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Target the more druggable protein states in a highly dynamic protein-protein interaction system Zuojun Guo, Atli Thorarensen, Jianwei Che, and Li Xing J. Chem. Inf. Model., Just Accepted Manuscript • DOI: 10.1021/acs.jcim.5b00503 • Publication Date (Web): 09 Dec 2015 Downloaded from http://pubs.acs.org on December 15, 2015

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Journal of Chemical Information and Modeling

Target the More Druggable Protein States in a Highly Dynamic Protein-Protein Interaction System Zuojun Guo1, Atli Thorarensen1, Jianwei Che2, Li Xing1*

1

Worldwide Medicinal Chemistry, Pfizer Inc, Cambridge, Massachusetts, USA.

2

Department of Chemistry and Biochemistry, University of California, San Diego, California,

USA

∗

To whom correspondence should be addressed. Tel: (617) 674-7462. Fax: (617) 665-5575. Email:

[email protected]

ACS Paragon Plus Environment


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Abstract The proteins of Bcl-2 family play key roles in the regulation of programmed cell death by controlling the outer mitochondrial membrane integrity and the initiation of apoptosis process. We performed extensive MD simulations to investigate the conformational flexibility of the BclxL protein in both the apo and holo (with Bad peptide and ABT-737) states. The accelerated molecular dynamics (aMD) method implemented in Amber 14 was used to produce broader conformational sampling of 200 ns simulations. The pocket mining method based on the variational implicit solvent model (VISM) tracks the dynamic evolution of the ligand binding site with a druggability score characterizing the maximal affinity achievable by a drug-like molecule. Major movements were observed around the α3-helical domain and the loop region connecting the α1 and α2 helices, reshaping the ligand interaction in the BH3 binding groove. Starting with the apo crystal structure, which is recognized as “closed” and undruggable, the BH3 groove transitioned between the “open” and “closed” states during equilibrium simulation. Further analysis revealed a small percentage of the trajectory frames (~10%) of moderate degree of druggability that mimic the ligand-bound states. The ability to attain and detect by computer simulation the most suitable conformational states for ligand binding, in advance to compound synthesis and solving structure of ligand-protein complex, is of immense value to the application and success of structure-based drug design. Keywords: PPI: Protein-Protein Interaction; aMD: accelerated molecular dynamics; VISM: variational

implicit solvent model; Druggability; SASA: solvent accessible surface area; RMSD: root mean squared deviation; RMSF: root mean squared fluctuation; PCA: principal component analysis.


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1. Introduction The proteins of Bcl-2 family play key roles in the regulation of programmed cell death by controlling the outer mitochondrial membrane integrity and initiating the apoptosis process.1 Up to date, more than 25 Bcl-2 family members have been discovered.2, 3 Coupled with their tissuespecific patterns of distribution, each family member functions as either anti-apoptotic (Bcl-2, Bcl-xL, Bcl-w etc.) or pro-apoptotic (Bak, Bax, Bad, etc.) mediators. The balance between the pro-survival and pro-apoptotic proteins through extensive protein-protein interactions decides the life or death of a cell. Essentially, the BCL-2 proteins have functions in normal development, tissue remodeling, immune response, and overall maintenance for all multicellular organisms.2, 4 Disruption of the apoptosis process is implicated in a number of disease states, including Alzheimers disease, autoimmune disorders, and cancer. Therefore proteins of Bcl-2 family, in particular Bcl-xL, have been attractive targets for drug discovery research.5-7 Despite the distinct functional roles of the individual family members, the Bcl-2 proteins share remarkably similar three-dimensional folds in solution and in crystal structures.8 The tertiary structures of Bcl-2 proteins consisted of two central hydrophobic α-helices which are enclosed by additional six or seven amphipathic α-helices of varying lengths. Upon receipt of apoptotic stimuli, the Bcl-2 family proteins associate with each other to form complex networks of homo- and heterodimers as part of a cellular macromolecular complex. The relative ratios of the anti- and pro-apoptotic Bcl-2 family proteins in the oligomeric super-structure direct the final sensitivity or resistance of cells to various apoptotic stimuli.1, 2, 9-12 In particular, the oligomeric complexes of the pro-apoptotic proteins, i.e., Bax and Bak, form potential pore-like structures in the outer membrane of mitochondria. This allows the release of apoptogenic factors that trigger the activation of caspases for cellular demolition. The oligomerization of anti-apoptotic proteins,



