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Atomistic modeling of the ABL kinase regulation by allosteric modulators using structural perturbation analysis and communitybased network reconstruction of allosteric communications Lindy Astl, and Gennady M. Verkhivker J. Chem. Theory Comput., Just Accepted Manuscript • DOI: 10.1021/acs.jctc.9b00119 • Publication Date (Web): 24 Apr 2019 Downloaded from http://pubs.acs.org on April 25, 2019

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Journal of Chemical Theory and Computation

Atomistic modeling of the ABL kinase regulation by allosteric modulators using structural perturbation analysis and community-based network reconstruction of allosteric communications Lindy Astl1, Gennady M. Verkhivker1,2

1

‡

Graduate Program in Computational and Data Sciences, Keck Center for Science and

Engineering, Schmid College of Science and Technology, Chapman University, One University Drive, Orange, CA 92866, USA 2

Depatment of Biomedical and Pharmaceutical Sciences, Chapman University School of

Pharmacy, Irvine, CA 92618, USA ‡corresponding author E-mail: [email protected]

ACS Paragon Plus Environment

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Abstract In this work, we have examined the molecular mechanisms of allosteric regulation of the ABL tyrosine kinase at the atomic level. Atomistic modeling of the ABL complexes with a panel of allosteric modulators has been performed using a combination of molecular dynamics simulations, structural residue perturbation scanning and a novel community analysis of the residue interaction networks. Our results have indicated that allosteric inhibitors and activators may exert a differential control on allosteric signaling between the kinase binding sites and functional regions. While the inhibitor binding can strengthen the closed ABL state and induce allosteric communications

directed from the allosteric pocket to the ATP binding site, DPH

activator may induce a more dynamic open form and activate allosteric couplings between the ATP and substrate binding sites. By leveraging a network-centric theoretical framework, we have introduced a novel community analysis method and global topological parameters that have unveiled

the hierarchical modularity and

the inter-community bridging sites in the residue

interaction network. We have found that allosteric functional hotspots responsible for the kinase regulation may serve the inter-modular bridges in the global interaction network.

The central

conclusion from this analysis is that the regulatory switch centers play a fundamental role in the modular network organization of ABL as the unique inter-community bridges that connect the SH2 and SH3 domains with the catalytic core into a functional kinase assembly. The hierarchy of network organization in the ABL regulatory complexes may allow for the synergistic action of dense inter-community links required for the robust signal transfer in the catalytic core and sparse network bridges acting as the regulatory control points that orchestrate allosteric transitions between the inhibited and active kinase forms.


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Introduction Allosteric regulation is a common mechanism employed by proteins and complex biomolecular assemblies for regulation of their activity and adaptability in the dynamic cellular environment during processes of signal transduction, catalysis, and gene regulation.1-5 The initial conceptual framework and theoretical basis of allosteric regulation were proposed in a seminal study by Monod, Wyman and Changeux (MWC model) postulating that the ligand binding to one protein subunit can trigger the concerted conformational changes and lead to a long-range protein response at the distant sites, thereby altering the ligand affinity to other protein subunits.1 An alternative model was later developed by Koshland, Enmity and Filmer (known as KNF sequential model), according to which allosteric coupling and global conformational changes in proteins can occur through a cascade of discrete conformational changes.2 Fifty years after the KNF and MWC models had been proposed, the quantitative characterization of allosteric protein regulation continues to be at the forefront of modern molecular biology owing to the complexity, diversity and dynamic nature of these biological events.3,4 The remarkable advances in the X-ray crystallography, NMR and biophysical techniques have enabled numerous detailed investigations of large protein systems and conformational dynamic processes at atomic resolution.5-7 These developments have fueled the resurgence of computational and experimental studies of allosteric regulation, leading to novel conceptual outlooks and various attempts to develop a unified theory of this long-standing biological phenomenon. Most notably, the thermodynamics-based conformational selection model of allosteric regulation has exploited the energy landscape theory of protein dynamics, assuming that a statistical ensemble of preexisting conformational states and communication pathways is inherent to a protein system and can be modulated through allosteric ligand perturbations.8-12 While studies


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of allosteric regulation are often focused on thermodynamic aspects of the mechanism, there has been an increasing realization of the critical role of conformational dynamics, which is central to the ‘entropy-driven’ allosteric model13-16

in which allosteric interactions are propagated

through dynamically modulated functional motions in the absence of structural transformations or even visible structural changes.

Allosteric regulation is an intrinsic and global property of

complex biological systems that can be characterized by dynamic networks of interactions between their components and integrated modules. Among recently advocated approaches to quantify and explain allosteric regulation events are graph-based network approaches that reduce the complexity of protein structures and interactions to one-dimensional maps comprised of nodes (residues) connected by edges (inter-residue interactions).17-23 The topological organization and dynamic evolution of the residue interaction networks can adequately characterize the ensembles of protein conformational states and communication pathways that transmit signals by propagating conformational fluctuations throughout the protein. By mapping the thermal fluctuations in a protein system onto a protein structure graph where the edges are weighted by the strength of residue interactions and correlated motions, the recent network-based computational studies have successfully identified key functional centers and characterized allosteric interactions in a variety of proteins.24-32 The integration of dynamic correlation parameters

into network-centric protein models allows to adequately

capture and explain allosteric protein responses to ligand binding as was recently manifested in an illuminating study of drug–drug synergistic activity mediated by allosteric cross-talk in chromatin. 33 According to this investigation, binding of drug RAPTA-T can trigger a cascade of correlated motions promoting allosteric interactions and communication through the histone core to elicit the concerted protein response at the distal active site of an unrelated drug


