myosin IIA, and smooth muscle myosin) that exhibit a broad range of blebbistatin IC50 values (Figure 1B). Since blebbistatin preferentially inhibits myosin when the motor domain is bound to ADP and phosphate (Kovács et al., 2004), we ran these MD simulations with ADP*Pi in the active site. Each of the myosin-II isoforms was launched from a closed, blebbistatin-free starting conformation (Figure 5A). We observed pocket opening in all simulations of myosin-II isoforms, but the likelihood of opening was substantially enhanced in simulations of fast skeletal muscle myosin IIA and β-cardiac myosin. Indeed, in simulations of both skeletal muscle myosin IIA and β-cardiac myosin, a rolling average (window of 10 ns) of pocket volumes exceeds the holo volume for over 500 ns while in simulations of nonmuscle myosin IIA and smooth muscle myosin the pocket only opens transiently a handful of times (Figure 5, Figure 5—figure supplement 2).

We find that the probability of pocket opening is larger for myosin-II isoforms more potently inhibited by blebbistatin. To quantify probabilities of pocket opening, we constructed a MSM of the conformations seen in the blebbistatin pocket for each isoform separately. We then computed the blebbistatin pocket volume for all structures visited by the simulations, assigned a probability to each structure based on the MSM, and found the overall probability of reaching a pocket volume matching or exceeding that of a holo crystal structure (see Materials and methods). Among myosin-IIs, the probability of pocket opening is substantially higher for fast skeletal myosin IIA and β-cardiac myosin than it is for nonmuscle myosin IIA and smooth muscle myosin (Figure 5E). Smooth muscle myosin has the lowest probability of pocket opening (~0.0005), so its free energy difference between open and closed states is the largest. Similarly, nonmuscle myosin IIA has a slightly larger but still low probability of pocket opening (~0.001), consistent with its intermediate IC50. Conversely, both fast skeletal myosin IIA (p_open = 0.31) and β-cardiac myosin (p_open = 0.46) have large probabilities of adopting open states in their MSMs. Given that β-cardiac myosin has a higher opening probability but is less sensitive to blebbistatin, we wondered if volume is a useful, yet incomplete descriptor, for assessing differences in blebbistatin affinity. To test this hypothesis, we turned to molecular docking to see if we could quantitatively predict the binding affinity for blebbistatin between these myosin-II isoforms by using ensembles of structures from our simulations.

We reasoned that molecular docking could improve our ability to predict blebbistatin’s potency by considering the chemical environment of the pocket rather than just the volume available to it. Some of the states we had labeled as closed based on pocket volume, especially those with volumes slightly less than the holo crystal structure, might be able to accommodate blebbistatin in alternate poses. In contrast, some open states may be less compatible with binding than others. We have previously shown that docking compounds to a diverse set of conformations from a Markov State Model improves agreement with experiment (Hart et al., 2016). In this work, we wished to dock to open and closed structures from the ensemble to generate a robust estimate of the free energy of blebbistatin binding.

To determine whether crystal structures were sufficient to explain differences in blebbistatin potency, we first docked blebbistatin to single open and closed structures found in the PDB. When we docked blebbistatin to the closed experimental structures from which simulations were launched, we found that it occupied an adjacent cavity but that these poses received very poor docking scores for all myosin-II isoforms (Figure 6—figure supplement 1). There were no subtle structural differences between experimental structures or adjacent pockets that might explain variation in blebbistatin potency. We also docked blebbistatin to homology models of the 4 different myosin-IIs considered above in the holo crystal structure. This allowed us to interrogate if a single binding-competent structure with the appropriate pocket residues could explain differences in blebbistatin affinity. We found virtually no differences in predicted binding affinities between myosin-IIs using docking to these static structures (Figure 6—figure supplement 1).

Next, we docked blebbistatin to the ensemble of structures represented in our MSMs. Specifically, we used AutoDock Vina to dock blebbistatin against representative structures from each state of our MSM within a box centered on the blebbistatin binding site (see Materials and methods). After completing docking, we investigated both the highest scoring poses and those poses with the lowest blebbistatin RMSD from holo. Encouragingly, the highest scoring pose for skeletal muscle myosin and β-cardiac myosin is very similar to blebbistatin’s pose in the previously determined experimental holo crystal structure (RMSD <3 Å for the ligand heavy atoms, Figure 6—figure supplement 3). All four myosin-II isoforms had docked poses with ligand heavy atom RMSD <3 Å from the holo pose, but the conformational ensembles of skeletal muscle and β-cardiac myosin have a substantially higher probability of adopting conformations where blebbistatin docks in holo-like poses (Figure 6—figure supplement 2).