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i.e., Bcl-xL and Bcl-2, then results in the formation of ion-channels in membranes.13, 14 Despite the extensive efforts in biophysics and biochemistry, a detailed understanding of the structural transition and the mechanisms that regulate the activation of the Bcl-2 family proteins remains elusive. Besides the experimental endeavors, a substantial amount of computer simulations have been devoted to investigating the conformational dynamics and structural transitions of Bcl-2 family proteins, especially with a focus on Bcl-xL.15-20 Using molecular dynamics (MD), these studies aimed to obtain structural insights into the functional mechanisms, and to assist drug discovery research targeting this important biological pathway. Overall, the protein systems studied by simulations can be grouped into three categories: (a) the apo Bcl-xL protein in water or in membrane.18 (b) the BH3 peptides (Bak, Bad, Bim, etc) bound with Bcl-xL,20 (c) the drug-Like small molecular inhibitors (4FC, TN1, N3B, ABT-737, etc.) bound with the anti-apoptotic Bcl-2 proteins.17, 19 These computational studies elucidated the structural dynamics of Bcl-xL protein and emphasized the importance of accounting for protein flexibility in inhibitor design. Specifically, Novak et al observed that the improvement of binding affinity directly correlated with the reduction of protein local flexibility and the enhanced stability of specific binding regions.17 Lama et al also pointed out that the stabilization of the BH3 domain in the binding partner enhanced the binding affinity.20 Analysis of the unbound crystallographic structures of Bcl-xL revealed that the apo structure had a very small BH3 binding groove that could not accommodate the Bak peptide.21 The extremely low druggability predicted from the crystal structure of the apo protein is in contrast to the fact that multiple chemical series of Bcl-xL inhibitors had been reported.5, 22, 23 Applying a short simulation of 100 ps, Hajduk et. al. probed the impact of thermal motion on the druggability of Bcl-xL. Obtained by a quantitative fitting


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model the druggability of the binding pocket was reported to fluctuate over a broad range in the short period of molecular simulation.21 Due to the technical limitations in computer software and hardware, the longest molecular dynamics study on Bcl-xL spanned the time frame of nano-seconds.18 The conformational spaces sampled by these relatively short conventional MD simulations could be restricted to local energy wells in proximity to the crystal structures. Movements were typically limited to protein side chain and/or flexible loop regions. In the context of conformational heterogeneity, it is desirable to study protein dynamics in the range of micro- to milli-second time scale since it is likely in this regime that the conformers of biological importance are found. The recent development of new technologies in computer simulation affords feasible approaches to tackle the structural versatility.24 Amongst them the accelerated molecular dynamics (aMD) method extended the MD simulation to a much longer time scale. The enhanced sampling capability of aMD was accomplished by elevating the energy minima in the potential energy landscape to reduce the transitional barriers that separated different states.25 Combined with the implementation of the graphics processing unit (GPU), aMD allowed sampling of conformational spaces that were not readily accessible in a conventional molecular dynamics (cMD) scenario. In the current study, we performed extensive aMD simulations to investigate the conformational flexibility of the prototypical protein-protein interaction system Bcl-xL, over time periods that are equivalent to several-thousand folds of the previous cMD investigations. Using the aMD implementation in Amber 14, the conformational variations between the apo and the holo states resolved by crystallographic experiments are investigated. Combined with the recently published variational implicit solvent model (VISM), a physically based method for



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protein binding site identification and characterization,26 we probed and monitored the dynamic changes in the topological and physicochemical properties of the BH3 binding pocket in a systematic manner. Associated with the highly flexible nature of the Bcl-xL protein, the druggability of the ligand binding pocket is shown to be an inherently fluid variable. It is also demonstrated that by simulating the apo protein system, the productive conformations in resemblance to the ligand-bound states can be attained. The ability to identify the more druggable conformations and exploit them effectively in compound design is a key element in driving the success of drug discovery.

2. Materials and Methodologies 2.1 Starting structure and their preparations for MD simulations. The initial structures for the apo and ligand-bound Bcl-xL proteins were extracted from the Protein Data Bank: the apo Bcl-xL crystal structure (PDB 1R2D),27 the co-crystal structure of Bcl-xL with small molecule ABT-737 (PDB 2YXJ)28 and the NMR structure of Bcl-xL bound with the Bad peptide (PDB 1G5J).29 All protein constructs lack the putative carboxy-terminal transmembrane region. The residues 49-88 are deleted, as in all the previously reported protein constructs. It is part of the flexible loop between helices α1 and α2 that is not necessary for antiapoptotic activity of Bcl-xL.29-31 As a result a shorter sequence is expressed for the connecting loop. Furthermore, in both of the X-ray structures (apo- and ABT-737/Bcl-xL) the loop residues of 28-48 were disordered. In order to afford consistent protein systems for aMD simulations, the disordered loop region containing residues 28-48 was built for both the apo and the ABT-737 bound structures by homology modeling, using the Bad/Bcl-xL complex as the template.