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auranofin. The insights from multi-microsecond time scale molecular simulations and the incorporation of correlated dynamic fluctuations into a community-based network analysis have enabled atomistic reconstruction of the activation mechanism of the Cas9 catalytic domains for DNA cleavage, revealing molecular details that may control CRISPR-Cas9 functionality.34 The advantages and limitations of the network-centric framework in advancing our understanding of allosteric regulation and informing applications in drug design have been extensively discussed in a recent comprehensive review of allostery.35 The human protein kinases are allosterically regulated molecular switches that respond to cellular signals by modulating the dynamic equilibrium between the inactive and active states of the kinase domain (KD).36-40 It is well-established that conformational transitions in the protein kinases can be orchestrated by the conserved HRD and DFG motifs in the kinase core that are coupled with the regulatory αC-helix to form

the intramolecular networks termed

as the regulatory spine (R-spine) and the catalytic spine (C-spine).36-40 Structural and biochemical studies of ABL kinases have provided a molecular footprint for understanding the activation and inhibitory kinase mechanisms by unveiling structural organization of the regulatory complexes.41-45 In the downregulated ABL-SH2-SH3 complexes, the SH2 domain interactions with the C-terminal lobe of the KD form an autoinhibitory clamp that maintains the KD core in a conformation with a low catalytic activity (Figure 1). Biochemical studies have shown that the intramolecular SH2-KD interactions in ABL can allosterically induce the kinase activation.46,47 The latest NMR study has revealed a detailed atomistic picture of allosteric regulation in ABL kinase, showing how the interacting signaling modules cooperate to form a multilayered regulatory mechanism that exploits various allosteric switches powered by binding or phosphorylation at different sites of the regulatory assemblies.48


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Figure 1. Structural organization of the ABL complexes with allosteric modulators. (A) The structure of the ABL-SH2-SH3 complex with Nilotinib and Asciminib. The core domains are shown : SH3: Src homology domain 3 ( in magenta), SH2: Src homology domain 2 ( in orange), SH2-kinase linker (SH2 Linker) ( in cyan), and kinase domain (KD) (in red). The inhibitors are shown in sticks with atom-based coloring. (B) A close-up of the allosteric binding site shows bending of the αI-helix in the closed inhibitory form of ABL. (C) The structure of the ABL-KD complex with Imatinib and GNF-2 inhibitors that are shown in sticks. (D) A close-up of the allosteric site highlights bending of the αI-helix induced by GNF-2 binding. (E) The structure of the ABL-KD complex with Imatinib and allosteric DPH activator. (F) A close-up of binding of DPH to the myristate pocket shows unbending of the αI-helix that leads to disruption of the autoinhibitory constraints and activation of ABL kinase.


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The quantitative characterization of molecular mechanisms underlying complexity of allosteric regulation has become particularly important due to a considerable interest in the discovery of selective allosteric inhibitors for a variety of therapeutically important targets, including protein kinases.49-51 Structural and biochemical studies have identified the allosteric binding site in ABL kinase and demonstrated that binding of an allosteric inhibitor GNF-2 to the myristoyl pocket in the C-lobe can induce long-range dynamic changes in the ATP-binding site and inhibit the kinase activity.52,53 GNF-5 (a GNF-2 analog with the improved pharmacokinetics profile) used in combination with the ATP-competitive drugs Imatinib or Nilotinib can inhibit both the wild-type (WT) and ABL-T315I drug-resistant mutant in biochemical and cellular assays.53 ABL001 (Asciminib) is another allosteric ABL inhibitor that impedes kinase activity by targeting the myristoyl pocket.54-56 The inhibition mechanism exploited by these allosteric modulators was detailed in structural studies, revealing that the ligand-induced bending of the αI helix in the C-terminal lobe (Figure 1A-D) can promote the SH2-KD interactions and facilitate stabilization of the closed inhibited conformation.52-56 This structural feature shared by the allosteric inhibitors Asciminib (Figure 1A,B) and GNF-2 (Figure 1C,D) has emerged as the essential prerequisite required for stabilization of the closed inhibited form of the enzyme.54-56

The observed synergistic actions of the allosteric and ATP-competitive kinase

inhibitors have confirmed that ligand binding in the allosteric pocket of ABL can perturb dynamics at the distal regions and elicit an efficient cross-talk between the binding sites and functional regions of the catalytic domain. However, it still remains unclear how this long-range effect is transmitted from the myristoyl pocket to the ATP binding site. While the allosteric inhibitors that mimic binding of myristate and induce bending of the αI helix are typically functional antagonists, ligands that bind to the same pocket without causing this specific


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conformational change can often function as kinase agonists.57 A high-throughput screen has discovered a class of allosteric activators that target the myristoyl pocket of ABL-KD but can enhance the rate of kinase activation in the presence of ATP.58 Unlike allosteric inhibitors, allosteric agonist DPH forces the αI-helix into a linear extended conformation that promotes disengagement of the SH2 domain and conformational transition to the active ABL form (Figure 1E,F). These studies have indicated that local structural environment near the myristate pocket can serve as a sensor of ligand binding, triggering either stabilization of the inactive state or conformational shift and activation. Computational approaches have played a vital role in revealing the atomistic details of the protein kinase regulation through extensive molecular dynamics (MD) simulations of the kinase domain and regulatory assemblies.59-67 Modeling of allosteric kinase regulation by integrating MD simulations and dynamic network analysis of protein structures was extensively explored in our earlier studies of the ABL, SRC and ErbB kinases59-64 producing a plausible mechanistic model of allosteric coupling between the ATP-binding and the substrate binding sites. The detailed atomistic reconstruction of allosteric interactions in the SRC kinase using the network-based framework has revealed a similar mechanism,65 suggesting that the conserved topological architecture of the protein kinase networks may define the unified aspects of allosteric signaling and dictate localization of the regulatory hotspots in these proteins. A multi-disciplinary approach combining simulations, functional assays and mutagenesis showed that the SH2-KD interactions can allosterically stabilize the catalytically competent position of the αC-helix and thus exert control over kinase activity.66 Computational and theoretical studies have shown that examining proteins as dynamic regulatory machines that continuously fluctuate between different allosteric states in response to ligand binding is


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a key ingredient to understanding the underlying complexity of allosteric regulation. However, the quantitate characterization of these highly dynamic and often elusive processes continues to present considerable challenges. In this work, we probe the conjecture that allosteric interaction networks and pathways in the regulatory kinase complexes can be dynamically remodeled based on the adopted allosteric mechanism of kinase inhibition or activation. We explore and quantify mechanisms of differential modulation of the ABL kinase regulatory complexes with Asciminib/GNF-2 inhibitors and DPH activator at the atomic level. By using a combination of MD simulations, distance fluctuation analysis and structural residue perturbation scanning we set out to examine ligand-induced modulation of conformational dynamics in the ABL complexes. Through integration of MD simulations and residue perturbation response analysis, we quantify allosteric propensities of protein residues and identify effector and sensor residues that mediate the propagation of dynamic fluctuations in allosteric interaction networks. By leveraging community analysis of the residue interaction networks and topological parameters for identification of the inter-modular network bridges, we propose a computational framework for the atomistic reconstruction of allosteric communications in protein kinases. Using this theoretical framework, we quantify regulatory scenarios exploited by the allosteric modulators to differentially control a cross-talk between functional regions in the ABL structures. Our study also reveals that the regulatory switch centers of kinase activity may serve as unique global mediators of allosteric interactions that enable a dynamic assembly of the ABL structural modules into a functional complex. We argue that exploiting modularity of the interaction networks by targeting weak links and inter-community bridges may offer a plausible approach for engineering and modulating kinase activities.