We find that computationally predicted blebbistatin binding free energies based on state populations from our Markov State Models closely match experimental values. To calculate a blebbistatin binding free energy for each isoform, we assigned a probability to each structure from docking based on the overall probability of that structure’s MSM state and the number of other structures that were mapped to that MSM state (see Materials and methods). Finally, we aggregated the docking results by finding a weighted average of binding constants and converting this value to a free energy of binding. To assess the accuracy of these predictions, we pooled IC50 measurements from all available experiments and converted these measurements to binding free energies under the assumption that IC50 was essentially equivalent to K_i (see Materials and methods for detailed rationale). When we compared the predicted binding affinity of blebbistatin from docking to experimental averages, we find that these parameters are well correlated (Figure 6A, R²=0.82). Moreover, we note that the absolute value of our predictions of binding free energy are in good agreement with binding affinities estimated from experiment (root mean square error of 0.7 kcal/mol). Thus, our results suggest that docking to the ensemble of structures with MSM weighting provides an accurate way to rapidly assess the relative binding affinity of blebbistatin across myosin isoforms.

To test our ability to make blind predictions with this approach, we computationally and experimentally interrogated blebbistatin inhibition of a sarcomeric myosin-II isoform called Myh7b whose sensitivity to blebbistatin had not been determined. Myh7b’s blebbistatin binding site is identical to β-cardiac myosin’s. However, there are numerous sequence differences immediately surrounding blebbistatin’s binding site (7 positions that differ between Myh7b and β-cardiac myosin within 1 nm of blebbistatin’s binding site). In simulations of a homology model of its motor domain, Myh7b had substantial pocket opening (Figure 6—figure supplement 5). Moreover, when we docked to representative structures from the Myh7b simulations and aggregated predicted binding affinities using its MSM, we predict that the binding affinity of blebbistatin for Myh7b is –8.8 kcal/mol. Thus, we hypothesized that Myh7b would be highly sensitive to blebbistatin inhibition and that its IC50 would be more similar to the IC50 of fast skeletal and β-cardiac myosin than that of smooth muscle myosin.

We find that blebbistatin potently inhibits the actin-activated ATPase activity of Myh7b (Figure 6B, IC50: 0.36 μM). We used recombinant human Myh7b and β-cardiac myosin S1 constructs expressed in C₂C₁₂ cells in our experiments. We measured NADH-linked ATPase rates at increasing concentrations of blebbistatin (0.3125 μM to 20 μM) and fit a hyperbolic Hill equation to the data to determine the IC50. As a control, we measured the IC50 value for β-cardiac myosin S1. We obtain an IC50 of 1.12±0.29 μM (error indicates standard deviation between replicates), a value which closely matches with previously published IC50s (Figure 6—figure supplement 6). Consistent with our hypothesis and the computational prediction of blebbistatin’s affinity for Myh7b based on MSM-docking (0.67 μM), we obtain an experimentally measured IC50 for Myh7b (0.36±0.08 μM) that is more similar to fast skeletal and β-cardiac myosin’s IC50 than smooth muscle myosin’s IC50. Thus, our experiments provide additional validation for the MSM-docking approach.

We queried the PDB for all experimental structures of myosin motor domains with a sequence identity cutoff of 10% to hs MYH7, resolution ≤ 4.0 Å, and a BLAST E-value less than 0.1. All PDBs satisfying these criteria were downloaded for further analysis. Some of these PDB files contained fragments of motor domains; therefore, the resulting database of PDBs was parsed further by selecting the largest chain in each crystal structure if the sequence was >600 amino acids. Structures containing blebbistatin, or blebbistatin derivatives, were also excluded from the set (PDB: 6Z7U, 6YSY, 3MJX, 3BZ8,3BZ7, 3BZ9, 1YV3, 3MYK, 3MYH). In total, our set included 124 myosin experimental structures and 23 unique myosin sequences (11 of which were from the myosin-II family).

A multiple sequence alignment was performed with 1YV3 as reference. The resulting alignment was used to identify ligand binding site for the blebbistatin binding pocket and pocket volumes were calculated on each of these structures with the LIGSITE pocket detection algorithm (Hendlich et al., 1997) with a minimum rank of 6, probe radius of 0.14 nm, and a minimum cluster size of 3 grid points.

Initial structural models (i.e. starting structures) for each myosin isoform were generated with homology modeling using SWISS-MODEL (Waterhouse et al., 2018). Crystal structures for simulations were selected based on their sequence similarity to the isoform of interest. Higher resolution structures were prioritized. For 5N6A, the converter and N-terminal regions of the protein were replaced with the corresponding converter and N-terminal regions from 5N69 because these regions were poorly resolved in 5N6A. Below, the UniprotID of the respective human myosin isoform are provided as well as the relevant crystal structures used for modeling (Table 1).