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Hydrogen atoms were added to the proteins as necessary. The protonation states were assigned by propKa at pH=7.0. The protein preparation module within Maestro molecular modeling suite was used for an iterative energy minimization process.32 During the relaxation procedures, the hydrogen atoms were minimized while keeping the protein structure fixed.

2.2 Accelerated Molecular Dynamics (MD) Simulations of Bcl-xL. The model systems were built using xleap in Amber14. Each protein complex was placed in a cubic simulation box, with size adjusted to maintain a minimum distance of 10 Å to the cell boundary. The systems were then soaked with a pre-equilibrated box of water. Amber force field parameters ff14SB were used for protein and TIP4PEW for water molecules. The General Amber Force Field (GAFF) was used for the ligand ABT-737. Sodium counterions were added to neutralize the systems. All histidine residues in the simulations were kept in the state that the delta nitrogen was protonated (HID in Amber) which was predicted by the WHATIF webserver by Atanu et. al.18, 33 In these simulations, bonds containing hydrogen atoms were restrained with the SHAKE algorithm.34 Weak coupling to an external temperature and pressure bath was used to control the system temperature and pressure.35 The particle-mesh Ewald method (PME) with a cutoff of 10.0 Å were used for the electrostatic interaction calculation.36 The solvated systems were initially relaxed by a series of minimizations and equilibration steps to remove any bad contacts. The relaxation protocol is set as follows: 1) 2,000 steps of gradient descent minimization with solute heavy atoms constrained at 20 kcal/mol•A2; 2) 2,000 steps of minimization without constraints; 3) 200ps of isothermal-isobaric (NPT) conventional MD simulation with solute heavy atom constrained by a force constant of 2 kcal/mol•A2 at 300



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K; 4) With the constraints removed, the system was heated up from 0 K to room temperature at 50 K increments by a series of isothermal-isovolumetric (NVT) MD simulations; 5) Finally, at the target temperature of 300 K, the system was further equilibrated at constant NPT for 2 ns for water density relaxation. After these minimization and equilibration processes, all of the accelerated molecular dynamic (aMD) production simulations were conducted using Amber14 suite implemented on the Nvidia Tesla K20m Graphics Processing Unit (GPU). The production runs were conducted at NVT ensemble for 200 ns with 2 fs time step, and the full periodic boundary conditions were applied throughout. Simulation configurations were saved at 1 pico-second intervals.

2.3 Trajectory analysis. The simulation frames are sampled at an interval of 200 ps. The secondary structure analysis using the frames of the aMD simulations was performed by STRIDE for both the bound and unbound states.37 Principal component analysis (PCA) and the residue-residue cross correlations were performed for the three Bcl-xL simulation using Bio3D.38 The PCA was conducted using Cα atoms of residues 90-200, excluding the N-terminal α1 and the long flexible loop between α1 and α2 (residues 28-48). The structural uncertainties of this region are high, consistent with the low experimental resolution of the coordinates. All residues constituting the BH3 binding groove are included. The recently developed binding pocket identification algorithm based on the level-set variational implicit-solvent model with Coulomb-field approximation (VISM-CFA)26 is used to characterize the binding site, including a druggability assessment for the individual protein


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conformations along the simulation time. The VISM-CFA method was originally designed as an alternative implicit solvent model for molecular solvation.39-41 Instead of predefining the solutesolvent interface as done in many other implicit solvent model,42 the molecular solvation equilibrium was described by the minimum of the solvation free energy functional with respect to the solute-solvent interface. Therefore, the method provides a physically more meaningful equilibrium solute-solvent interface as well as more accurate solvation free energy estimates.41, 43 A retrospective study of the binding pocket identification with large number of protein-ligand crystallographic structures has shown good sensitivity and specificity.26 Recently, it has also been publicly released for the analysis of biomolecular solvation.26, 44 In the current investigation we extend this method to analyze the individual MD simulation frames, with a goal to uncover the dynamic nature of the BH3 binding site and to explore transient pocket for novel “druggable” opportunities. Individual protein simulation frames were chosen as input structures. The partial charges and 12-6 Lennard-Jones (LJ) potential parameters of solute atoms are obtained from the Amber force field: TIP3P water LJ parameter ε ww = 0.152 kcal/mol; and the solvent molecular diameter σ ww = 3.15 Å. The macroscopic planar surface tension was set at = 0.076 kcal/mol⋅Å2 at 300 K which was obtained from the TIP3P water simulation. The Tolman coefficient was chosen to be 1 Å for the convex and concave atomic level surface tension correction. These parameters are consistent with the previous studies.26, 41, 43