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Materials and Methods MD Simulations MD simulations of the ABL crystal structures (500 ns for each structure) were performed for the ABL regulatory complex with Asciminib/Nilotinib (pdb id 5MO4), ABL-KD complex with GNF-2 /Imatinib (pdb id 3K5V) and DPH/Imatinib (pdb id 3PYY). The crystal structures were obtained from the Protein Data Bank.68 The missing residues, unresolved structural segments and disordered loops were modeled with the ArchPRED server.69 MD simulations were carried out using NAMD 2.6 package70 with the CHARMM27 force field71 and the explicit TIP3P water model. The employed MD protocol is consistent with the overall setup that was described in details in our earlier studies.64 The initial structures were solvated in a water box with the buffering distance of 10 Å. Long-range non-bonded van der Waals interactions were computed using an atom-based cutoff of 12Å with switching van der Waals potential beginning at 10Å. For all simulations, the SHAKE method was used to constrain all bonds associated with hydrogen atoms.72 A step size of 2 fs was used, and simulation trajectories were saved every 1 ns. The smooth particle mesh Ewald (PME) method73 was employed to treat the long-range electrostatics. All atoms of the complex were first restrained at their crystal structure positions with a force constant of 10 Kcal mol-1 Å-2. The system was subjected to gradual heating and cooling simulated annealing protocol to ensure the proper equilibration. An NPT production simulation was subsequently run on the equilibrated structures for 500 ns keeping the temperature at 300 K and constant pressure (1 atm) using Langevin piston coupling algorithm. Principal component analysis (PCA) of the MD conformational ensembles was performed using the CARMA package.74


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Distance Fluctuations Analysis We evaluated allosteric propensities of protein residues using distance fluctuation analysis of the conformational ensembles for studied systems. Our approach is rooted in the protein mechanics-based approach75,76

in which the fluctuations of the mean distance between a given

residue and all other residues in the conformational ensemble are converted into distance fluctuations indexes that measure the energy cost of the residue deformation during MD simulations.

The high index values are associated with stable residues that exhibit relatively

small fluctuations in their distances to all other residues, while small values of this parameter would correspond to more flexible positions that fluctuate considerably and produce large deviations of their inter-residue distances. In this model, residues with high index value are considered to have strong allosteric propensities. Our previous studies showed that distance fluctuations profiles and respective force constant indexes can be related with allosteric residue propensities as the mean square fluctuations between a pair of residues provides an adequate estimate of the signal commute time.64 We computed the fluctuations of the mean distance between each atom within a given residue and the atoms that belong to the remaining residues of the protein. The distance fluctuation index for each residue is computed as the average of the fluctuation indexes for all its atoms. Alternatively, the mean fluctuations of a given residue can be also characterized using only C atom positions. In our model, the distance fluctuation index for each residue is calculated by averaging the distances between the residues over the MD trajectory using the following expression:

ki 

3kBT ( di  di ) 2

(1)


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di  dij

d ij

j*

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(2)

is the instantaneous distance between residue i and residue j , k B is the Boltzmann constant,

T =300K.

denotes an average taken over the MD simulation trajectory and di  dij

j*

is

the average distance from residue i to all other atoms j in the protein (the sum over j* implies the exclusion of the atoms that belong to the residue i ). The interactions between the C atom of residue i and the C atom of the neighboring residues i -1 and i +1 are excluded in the calculation since the corresponding distances are nearly constant. The inverse of these fluctuations yields distance fluctuation parameter ki. This parameter describes the ease of moving an atom (or residue) with respect to the rest of the protein structure. The average distance fluctuations profiles define allosteric propensities and can also characterize the distribution of stable and flexible regions in the protein structures. Perturbation Response Scanning Perturbation Response Scanning (PRS) approach77,78 allows to evaluate residue displacements in response to external forces. In this approach, it 3N × 3N Hessian matrix 𝑯 whose elements represent second derivatives of the potential at the local minimum connect the perturbation forces to the residue displacements. The 3N-dimensional vector 𝚫𝑹 of node displacements in response to 3N-dimensional perturbation force follows Hooke’s law 𝑭 = 𝑯 ∗ 𝜟𝑹. A perturbation force is applied to one residue at a time, and the response of the protein system is measured by the displacement vector ∆𝑹(𝑖) = 𝑯−𝟏 𝑭(𝒊) that is then translated into N×N PRS matrix.29 The second derivatives matrix 𝑯 is obtained from simulation trajectories for each protein structure, with residues represented by 𝐶𝛼 atoms and the deviation of each residue from an average


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structure was calculated by ∆𝐑𝑗 (𝑡) = 𝐑𝑗 (𝑡) − 〈𝐑𝑗 (𝑡)〉, and corresponding covariance matrix C was then calculated by ∆𝐑∆𝐑𝑇 . We sequentially perturbed each residue in the ABL structures by applying a total of 250 random forces to each residue to mimic a sphere of randomly selected directions.79 The displacement changes, ∆𝑹𝒊 is a 3N-dimensional vector describing the linear response of the protein and deformation of all the residues. Using the residue displacements upon multiple external force perturbations, we compute the 2

(𝒊) magnitude of the response of residue k as 〈|∆𝑹𝒌 | 〉 averaged over multiple perturbation forces

𝑭𝑖 yielding the 𝑖𝑘 𝑡ℎ element of the N×N PRS matrix. A measure for the response of residue k is 2

(𝒊) the magnitude 〈|∆𝑹𝒌 | 〉 of the kth block of ∆𝑹𝒊 averaged over multiple F(i), expressed as the

ikth element of the N×N PRS matrix, SPRS. The elements of SPRS refer to unit (or uniform) perturbing force. The response to unit deformation at each perturbation site is obtained by dividing each row by its diagonal value:

0  1 / d1   S PRS    S PRS  0 1 / d N  

(3)

The ith row of SPRS is referred to as the effector profile generated upon perturbing residue. The average effect of the perturbed effector site 𝑖 on all other residues is computed by averaging over all sensors (receivers) residues 𝑗 and can be expressed as〈(∆𝑹𝒊 )2 〉𝑒𝑓𝑓𝑒𝑐𝑡𝑜𝑟 . The effector profile

〈(∆𝑹𝒊 )2 〉𝑒𝑓𝑓𝑒𝑐𝑡𝑜𝑟 describes the average effect that local perturbation in the effector site

𝑖 has on all other residues. Similarly, the 𝑗 𝑡ℎ column of the PRS matrix SPRS represents the sensitivity profile of sensor residue j in response to perturbations of all residues and its average is denoted as 〈(∆𝑹𝒊 )2 〉𝑠𝑒𝑛𝑠𝑜𝑟 . ACS Paragon Plus Environment

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Protein Structure Network Analysis We employed a graph-based representation of protein structures20,21 to represent residues as network nodes. Dynamic contact maps of residue cross-correlations22 and coevolutionary residue couplings80 were used to define edges (interacting nodes) in the construction of the residue interaction network as described in details in our previous study.81 The edge lengths in the network are obtained using the generalized correlation coefficients 𝑹𝑀𝐼 (𝑿𝒊 , 𝑿𝒋 )associated with the dynamic correlation and mutual information shared by each pair of residues. The length (i.e. weight) 𝑤𝑖𝑗 = − log[𝑹𝑀𝐼 (𝑿𝒊 , 𝑿𝒋 )] of the edge that connects nodes

i and j is calculated

from the corresponding generalized correlation coefficient between the nodes.82 Network edges were weighted for residue pairs with 𝑹𝑀𝐼 (𝑿𝒊 , 𝑿𝒋 ) > 0.5 in at least one independent simulation. The ensemble of shortest paths is determined from matrix of communication distances by the Floyd-Warshall algorithm.83 Network graph calculations were performed using the python package NetworkX.84 Protein structure networks were initially analyzed for detection of k cliques and k -clique communities using Clique Percolation algorithm85,86 in which community is associated with a subgraph containing k -cliques that can be reached from each other through a series of adjacent k-cliques. We employed a community definition according to which in a k clique community two k -cliques share k  1 or k  2 nodes. The local communities that remained stable in > 75% of the conformations in the equilibrium ensemble were reported and analyzed. Using the constructed protein structure networks, we computed the residue-based betweenness parameter. The betweenness of residue i is defined to be the sum of the fraction of shortest paths between all pairs of residues that pass through residue i :


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N

g jk (i)

j k

g jk

Cb (ni )  

(4)

where g jk denotes the number of shortest geodesics paths connecting j and k , and g jk (i) is the number of shortest paths between residues j and k passing through the node ni . All topological measures were computed using

the python module the python package

NetworkX84 and Cytoscape platform for network analysis.87

The RING program88,89 was

used to generate the residue interaction networks with RINalyzer90 and NetworkAnalyzer91 plugins employed for analysis. Community Analysis and Inter-Modular Bridgeness Parameters The Girvan-Newman algorithm92-94

is used to identify local communities

and optimize

modularity of the interaction network. In this approach, edge centrality (edge betweenness) is defined as the ratio of all the shortest paths passing through a particular edge to the total number of shortest paths in the network. The methods employ an iterative elimination of edges with the highest number of the shortest paths that go through them. By eliminating edges the network breaks down into smaller communities. The algorithm starts with one vertex, calculates edge weights for paths going through that vertex, and then repeats it for every vertex in the graph and sums the weights for every edge. In complex networks it is often the case that more edges have the same highest edge betweenness. We employed a modification of Girvan-Newman method, where instead of a single highest edge betweenness removal, all highest betweenness edges are removed at each step of the protocol. This modification

makes community structure

determination invariant to the labeling of the nodes in the graph and leads to a more stable solution. The modified algorithm proceeds through the following steps : 1) Calculate edge


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betweenness for every edge in the graph; 2) Remove all edges with highest edge betweenness within a given threshold; 3) Recalculate edge betweenness for remaining edges; 4) Repeat 2-4 until graph is empty. The residue betweenness is used to rank the most influential nodes in the network and communities.

For defining community leader nodes, we follow the Leader-

Follower algorithm, in which a community is defined as a clique and is characterized by the presence of a leader and at least one ‘loyal follower’.95 Community leaders are defined as nodes that (a) are connected not only to members of the local community but also have neighbors outside of the community; and (b) whose distance to other nodes in the network is less than the neighbors in their respective communities. Loyal follower in a community is defined as a residue node that only has neighbors within this single community. To characterize global bridges from a community structure, bridgeness metric similar to Rao-Stirling index.96-98

we

introduce a community

This parameter uses as input a prior

categorization of the nodes into distinct communities : 𝐺(𝑖) = ∑𝑗∈𝐽 𝑙𝐼𝐽 𝛿𝑖𝐽 (5) where the sum is over communities 𝐽 (different from the community of node 𝑖, denoted as 𝐼), 𝛿𝑖𝐽 is equal 1 if there is a link between node 𝑖 and community 𝐽 and 0 otherwise. 𝑙𝑖𝐽 corresponds to the effective distance between community 𝐼 and community 𝐽 as measured by the inverse of the number of links between them. Nodes that are only linked to nodes of their own community, i.e. loyal follower nodes have 𝐺(𝑖)=0, while community leader nodes involved in bridging two (or more) communities have a positive value of the index. The ModuLand program within the Cytoscape platform87 was adapted to determine a hierarchical network structure and the intercommunity bridging sites that connect modules in the protein structures.


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Results and Discussion MD Simulations of the ABL Kinase Complexes with Allosteric Modulators We performed 500 ns all-atom MD simulations for three different ABL-ligand complexes using crystal structures of the ABL-SH2-SH3 complex bound with Asciminib (pdb id 5MO4) and the crystal structures of the catalytic KD in complexes with GNF-2 inhibitor (pdb id 3K5V) and DPH activator (pdb id 3PYY). The following specific objectives were addressed in our simulations: (a) characterization of the intrinsic differences in the conformational dynamics of the ABL kinase regulatory core bound to the allosteric inhibitors and activator; (b) analysis of the ligand-induced conformational variations in the allosteric binding site and the ATP binding region; (c) analysis of the long-range dynamic changes and coupling between binding sites that may be relevant for the mechanism of kinase inhibition and activation.