Table 2 provides the sequence identity and sequence similarity between the modeled sequence and template structure’s sequence.

We note that the prepowerstroke state structures of myosin-IIs share very high structural similarity (less than 1 Å in C-α RMSD, Supplementary file 1).

GROMACS (Abraham et al., 2015) was used to prepare and to simulate all proteins using the CHARMM36m force fields (Huang et al., 2017). The protein structure was solvated in a dodecahedral box of TIP3P water (Jorgensen et al., 1983) that extended 1 nm beyond the protein in every dimension. Thereafter, sodium and chloride ions were added to the system to maintain charge neutrality and 0.1 M NaCl concentration. Each system was minimized using steepest descent until the maximum force on any atom decreased below 1000 kJ/(mol x nm). The system was then equilibrated with all atoms restrained in place at 300 K maintained by the Bussi-Parinello thermostat (Bussi et al., 2007) and the Parrinello-Rahman barostat (Parrinello and Rahman, 1981).

Production simulations were performed in the CHARMM36m forcefield. Simulations were run in the NPT ensemble at 310 K using the leapfrog integrator, Bussi-Parinello thermostat, and the Parrinello-Rahman barostat. A 12 Å cutoff distance was utilized with a force-based switching function starting at 10 Å. Periodic boundary conditions and the PME method were utilized to calculate the long-range electrostatic interactions with a grid density greater than 1.2 ų. Hydrogen bonds were constrained with the LINCS algorithm (Hess et al., 1997) to enable the use of a constant integration timestep of 2 fs.

Molecular dynamics simulations were initially performed in parallel from single starting structures first on our in-house supercomputing cluster or on Oracle Cloud Infrastructure using a combination of Tesla P100, Quadro RTX 6000, and NVIDIA RTX A5000 nodes. Five starting structures were obtained from RMSD clustering this initial trajectory data based on the pocket backbone and C-β positions. These starting structures were then used for additional simulations on Folding@home (Shirts and Pande, 2000) (750 clones initiated with different velocities for each starting structure).

To construct a Markov State Model (Bowman et al., 2014) of the blebbistatin pocket, we first defined a subset of features that were relevant to blebbistatin pocket opening. We used backbone (phi, psi) and sidechain dihedrals of residues within 5 Å of the blebbistatin molecule as an input set of features describing the blebbistatin pocket.

To perform clustering in a kinetically relevant space, we applied time-structure-independent component analysis (tICA) to these features (Pérez-Hernández et al., 2013). Specifically, we used a tICA lag time of 10 ns and retained the top n tICs for each isoform that accounted for 90% of kinetic variance using commute mapping.

To determine the number of microstates in our Markov State Model, we used a cross-validation scheme where trajectories were partitioned into training and testing sets (McGibbon and Pande, 2015). Clustering into k microstates was performed using only the training set, and the test set trajectories were assigned to these k microstates based on the their Euclidean proximity in tICA space to each microstate’s centroid. Using the test set only, an MSM was fit using maximum likelihood estimation (MLE) (Prinz et al., 2011) and the quality of the MSM was assessed with the rank-10 VAMP-2 score of the transition matrix (Wu and Noé, 2020).

Finally, Markov state models of the blebbistatin pocket were fit for each isoform separately using MLE. Lag times were chosen by the logarithmic convergence of the implied timescales test (Pande et al., 2010). MSM construction was performed using the PyEMMA software package (Scherer et al., 2015). Details for each model can be found in Table 3.

To compare blebbistatin pocket opening in myosin-IIs with opening in other myosin families, we have also used an existing dataset of myosin motor domain MSMs. These MSMs were constructed from AMBER03 force field simulations gathered on folding@home. Clustering was performed using the Euclidean distance between residue sidechain solvent accessible surface area as a distance metric. MSMs were fit for each isoform using row normalization after applying a 1 /n pseudocount. Full simulation, clustering, and MSM construction details can be found in Porter et al., 2020.