3. Results and Discussions 3.1 Structures of Bcl-xL in apo and holo states.



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(a)

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(b)

(c) Figure 1. X-ray structures of human Bcl-xL. (a) Apo Bcl-xL showing major α helices in different colors (PDB 1R2D). (b) Three Bcl-xL structures superimposed, with α3- α4 depicted in different colors: apo Bcl-xL in orange, ABT-737/Bcl-xL in magenta (PDB 2YXJ), and Bad peptide/Bcl-xL structure in cyan (PDB 1G5J). (c) Domain annotation of Bcl-xL. The anti-apoptotic Bcl-xL protein consists of five major α-helical structures (α1, α2, α4, α5, α6) and four minor α-helical structures (α3, α7, α8, α9) intersected by connecting loops in junction (Figure 1a). The hydrophobic groove, formed mainly by the α2, α3, α4, and α5 helices, serves as the binding site for the BH3 domains of the Bcl-2 family proteins. The BH3 binding


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groove also affords the binding site for small molecule ligands. Depending on its binding partner, the Bcl-xL binding pocket adopts different shapes to accommodate the specific ligands through extraordinary structural flexibility. Figure 1b shows the alignment of three structures of Bcl-xL. As highlighted by different colors, the major structural differences between the apo and the ligand-bound states are located in the α3 and α4 regions. Both helices are displaced upon ligand binding with an outward shift from the apo state to afford a larger binding site. Furthermore, distinctions are observed between the compound bound (ABT-737) and the peptide (Bad) bound structures. While the short helix α3 is partially preserved upon binding of ABT-737, it is forced to unwind by the occupation of the Bad peptide, adopting an extended loop conformation. 3.2 Druggability assessments of the apo and holo structures.

a) Bcl-xL apo

b) Bcl-xL with Bad peptide

c) Bcl-xL with ABT-737

Figure 2. Binding pocket characterization of apo Bcl-xL structure (a), binary complexes with Bad peptide (b) and small molecule inhibitor ABT-737 (c). Blue surfaces delineate binding pockets identified by VISM. In the unbound state, the BH3 binding region is shallow and featureless, and is characterized as undruggable. In the ligand-bound states, the BH3 binding



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groove becomes deeper to complement the Bad peptide, and even more druggable upon complex formation with ABT-737. The highly flexible nature of the Bcl-xL binding pocket is shown in Figure 2 by surface models comparing the apo and the bound forms induced by different ligands. In the apo Bcl-xL, the hydrophobic groove is restricted and shallow, to the extent that it is not identified as a druggable pocket by the VISM algorithm. In contrast, in response to the binding of an amphipathic helical peptide (Bad) or a drug like small molecule (ABT-737), the binding groove becomes much deeper and longer to facilitate the complex formations (Figure 2b and c). This is afforded by significant conformational changes including shifts in the helical positions of a2 and a4 and partial unfolding of α3, which collectively opens up the pocket that is otherwise blocked in the apo Bcl-xL structure. Two hydrophobic “hotspots” are identified, which are respectively occupied by the two aromatic side chains of Tyr308 and Phe319 of the Bad peptide. In analogy, these binding anchors are chemically replicated by the synthetic compound ABT-737, specifically by the para-chlorophenyl group and the thioether linked phenyl moiety. Despite their completely unrelated structural type, it is remarkable how closely in 3D space the key hydrophobic elements are preserved for the “hotspot” interactions. Furthermore, the binding interface displays distinct topological features as well as physical properties upon binding of different ligands. As summarized in Table 1, the 25-mer Bad peptide induces an elongated pocket per principal moment of inertia analysis. Overall, the binding pocket for ABT-737 is deeper and more hydrophobic than that of the Bad peptide, yielding a significantly higher druggability score (optimal binding affinity) than the Bad peptide (-13.6 vs. -8.7 kcal/mol respectively). In conjunction with the more druggable pocket, the binding affinity for ABT-737