These simulations

were analyzed to characterize the effect of allosteric modulators on the conformational dynamics of functional regions in the kinase catalytic core (Figure 2A). We also examined how presence of the SH3 and SH2 domains in the closed inhibited form can affect dynamic fluctuations in the ABL regulatory complex with Asciminib (Figure 2B). An important observation of this analysis was a notable reduction in the conformational mobility of the KD core in the ABL complexes with GNF-2 and Asciminib inhibitors as compared to the greater conformational variations of the catalytic domain in the complex with DPH activator (Figure 2A). In the regulatory ABL-SH2-SH3 complex with Asciminib, the stabilizing effect of the inhibitor was even more pronounced (Figure 2A), including the increased rigidity of the allosteric binding site residues. The binding site residues involved in hydrogen bonding (A462, G482, C483, A356, Y454, R351, E481, and A452) and residues forming hydrophobic contacts (A356, L359, L360, L448, I451, A452, C483, P484, V487, I521, and V525) with Asciminib


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experienced the restricted mobility upon inhibitor binding (Figure 2A,B). In the ABL complex with DPH activator, the number of hydrogen-bonding binding site residues was reduced (A356, E481, A452, and Y454), while the corresponding KD regions in the C-lobe experienced larger fluctuations and enjoyed the greater flexibility (Figure 2A,B). The synergistic allosteric effect of Asciminib binding and SH2-KD interactions on stabilization of the catalytic core was especially significant in the N-lobe regions close to the ATP site (residues 290-330), and the Clobe regions proximal to the myristate pocket (residues 450-490) (Figure 2 A, B). The conformational fluctuations in the regulatory αC-helix (residues 299-311) were markedly reduced in the ABL-Asciminib complex as compared to the ABL-DPH complex. In the ABLAsciminib complex, the thermal fluctuations of the P-loop and αC-helix were especially small as conformational mobility of the KD is largely restricted due to an autoinhibitory clamp that locks the core domain in a conformation with a low catalytic activity (Figure 2A,B). The dynamic differences between the ABL complexes with GNF-2 and DPH were smaller, but displayed a fairly similar trend. These results indicated that the long-range effects of the allosteric inhibitors could be more apparent in the complete ABL-SH2-SH3 complex, due to synergistic contributions of the SH2-KD and the inhibitor-KD interactions that together stabilize the closed enzyme form. Our findings are consistent with the NMR studies that revealed a synergistic nature of the structural elements forming the regulatory ABL-SH2-SH3 machinery that allows for the efficient ligand-induced modulation of the kinase activity between complete suppression and full activity.48 It is noteworthy repeating that the salient feature of the ABL complexes with inhibitors is bending of the αI-helix on the C-lobe of the KD (residues 504-531). The bending region of the αI helix starts at F516 and is followed by a short loop and a three-turn helix, called the αI’ helix


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(Figure 1). While the inhibitor binding to the myristate pocket can induce bending of the αI'helix and formation of the inhibited kinase conformation, binding of DPH is not compatible with the inactive conformation and leads to unbending of the αI'-helix and disruption of the autoinhibitory constraints (Figure 1). It is believed that this structural feature could trigger longrange changes in promote inhibition of ABL by the inhibitors, while DPH activates ABL kinase. While conformational fluctuations in this region were clearly greater in the ABL-DPH complex, the αI-helix on the C-lobe of the ABL complexes with inhibitors could still experience moderate mobility, particularly in the more solvent-exposed αI’ helix (residues 516-531) (Figure 2). We observed an appreciable difference in stability of this bending region only for residues 504-515 (αI helix region)

and when the complete ABL-SH2-SH3 regulatory complex was

considered (Figure 2).


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Figure 2. Conformational dynamics of the ABL kinase complexes with allosteric modulators. (A) The computed B-factors for the KD residues obtained from MD simulations of the ABLAsciminib complex (in blue lines), ABL complex with GNF-2 (in red lines) and ABL-DPH complex (in green lines). (B) The dynamics profile of the ABL-SH2-SH3 complex with Asciminib. The computed B-factors for the SH3 domain residues are in blue lines, SH2 domain in red lines, and the SH2 linker in green lines and KD residues in maroon lines. (C) The crosscorrelation matrices of residue fluctuations along the 10 low frequency modes are obtained from PCA computations of the ABL complex with GNF-2. (D) The cross-correlation heat maps for the ABL-DPH complex. PCA computations based on the backbone heavy atoms (N, Cα, Cβ, C, and O) and Cα atoms resulted in similar cross-correlation heat-maps. For simplicity, we present the results of PCA computations using Cα atoms. The axes denote Cα atoms of the protein residues in sequential order, so that each cell in the plot shows the correlation of two residues in the protein. Cross-correlations of residue fluctuations vary between +1 (fully correlated motion, colored in green) and -1 (fully anti-correlated motions, colored in red).

The cross-correlation matrices of residue fluctuations along the low frequency modes

pointed

to the significant inter-residue couplings in the ABL complex with GNF-2 inhibitor (Figure 2C) as compared to the ABL-DPH complex (Figure 2D). Moreover, allosteric inhibitors induced a stronger long-range coupling between the kinase lobes, particularly between the allosteric and ATP binding sites. In the ABL-DPH/Imatinib complex, these couplings were markedly weaker, reflecting a more dynamic conformation of the kinase core and the highly flexible N-terminal and C-terminal regions (Figure 2D). In general, the cross-correlation maps of the residue fluctuations in the catalytic domain were indicative of more cooperative allosteric interactions in