We calculated pocket volumes for the blebbistatin pocket using the LIGSITE algorithm (Hendlich et al., 1997). Specifically, we used the LIGSITE implementation in the enspara software package (Porter et al., 2019b) with a minimum rank of 6, probe radius of 0.14 nm, and a minimum cluster size of 3 grid points. We chose a large probe radius to avoid the erroneous detection of pockets that are too small to be relevant for ligand binding. For a grid point to be part of a pocket, LIGSITE requires it be further than the probe radius and van der Waals radius of nearby protein atoms (i.e. its minimum distance to the nearest protein atom must be greater than the sum of the closest atom’s van der Waals radius and the probe radius). As a result, our reported volumes represent the core of a pocket and are smaller than the space available to ligands. After generating pocket grid points for a myosin structure, we filtered those grid points in the blebbistatin pocket if they were within 2.5 Å of an aligned blebbistatin molecule. We employed a local alignment using homologous residues within 5 Å of blebbistatin. Finally, we required that pockets be continuous and selected the largest cluster of grid points defined as having a shortest inter-grid point distance of 1.5 Å.

To generate distributions of pocket volumes, we followed two different procedures. For the previously published dataset (Porter et al., 2020), we calculated volumes for each representative structure in the Markov State Model and weighted by the equilibrium probability of each state in the MSM. This was done because (a) the size of the dataset prohibited calculating pockets for all simulation frames and (b) these MSMs contained thousands of states and hence were likely to capture a substantial degree of heterogeneity in the blebbistatin pocket. For the new simulations generated for this work, we calculated pocket volumes for every structure we saved from our simulations (save rate of one frame per 20 ps) and then weighted each of these volumes by the probability of a given structure in that isoform’s MSM, specifically the equilibrium probability of the MSM state that the structure is assigned to divided by the number of structures assigned to that MSM state. This second approach allows us to track the temporal evolution of pocket volumes in individual trajectories.

Docking against individual structures was performed using smina (Koes et al., 2013; Trott and Olson, 2010). For each MSM state, we randomly extracted either 3 different structures from that state or πi*2000 different structures if that number exceeded 3/2000 (where πi is the equilibrium probability of the MSM state). PDB files were converted to PDBQT files using AutoDockFR (ADFR suite). The ligand PDBQT files were generated using the same ADFR suite. The ligand charges were assigned using antechamber. To center the docking grid box on the blebbistatin binding pocket, we first selected backbone heavy atoms from residues within 0.5 nm of blebbistatin in its holo structure (PDB: 1YV3) and aligned this selection using an iterative procedure described in Grossfield et al., 2007. We then used the centroid of the average structure from the final alignment as the center of our box— (0,0,0) in that coordinate system. All alignment and frame selection was done using LOOS (Romo et al., 2014; Romo and Grossfield, 2009) For the docking search, we set the exhaustiveness to 8 and used the smina scoring function. Jug (Coelho, 2017) was used to parallelize docking while gnu parallel (USENIX, 2022) was used to parallelize receptor parameterization.

The overall free energy of binding from docking to an MSM can be written as:ΔGtotal=−RTln(∑πiKeqi)

where πi is the equilibrium probability of the MSM state and K_eqi is the equilibrium association constant for a MSM microstate calculated from the docking score for that MSM state. Since the scoring function returns docking scores in kcal/mol, it is straightforward to convert to K_eqi.

We note that this formula directly considers the configurational entropy of the protein by taking a weighted average of the micro-association constants over all the states in the apo MSM. Closed states will receive unfavorable (i.e. more positive) docking scores reflecting in small association constants while open states will receive favorable (i.e. more negative) docking scores resulting in large association constants. Similarly, high probability states will contribute more to the weighted sum of micro-association constants, which is the macro-association constant of the ligand binding reaction. Hence, a protein that pays a high entropic penalty for ligand binding because it has only a few, low probability open states will have a small macro-association constant and an unfavorable overall MSM-docking score. Conversely, a protein that pays a low entropic penalty for ligand binding because of its many, high probability open conformers will have a large macro-association constant and hence a more favorable overall MSM-docking score.

Because we docked to multiple structures for each MSM state, we found a consensus docking score by using the following equation:ΔGtotal=−kBTln(∑πsfNKeqi)

where f is a structure from simulation, s is the MSM state that structure belongs to, N is the number of structures from that MSM state for which docking was performed, and π is the equilibrium probability of that MSM state.