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(0.4 nM)45 is significantly higher than the Bad peptide (6 nM)46 , demonstrating an increase of 15 fold. Based on the comparative analysis of the Bcl-xL crystal structures, it is concluded that the PPI interface is highly adaptable. In response to the distinct perturbations exerted by different binding partners, the BH3 binding pocket changes its 3D configuration to afford specific complementarity. It is therefore suggested that it is inadequate to base our drug design hypothesis solely on the available crystallographic structures, as it could limit our opportunity to discover novel chemical spaces that is accommodated by protein flexibility. Table 1. BH3 binding pocket topological and physicochemical parameters in apo and bound states calculated by VISM.

Shape Depth PDB

SASA1

Volume

Ligand

Hydrophobic

Optimal Binding

Fraction (%)

Affinity (kcal/mol)

(Principal moment (Å)

(Å2)

(Å3) of inertia)

1r2d

NA

NA

NA

NA

NA

NA

1g5j

Bad

4.4

69.4

597.0

(0.16, 0.94, 1)

70.4

-8.7

2yxj

ABT-737

5.8

125.4

457.7

(0.22, 0.91, 1)

86.3

-13.6

1

SASA: Solvent Accessible Surface Area

3.3 Overall Bcl-xL structural flexibility by aMD simulations.



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(a)

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(b)

Figure 3. (a) Root mean squared deviation (RMSD) of Bcl-xL backbone Cα atoms as a function of simulation time of apo Bcl-xL (blue), Bcl-xL/Bad peptide (red) and Bcl-xL/ABT-737 (black) binary complexes. (b) Root mean squared fluctuation (RMSF) of the Cα atoms. The stability of the Bcl-xL protein in the apo form as well as different binary states in complex with either Bad peptide or ABT-737 were analyzed by root mean squared deviation (RMSD) during the course of aMD simulations (Figure 3a). The aMD trajectories were aligned using the Cα atoms of the major α-helical domains including α1, α2, α4, α5, and α6 onto the starting conformations. By avoiding the highly flexible α3 region from the alignment the presented RMSD values largely reflect the movement of the long loop connecting α1 and α2 helical domains and the dynamics of the α3 helix. The conformation of the α3 region has direct impact in shaping the ligand binding site, therefore regulating different types of binding interactions. The RMSDs of the three simulations reach a stable equilibrium at 20 ns. Comparing the different systems, the lower RMSDs of the Bcl-xL bound states manifest a ligand stabilization effect on the flexible region of the active site. Furthermore, the Bcl-xL protein in complex with the Bad peptide displays lower RMSD for most of the 200 ns time course, suggesting an even


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stronger stabilization of Bcl-xL by the peptide resulting from the extensive surface contacts between Bad and Bcl-xL. It was noted that the average RMSDs of 4 Å are relatively large compared to the conventional MD simulations, which were previously reported to be within 1-3 Å range.17 Possibly the classical simulations only accessed local energy basin proximal to the crystal structures, especially considering the short simulation time of 10 ns.17 The larger deviation can be attributed to the enhanced conformational sampling by the accelerated MD algorithm and the specific alignment process we adopted to emphasize the binding site fluctuations. The root mean squared fluctuations (RMSF) of the Cα atoms (indexed from N-terminal to C-terminal) of the three simulation systems over the production runs were shown in Figure 3b. The more flexible regions are highlighted by higher RMSF values. Except for the N- and Cterminals, the connecting loop between α1 and α2 helices (residues 28-48) is the most flexible region. Built by homology modeling, the loop residues fluctuate at an average RMSF of around 5 Å. The large RMSFs were consistent with the disordered nature of this segment in the apo- and the ABT-737/Bcl-xL crystal structures. In addition, the residues around the binding pocket (95150) also show significant deviations at an average RMSF of 4 Å. This deviation corroborates the dynamic nature of the BH3 binding site. The dynamic fluctuation of the apo Bcl-xL simulation appears the highest, which peaks at residue Tyr105. When Bcl-xL is complexed with a binding partner, e.g. either with Bad peptide or ABT-737, the binding site becomes significantly more stabilized, favoring the conformational states that are complementary to the binding ligand.