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the rigid ABL-inhibitor conformations (Figure 2C) as compared to a rather loose pattern of the correlated motions in the ABL-DPH complex (Figure 2D). We also performed PCA of the conformational dynamics in the ABL structures and analyzed the essential mobility profiles of the kinase residues (Figure S1, Supporting Information). In the ABL-KD complexes with GNF-2 inhibitor and DPH activator, the slow mode profiles displayed similar shapes due to a common topology of the catalytic domain. Nonetheless, we noticed large functional fluctuations in the ABL-DPH complex, particularly in the N-lobe. In the ABL complex with Asciminib, we found several deep local minima corresponding to the functionally important hinge positions including Y158 of the SH2 domain, P242 of the SH2 linker, Y361 (phosphorylation site on the C-terminal of KD), D400, F401 of the regulatory DFG motif, L448 of the C-lobe (Figure S1, Supporting Information). These results showed that the functional relevance of these residues (especially Y158, P242 and Y361) can be partly determined by their coordinating role in driving functional motions in the downregulated kinase form. Y158 and Y361 residues are located at the SH2-KD interface and corresponded to the critical hinge position of the regulatory complex (Figure S1, Supporting Information). Consistent with these observations, mutations of Y158 in the SH2-KD can dramatically increase the kinase activity as these modifications unlock SH2 domain and promote activating changes.41,46,47 NMR and biochemical experiments showed that P242E/P249E double mutant can modulate the dynamic two-state equilibrium in ABL by disrupting the linker interactions, and causing a pronounced increase in the population of the activated state.48 Interestingly, according to our data P242 of the SH2 linker is aligned with one of the hinge positions that controls functional movements of the SH2 domain and the linker.


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The important revelation of this analysis was that several experimentally known regulatory switches of the ABL activity can act as hinge points controlling global functional movements during the dynamic equilibrium between the open and closed inhibited states of ABL. Overall, the conformational dynamics analysis suggested that the examined allosteric inhibitors and DPH activator may differentially modulate conformational dynamics of the ABL catalytic core. We noticed that the enhanced conformational fluctuations in the ABL-DPH/Imatinib complex may be similar to the open inhibited form of the ABL kinase core. These findings are in agreement with the NMR studies99 according to which the ligand-unbound form of the ABL regulatory assembly is predominantly in the closed autoinhibited state which closely resembles the down-regulated crystal structure of ABL. Moreover, while Imatinib binding to the ABL can induce conformational changes and promote stabilization of the open kinase form with more flexible SH2/SH3 domains, the addition of the allosteric inhibitor GNF-5 to the ABL-Imatinib complex can fully restore the closed, inactivated state.99 Our results are consistent with these data, indicating that GNF-2 and Asciminib inhibitors can allosterically reduce mobility of the entire catalytic core and stabilize the ATP binding site residues by favoring the closed inhibited ABL form. In contrast,

the conformational

dynamics of the ABL-DPH/Imatinib complex may promote the greater mobility of the catalytic domain and be conducive to the Imatinib preferences for an open and more flexible catalytic core. These findings are supportive of the conformational selection mechanism and a ‘twostate’ equilibrium model of the ABL regulation in which the kinase switches between a structurally rigid inhibited form and a more dynamic and heterogeneous active state.48,99


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Distance Fluctuations Analysis Probes Allosteric Residue Propensities in the ABL Complexes Distance fluctuations analysis of the conformational ensembles was used to probe structural stability and allosteric communication propensities of the protein residues in the studied ABL complexes. Our previous studies suggested that distance fluctuations profiles could serve as robust indicators of allosteric residue propensities, as the mean square residue-residue fluctuations are related to the efficiency of propagation of the allosteric signal in the protein structure and can reproduce experimentally known regulatory sites involved in allostery.100-102 In this model, structurally stable residues with high values of the distance fluctuation index may exhibit the enhanced communication efficiency.

By using MD simulations, we computed the distance fluctuation profiles for the ABL complexes and examined differences in the communication distributions and localization of major allosteric centers (Figure 3A,B). In the ABL-Asciminib complex, the distance fluctuation profile featured several sharp prominent peaks that corresponded to the experimentally known regulatory sites (Figure 3A). In particular, we found that the key regulatory positions, including the SH2 residues (Y134, Y158) and the SH2 linker residues P242/P249 aligned almost precisely with the sharp peaks of the profile. Interestingly, several clear peaks in the ABL-KD regions corresponded to functional residues from the ATP site (V318 and the gate-keeper position T334I ) and allosteric site residues that formed strong contacts with Asciminib ( A363, L448, P484). Notably, another characteristic peak of the distribution coincided with F401 of the regulatory 400-DFG-402 motif at the beginning of the activation loop (Figure 3A).


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A similar distribution was seen in the ABL-KD complex with GNF-2 inhibitor (Figure 3B) highlighting a strong contribution of the regulatory sites in the allosteric binding site (A363, L448), the catalytic triad HRD (residues 380-382) and the DFG motif (residues 400-402). Structural mapping of the distribution peaks in the ABL-Asciminib complex illustrated that residues with high allosteric propensities can occupy strategic positions in the SH2-KD interface, SH2 linker-KD regions, and near the allosteric binding site (Figure 3C). In the ABL complexes with Asciminib and GNF-2, the density of potential allosteric centers tend to gravitate towards the proximity of the allosteric site (Figure 3C,D) suggesting that allosteric inhibitors can establish control over long-range communications in the catalytic core and govern the allosteric cross-talk with the ATP binding site (Figure 3C,D). Interestingly, more significant changes in the distance fluctuation distribution can be induced upon binding of the DPH activator (Figure 3B,E). First, we observed that peaks associated with the functional residues in the allosteric site (A363, L448, P484) were significantly reduced. Second, the distribution peaks corresponded to the

R-spine positions (H380, F401, and D440) and W424

from the conserved WTAPE motif in the substrate P+1 loop (Figure 3B,E).

The new peaks

were also noticed for C-spine residues (C388, L389, and I451). Hence, in a more dynamic ABL-DPH structure the allosteric centers tend to shift away from the allosteric site and consolidate in the ATP binding site and substrate binding site. The network formed by residues with high allosteric propensities in the ABL-DPH complex can enhance the long-range communication between the ATP binding site and the substrate binding region.

Accordingly, binding of DPH activator may trigger global redistribution in the

allosteric residue propensities that properly position the kinase domain for transition to a catalytically competent active state.