Recombinant myosin was produced as previously described (Sommese et al., 2013; Deacon et al., 2012; Lee et al., 2023; Resnicow et al., 2010) with minor changes. Adenoviruses encoding human β-MyHC S1 (amino acids 1–842) and human MYH7b S1 (amino acids 1–850) followed by a flexible GSG linker and C-terminal PDZ binding peptide (RGSIDTWV) were used to infect differentiated C₂C₁₂ cells. The source of the cell lines was ATCC. They were not authenticated or tested for mycoplasma because they were exclusively used to produce protein, and their identity does not impact any of the results in the manuscript. C₂C₁₂ cells were harvested 4 days after infection, flash frozen in liquid nitrogen, and stored at –80 °C. Cell pellets were thawed and lysed using dounce homogenization in 50 mM Tris pH 8.0, 200 mM NaCl, 4 mM MgCl₂, 0.5% Tween-20, 5 mM DTT, 1 mM ATP, 0.2 mM PMSF, and 1 X protease inhibitor cocktail (Millipore Sigma/Roche, 11873580001). Lysates were clarified by centrifugation at 39,000 x g for 25 min at 4 °C. The supernatant was filtered through 5 µM and 1.2 µM filters and applied to a column containing SulfoLink resin (ThermoFisher, 20402) coupled to PDZ. The column was washed with 30 mM Tris pH 7.5, 50 mM KCl, 5 mM MgCl₂, 1 mM DTT, and 1 mM ATP and myosin S1 (bound by endogenous C₂C₁₂ light chains) was eluted using a peptide with tighter affinity for PDZ (WQTWV). Proteins were dialyzed against a storage buffer containing 20 mM MOPS pH 7.0, 25 mM KCl, 5 mM MgCl₂ and 10% sucrose, flash frozen in liquid nitrogen, and stored at –80 °C.

Actin was purified from porcine ventricles as previously described (Greenberg et al., 2014; Clippinger et al., 2019). The concentration of actin was determined spectroscopically as previously described (Greenberg et al., 2014; Clippinger et al., 2019).

Actin-activated ATPase rates were measured across a range of blebbistatin concentrations using the NADH-coupled assay in a 96-well plate (De La Cruz and Ostap, 2009) with a 0.1 μM myosin S1 concentration and 10 μM actin concentration. Before the experiment, actin was polymerized by dialysis in ATPase buffer containing 20 mM Imidazole, 10 mM KCl, 2 mM MgCl₂, and 1 mM DTT followed by 1.1 x molar ratio phalloidin stabilization. Experiments were conducted in ATPase buffer with the addition of the NADH-coupled enzymes (0.5 mM phospho(enol)pyruvate [Sigma, P0564], 0.47 mM NADH [Sigma, N7410], 100 U/mL pyruvate kinase [Sigma, P9136], and 20 U/mL lactate dehydrogenase [Sigma, L1254]). Blebbistatin (Selleckchem, S7099) was dissolved in DMSO. The blebbistatin concentration was varied using serial dilutions. Before gathering data, 2 mM ATP was added to each well. Experiments were performed at 25 ° C using a BioTek Syngergy H1 microplate reader. Absorbance was monitored at 340 nm and it decreased linearly with time. Rates for each well were determined based on the linear fitting of the absorbance as a function of time. A control well containing actin, no myosin, and 20 μM blebbistatin was used as a baseline. Finally, a Hill equation was fit to the data to determine an IC50 for each experiment. Each data point consists of five technical replicates.

Bootstrapping was performed to generate error bars for each of the reported simulation measurements. Specifically, we performed 250 trials where we drew N trajectories with replacement from each set of N trajectories, constructed a MSM with the drawn trajectories, and recomputed the observable of interest (e.g. MSM-weighted docking score).

The mechanism by which blebbistatin inhibits skeletal muscle myosin’s actin-activated ATPase activity has been characterized in detail (Kovács et al., 2004). These experiments indicate that blebbistatin binds with the highest affinity to myosin when it is in its ADP*Pi state capable of weakly binding to actin but also has non-negligible affinity for myosin in its ATP-bound state. Thus, blebbistatin can be considered a mixed inhibitor of actin-activated ATPase activity. The K_m for actin activation of S1 ATPase was 24 μM while the actin concentration used to determine blebbistatin’s IC50 for skeletal muscle myosin was 43 μM (Kovács et al., 2004). Given that blebbistatin’s affinity for the ADP*Pi state is 10 x that of its affinity for the ATP-bound state and that KmS=0.56, we conclude that IC50 is essentially equal to K_i for skeletal muscle myosin (Cheng and Prusoff, 1973). We assume that the same mechanism of inhibition applies to the other myosin-II isoforms and that IC50s for other myosin-II isoforms can be directly converted to K_i’s. Thus, we pooled reported IC50s with K_i’s across experiments and converted to binding free energies under these assumptions.

The code used for the generation, analysis, and visualization of the molecular dynamics data is available via a Github repository at https://github.com/bowman-lab/blebbistatin-specificity; Meller, 2022. The dataset corresponding to this repository is at https://osf.io/cv6d2/. To generate Figure 4, the existing dataset found here (https://osf.io/54g7p/) was used.