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(a)

(b)

(c)

(d)

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Figure 4. Stability of the secondary structures (α-helices in pink, turns in cyan and coils in white colors) for the three simulation systems: (a) apo Bcl-xL (b) Bcl-xL/Bad peptide (c) Bcl-xL/ABT737. The helical propensity of each residue during aMD is plotted for the three simulations (d). The stability of the secondary structures in aqueous solution was analyzed by STRIDE during the course of the aMD simulations for bound and unbound states.37 As shown in Figure 4(a, b, c), the largest secondary structure changes are located between α2 and α4. Consistent with the comparison of experimental structures, the most plastic region of Bcl-xL is highlighted by the partial unwinding and shifting of the α3 helical domain. This is especially prominent in the apo


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Bcl-xL where α3 is frequently superseded by turns and coils. Figure 4d displays the percentage of α-helical structures during the simulations for each residue, which further illustrates the transitions around the α3 region (residues 102-114). When there is no ligand bound, residues 102-114 primarily exist in the unstructured states indicated by a sharp downslide in the fraction of helicity. On the other hand, in the simulations where Bcl-xL is bound with Bad or ABT-737 the same segment folds into α-helical structure with greater than 60% helicity on average. Furthermore, the Bcl-xL/ABT-737 binary complex demonstrates even higher propensity of helical formation, showing an intact helical fold of α3 at almost 100% distribution. Therefore, the ligand-free BH3 binding site is inherently plastic manifested by the high fluctuation of the α3 region that partially defines the pocket. Binding of a high-affinity ligand effectively stabilizes the protein into a preferred selective state and significantly reduces the conformational entropy of the target protein.

(a)



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(b)

(c) Figure 5. Cross-correlation of residue fluctuations from (a) apo Bcl-xL (b) Bad/Bcl-xL and (c) ABT-737/Bcl-xL simulations. The panels on the left show correlation matrix of Cα atoms, and on the right are the corresponding projections of correlation onto the Bcl-xL protein structure. The strong red color represents highly correlated regions, and the deep blue color represents the highly anti-correlated regions suggesting movements in opposite directions.


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To understand the correlated and anti-correlated motions within Bcl-xL we performed a residue cross-correlation analysis (Figure 5). Similar patterns were observed for all three simulation systems. Most of the correlations appear within a single helical domain, suggesting the constrained movements of the entire secondary structure. The long loop region connecting the α1 and α2 helical domains (residues 25-45) as well as the flexible region connecting α2 and α4 helical domains (residue 102-114) showed anti-correlations to most of other residues in BclxL. In particular from the apo Bcl-xL simulation we observed a strong anti-correlation between the α3 flexible region and the α5 helical domain (residues 137-158). By virtue of being anticorrelated, the motions of the two regions substantially modify the BH3 groove, giving rise to the “open” and “closed” conformations of the ligand binding site that regulates the apoptotic function of the Bcl-xL protein. On the other hand, the anti-correlation was much attenuated in the Bcl-xL bound states, indicating that the binding site is locked in the “open” state by the bound ligands. With ligand binding to the BH3 binding groove, the domain correlations become weaker as indicated in Figure 5b and 5c in comparison. Overall, the ligand-bound states of Bcl-xL show less correlated motions, not only for the BH3 binding regions but also those beyond them.



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Figure 6. The principal component analysis (PCA) of Bcl-xL in bound and unbound states. The blue dots represent the conformational population of free Bcl-xL, red dots are the population of Bcl-xL with Bad peptide, black dots for the binary complex with compound ABT-737.

Principal component analysis (PCA) on the trajectory coordinates was performed to identify the collective motions of Bcl-xL in bound and unbound states. With an emphasis on the BH3 binding cleft that regulates the anti-apoptotic function of Bcl-xL, the PCA was conducted for Cα atoms of residues 90-200, excluding the long flexible loop region connecting α1 and α2. As shown in Figure 6 the protein spans a wide range of conformational spaces during the course of the aMD simulations. The ligand-bound simulations form distinct clusters with little and