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Figure 3. The distance fluctuation profiles for the ABL kinase complexes with allosteric modulators. (A) the residue-based distance fluctuation distribution for the ABL-SH2-SH3 complex with Asciminib is shown : SH3 domain residues are in blue lines, SH2 domain in red lines, and the SH2 linker in green lines and KD residues in maroon lines. (B) The distance fluctuation profiles for the KD residues obtained from MD simulations of the ABL-Asciminib complex (in blue lines), ABL complex with GNF-2 (in red lines) and ABL-DPH complex (in green lines). (C) Structural mapping of the distribution peaks (high allosteric propensities) for the ABL-Asciminib complex. The putative allosteric sites with high distance fluctuation index are shown in red spheres. Structural projection of predicted allosteric residues for ABL complex with GNF-2 (D) and ABL-DPH complex (E). The bound inhibitors are shown in atombased color coded sticks.


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Rigidity-Flexibility Decomposition in the ABL Structures Reveals a Differential Effect of Allosteric Modulators on the Kinase Mobility

To further analyze the role of allosteric ligands in modulating the dynamics and stability of the kinase core, we

performed

the constraint network analysis (CNA) and

graph-based

decomposition of the ABL structures into rigid clusters and flexible connections using the FIRST approach103-107 and the Python-based

CAN interface.108,109 In brief, this algorithm

defines a rigid cluster as a set of residue nodes that move together as a rigid body, whereas residues that are not a component of a rigid cluster become assigned to a flexible region. Within the framework of the network-based FIRST approach103-107 one could rapidly emulate thermal unfolding of protein structures through a gradual removal of the non-covalent constraints from the constraint protein structure network. During this process, the weak constraints are removed first

and the stronger interactions are maintained longer, leading to the protein structure

decomposition into rigid and flexible regions. The flexible protein components that break away from the giant rigid cluster near the transition point are identified as ‘weak spots’ (also often termed as

‘unfolding nuclei’)

that can trigger

the progressive rigidity-flexibility

decomposition.108,109 The localization of high frequency weak spot residues characterizes the rigidity-flexibility partition in the protein structure. The higher the frequency of a weak spot, the greater the likelihood of the flexible decomposition to begin in these regions. This approach was previously adapted in our analysis of protein kinase flexibility, suggesting dynamic polarization of the kinase domain lobes and conformational plasticity of the N-lobe in oncogenic kinases.110 Here, we explored this method to quantify the global effect of allosteric ligands on the ABL kinase mobility and localization of flexible weak spots (Figure 4). In the ABL complexes with inhibitors, a dominant rigid cluster is noticeably larger and included residues


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from both the N-lobe and the C-lobe region (Figure 4A-D). The departing flexible cluster at the transition point was relatively small and weak spots included primarily mobile residues in the peripheral C-terminal regions (Figure 4A-D). Structural rigidity of the ABL-Asciminib state was evident near the allosteric binding site, at the SH2-KD and the SH3-KD interfacial regions, as well as in the N-lobe (Figure 4A,B). The peripheral regions of the SH2 and SH3 domains showed propensity for only moderate mobility. Consistent with the NMR experiments of the ABL-SH2-SH3 constructs in the complexes with Imatinib and GNF-5 inhibitors99,

we

found that allosteric inhibitors tend to promote the increased rigidity of the N-lobe regions, thereby facilitating stabilization of the closed form. In the ABL complex with DPH activator, the density of weak spots

markedly increased, spreading to the N-terminal lobe regions

(Figure 4E,F). In this case, the distribution of the weak spots featured two large peaks corresponding to the N-terminal and C-terminal regions, where the mobile clusters in the Nlobe included residues from the G-loop, β3-αC loop, β4-β5, and β6-β7 strands (Figure 4E,F). Accordingly, the DPH-induced allosteric dynamic changes in the ABL-KD may reduce energetic barriers for activating mutants and allow for acquisition of a catalytically competent active form of the enzyme. Our results may also help to explain why mutations in the N-terminal regions F311L, K294E and V299L (corresponding to F330L, K313E and V318L in the nomenclature of the crystal structure) can promote kinase activation by incurring resistance to GNF-2 and Asciminib that are located away from the mutational positions.55,56

In the context of our

analysis, these activating kinase mutations may counteract the stabilization effect of allosteric inhibitors by targeting weak spot positions and increasing the mobility of the N-lobe regions, which, in turn, would facilitate conformational transitions to the activated open state.


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Figure 4. The rigidity-flexibility decomposition analysis of the ABL complexes. The frequency of unfolding nuclei (or weak spots) in the ABL-Asciminib complex (A), ABL complex with GNF-2 (C) and ABL-DPH complex (E). The frequencies of

the ABL residues in the ABL-

Asciminib complex (A) are shown for the SH3 domain in blue bars, for the SH2 domain in red bars, for the linker in green bars, and for the KD regions in maroon bars. In (C,E) the frequencies are shown for the N-lobe residues in blue bars and C-lobe in red bars. Structural mapping of the rigidity-flexibility decomposition in the ABL-Asciminib complex (B), ABL complex with GNF-2 (D) and ABL-DPH complex (F). The ABL conformations are colored according to the rigidity-flexibility spectrum using a color range from blue (rigid) to red (flexible). The known functional residues and drug resistant sites are shown in black spheres.


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Perturbation-Based Residue Scanning Allosteric Interactions in the ABL Complexes Using the PRS method, we quantified the allosteric effect of each residue in the protein structures on all other residues in response to external perturbation. We primarily focused on the effector profiles measuring the ability of each residue to influence the dynamic changes in all other residues, where the respective peaks of the distribution

can be considered as potential

allosteric hotspots of the long-range communications (Figure 5). A comparative analysis of these profiles revealed several important differences between the ABL complexes with allosteric modulators. In the ABL-Asciminib complex, we observed a fairly broad distribution of the effector sites, with the most significant hotspot centers located at the SH2-KD and the SH3-KD interfaces, near the allosteric binding site, and also occupying functional sites in the SH2 linker (Figure 5A). Structural mapping of the regulatory and drug resistant sites highlighted a strong correspondence between functional positions and the effector-enriched regions (Figure 5B). Strikingly, the peaks of the effector profile mapped precisely onto known functional sites, such as Y158 of the SH2-KD interface, linker residues P242 and T243, KD residues V318 and T334,

and allosteric pocket residues A356, A363, and P484 (Figure 5A,B). The effector

profile peaks primarily corresponded to the SH2-KD interface positions and residues proximal to the allosteric site, while a lower density of the effector residues was seen near the ATP binding site (residues 300-330) (Figure 5A,B). A similar pattern of the effector profile was detected in the ABL complex with GNF-2 inhibitor (Figure 5C). In this case we also found that the most dominant peaks (residues 359-363, 448456, 380-HRD-382, 400-DFG-402, 482-484) corresponded to the functional sites near the allosteric site and in the regulatory region of the N-lobe. Structural projection of the effector profile highlighted a broader density of allosteric centers near the allosteric site, while a more