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subtle overlap between them (Bcl-xL/Bad and ABT-737/Bcl-xL). Apparently, binding of different ligands induce specific reshaping of the protein structure and stabilize Bcl-xL into the more productive conformations for complex formation. Accordingly, the energetic landscapes of BclxL were modified by the Bad and ABT-737 ligands, yielding disparate energy wells for the two bound states. The conformational population of Bcl-xL with ABT-737 shows narrower distribution than the Bad peptide, suggesting tighter complex formation of the small molecule inhibitor. In comparison the apo Bcl-xL simulation showed much broader distribution of conformational states. Two cluster centers are observed indicating distinct conformational preferences. One cluster overlaps with the ligand-bound states, while the other cluster remains distal representing the “closed” form of the BH3 binding groove. The “closed” form resembles that of the apo Bcl-xL crystal structure, in which the BH3 groove is too small and shallow to accommodate ligand and deemed undruggable. The more “open” states start to bridge toward the ligand-bound conformations, and their capability and quality for small molecule binding are characterized by a druggability assessment in the next section. Based on the simulation results the apo Bcl-xL rarely visited the ABT-737 bound conformation, suggesting that binding of ABT737 might proceed largely by induced fit mechanism as opposed to population shift. From the drug discovery perspective it is important to understand the intrinsic degree of protein flexibility and the related thermodynamic populations in an unbiased manner, that is, without the influence of ligand. Different conformational frames will likely have different degrees of capacity of binding ligand, and the ability to identify the most druggable states from the apo protein simulation is of paramount value to structure-based design. 3.4 The dynamics of the BH3 binding pocket from apo simulation.



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(a)

(b)

Figure 7. (a) The volume distribution of the BH3 binding pocket and the representative conformations from the aMD simulation. (b) Histogram of the pocket volume from the 1000 aMD frames. As shown, approximately 25% of the conformational states afford pockets that are large enough (>200 Å3) for small molecule binding.


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In order to understand the ligand binding capacity and the related thermodynamic properties, we performed a dynamic pocket analysis using the simulation frames of the apo BclxL. Every frame of 0.2 ns interval was examined and the protein surface was exhaustively probed for binding pockets using the VISM algorithm.26 In Figure 7a, the pocket volume distributions of the BH3 hydrophobic groove is shown throughout the 200 ns aMD simulation. From previous calibration study a pocket volume of 200 Å3 cutoff is appropriate to discriminate the small cavities on the protein surface that are not suitable for ligand binding.26 Based on the simulation results approximately 75% of the total conformations fall into the “closed” state, which is insufficient to harbor a small molecule ligand. For comparison, in the bound states the pocket volume is approximately 600 Å3 ABT-737, and 450 Å3 with the Bad peptide. Amongst the 25% of the simulation frames that show some degree of druggability, about 2% disclose a pocket volume comparable to or larger than 600 Å3 in size. Such widely open pockets approximate the highly druggable state represented by the potent Bcl-xL inhibitor ABT-737. It is noted that the size of binding cavity is the most important factor that governs its druggability substantiated by a strong correlation between the two parameters (Figure S4). Structure-based design is genuinely enabled when the protein pocket of high binding affinity can be attained without the requirement of discovering the compound a priori.



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(a)

(b) Figure 8. (a) The fluctuation of BH3 binding pocket druggability for 1000 snapshots of the aMD simulation. (b) Fraction of the BH3 binding pocket druggability binned with 5 (kcal/mol) interval.


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The druggability of the binding pocket can be characterized by the optimal binding affinity achievable by a hypothetical ligand which maximizes the occupation of the hydrophobic regions of the pocket.26 In Figure 8, we show the druggability of 1000 snapshots during the 200 ns aMD simulations of the apo Bcl-xL. In general, the druggability scores fluctuate between 0 and -20 kcal/mol. For reference the druggability scores of the Bcl-xL/Bad and Bcl-xL/ABT-737 crystal structures are -8.7 and -13.6 kcal/mol, respectively. According to the recent calibration study using 27 popular drug targets, a druggability score of -5.0 kcal/mol can be applied as a suitable cutoff for developing drug-like molecules.26 In our simulation 10% of the conformational frames meet the minimum criterion of being “druggable”. Furthermore, about 2% of the conformations show favorable druggability scores in analogy to the binding pocket induced by ABT-737, e.g. less than -10 kcal/mol. Upon visual inspection of these high druggability frames closer resemblances to the ABT-737 bound structure were observed. We then selected five representative frames based on conformational clustering and attempted docking of ABT-737. In contrast to the recognition that the apo Bcl-xL crystal structure is incompatible with ligand binding, reasonable poses were obtained for ABT-737 in the aMD generated conformational frames by Glide docking protocol. Admittedly, it is not guaranteed that molecular dynamics simulations would always reproduce the conformations of crystal complexes, especially when the free energy barrier is prohibiting for a highly flexible protein system. Likely the process of biomolecular recognition takes place via a combination of population shift and induced fit mechanisms.47 Once the favorable protein conformation is selected by ligand binding the conformational ensemble will undergo a population shift, leading to further adjustments that stabilize ligand binding. Therefore it could take additional iterations of molecular dynamic simulations to obtain the optimal ligand-protein binding complex.



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In summary, the interface of the Bcl-xL PPI is highly flexible as a consequence of the collective motions of both the protein backbone and the side chain atoms. Based on the simulation of the apo Bcl-xL the “open” states of the functional binding site are relatively small probability events. This is intrinsically tied to the energetic penalties associated with exposing the hydrophobic surfaces to the polar solvent environment. Furthermore, the energy barriers are costly for transitioning from the highly populated ground states to the more druggable conformations. The aMD algorithm is advantageous given its gained efficiency through operational reduction of energy barriers. The Sophisticated computational algorithms such as VISM can detect the more “druggable” binding pockets through thorough characterization of the geometric properties and electrostatic compositions of the surface cavities. Combining the robust pocket mining algorithm with extensive conformational sampling by aMD, the protein states that are more suitable for ligand binding can be detected. The real population distribution may need adjustment taking into account the energy costs associated with the conformational transition. In the end, the truly druggable states of the highly inducible protein would provide more fruitful structural models for compound design and structure-based virtual screening.

4. Conclusions In drug discovery research, the necessity of considering protein dynamic and flexibility has long been recognized.48, 49 In an ideal docking protocol of virtual screening, each compound in the large database shall be attempted for every possible protein configuration to evaluate their mutual binding complementarity. However, for a typical corporate compound file consisting of several million compounds, molecular docking into hundreds or thousands of protein


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conformations quickly becomes time prohibitive. In reality, rigid protein structures, usually the crystal structures are used as protein models in computational applications as a tradeoff for speed. This practice, albeit pragmatic, can produce consequential limitation to the outcome of structure-based design. Using Bcl-xL as a model system we have performed extensive aMD simulations to investigate the conformational flexibility of the protein. Taking advantage of the robust sampling capability we are able to explore the large conformational ensembles during the course of 200 ns simulations. In comparison to the apo protein simulation the ligand-bound systems are stabilized toward the ensuing binary complexes exhibiting less conformational fluctuations. The major domain movements are observed around the α3-helical region as well as the connecting loop between the α1 and α2 helices which directly impacts the size and shape of the BH3 binding site. As a result the binding pocket in the apo protein simulation transitions between the “closed” form that is incompatible with ligand binding and the “open” state characterized by the widening and deepening of the BH3 groove. Using the physics-based VISM method the detailed geometric and electrostatic properties of the BH3 pocket are analyzed and tracked in a systematic manner throughout the molecular dynamic trajectories. Indicated by the overall druggability score, the majority of the simulation frames suggest rmsdency of undruggable states with the “closed” binding pocket manifested by the crystal structure of the apo Bcl-xL. Approximately 25% of the frames display sufficient volume for small molecule binding, and half of them harbor sufficient hydrophobicity to be deemed as druggable pockets. Furthermore, the highly druggable conformations, e.g. induced by the binding of sub-nanomolar inhibitor ABT-737, showed very low occurrences at less than 2% of the total population of the aMD simulation.



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Since the outcome of structure-based design is intricately reliant on the relevance of the structural model in use, the ability to identify the more druggable protein conformations is in paramount demand. Through efficient sampling of the conformational space, e.g., by aMD methodology, we demonstrate that the pocket conformation that is more productive for ligand binding can be attained. Without being stabilized by its binding partner, such conformations are expectedly low probability events. With the aid of the robust pocket mining algorithm, e.g. VISM, the most suitable ensemble of protein models can be detected to be employed in the structure-guided compound design.

5. Acknowledgement The authors thank Drs. Brian Gerstenberger and Suvit Thaisrivongs for helpful discussions. This research is funded by Pfizer postdoctoral program. We thank the reviewers for their insightful suggestions.

Supporting Information Available: Detailed information on aMD methodology and simulation, relaxation protocol, VISM theory, protein-ligand interaction maps for Bcl-xL/Bad and BclxL/ABT-737, representative conformations and binding pocket delineations from principal component (PC) space, individual PC plots for apo Bcl-xL, Bcl-xL/Bad and Bcl-xL/ABT-737, correlation plot between pocket volume and druggability along with references, druggability profile of BH3 binding pocket for Bcl-xL/Bad aMD simulation and standard deviation, docking pose of ABT-737. This material is available free of charge via the Internet at http://pubs.acs.org.


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