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localized and weaker density could be seen in the N-lobe (Figure 5D). These findings supported the notion that the inhibitor binding can strengthen the allosteric effect of the myristoyl-binding site residues that would likely assume the primary control over allosteric signaling in the complex. Interestingly, mutations in the allosteric site positions A337V, P465S (corresponding to A356V, P484S in the crystal structure nomenclature) confer resistance against GNF-2 and Asciminib by dismantling the autoinhibited kinase assembly.56 According to our findings, these sites play a central role in regulating allosteric interactions and signal transmission in the complex. As a result, mutations in these positions may arguably not only weaken direct contacts with the inhibitors, but also compromise efficiency of the long-range coupling with the ATP binding site and other distant regions in the catalytic core. Overall, by quantifying allosteric residue potential in the ABL structures, the PRS analysis identified global mediating centers of allosteric interactions that coincided with many experimentally known regulatory sites. We observed several important differences between the effector profiles of the ABL-KD complexes with the inhibitors and DPH activator (Figure 5E,F). Our analysis revealed a switch in the distribution of the effector sites in the ABL-DPH complex, as the dominant peaks corresponded to residues from the ATP binding site and regulatory regions of the N-lobe, while the influence of the allosteric site on the rest of the protein was reduced (Figure 5E). Structural mapping showed these changes in the allocation of allosteric centers, particularly highlighting the increased density of the effector sites near the regulatory αC-helix and the emerging density near the substrate binding region in the C-lobe (Figure 5F). Hence, by quantifying allosteric potential of each residue in the ABL structures,

PRS analysis identified global mediating

centers of allosteric interactions that often coincided with the experimentally known regulatory positions.


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Figure 5. The PRS effector profiles for the ABL kinase complexes with allosteric modulators. (A) The effector profile of the ABL-SH2-SH3 complex with Asciminib is shown : SH3 domain residues are in blue lines, SH2 domain in red lines, and the SH2 linker in green lines and KD residues in brown lines. The positions of functional sites and Asciminib-resistant residues are indicated by filled maroon squares. (B) Structural map of the PRS effector profile in the ABL-SH2-SH3 complex with Asciminib. The positions of functional residues corresponding to peaks of the effector profile (allosteric hotspots) are shown in black spheres.

(C) The effector

profile of the ABL complex with GNF-2 is shown in brown lines and functional positions are annotated as filled maroon squares. (D) Structural map of the PRS effector profile in the ABL complex with GNF-2. The predicted allosteric hotspots are shown in black spheres. The effector profile of the ABL-DPH complex ( E) and the structural mapping of sensor propensities (F) are annotated in a similar manner. The ABL structures in different complexes (D-F) are colored according to the effector potential with red-to-blue color spectrum reflecting the decreased in the

effector capacity.


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The sensor profile describes the extent of response at residue j to perturbations of all other residues, thus measuring the residue capability of serving as a receiver of allosteric signals. In the ABL complexes with inhibitors, the peaks of sensor profiles corresponded to the flexible Nlobe regions (β3-αC loop and β4-β5 strands) as well as residues the vicinity of the ATP site (Figure 6A-D). Structural projection of the sensor profiles in the ABL complex with GNF-2 highlighted that major sensor sites that absorb allosteric signal are located in the N-lobe (Figure 6D). Hence, allosteric communications in the ABL-inhibitor complexes may be primarily directed from the allosteric binding site while the ATP binding site residues would likely act as receivers of the allosteric signal. In contrast, in the ABL-DPH complex, the sensors of allosteric perturbations are located in both the allosteric and the substrate binding sites (Figure 6E, F).

Based on these findings, we argue that the allosteric network in the ABL complexes with the inhibitors may be larger and feature different communication routes connecting allosteric site with other functional regions. On the other hand, the ABL complex with DPH activator may produce a smaller allosteric network, driven mostly by the effector residues in the ATP binding site.

These results

supported our conjecture that allosteric inhibitors can stabilize the closed

ABL state and strengthen the long-range couplings between the allosteric pocket and the ATP binding site, whereas DPH activator may induce a more dynamic kinase conformation and preferentially activate allosteric couplings between the ATP and substrate binding sites.


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Figure 6. The PRS sensor profiles for the ABL kinase complexes with allosteric modulators. (A) The sensor profile of the ABL-SH2-SH3 complex with Asciminib is shown : SH3 domain residues are in blue lines, SH2 domain in red lines, and the SH2 linker in green lines and KD residues in brown lines. The positions of functional sites and Asciminib-resistant residues are indicated by filled maroon squares. (B) Structural map of the sensor profile in the ABL-SH2SH3 complex with Asciminib. Functional residues corresponding to peaks of the effector profile (allosteric hotspots) are shown in black spheres.

(C) The sensor profile of the ABL complex

with GNF-2 (in brown lines) withy functional positions are annotated as filled maroon squares. (D) Structural map of the PRS sensor profile in the ABL complex with GNF-2. The sensor profile and structural map of sensor propensities for the ABL-DPH complex (E,F).


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Modeling and Analysis of the Residue Interaction Networks in the ABL Complexes: Residue Centrality Profiles Identify Regulatory Sites as Mediators of Allosteric Signaling Using a graph-based representation of protein structures20,21 we constructed the residue interaction networks in which the inter-residue edges were weighted using the residue crosscorrelations obtained from MD simulations22 and coevolutionary residue correlations.80,81 The dynamic averages of several fundamental network properties were computed for the studied systems. To identify key mediating centers of the allosteric interaction networks, we focused our analysis on the residue betweenness profiles (or residue centrality) and edge betweenness distributions (also called edge centrality). The distribution of high centrality sites in the ABL complexes with inhibitors showed that positions of known regulatory residues matched well the profile peaks (Figure 7A). In particular, the key inter-domain residues Y158 and KD residues V318, T334, A363, and P484 corresponded to sharp centrality spikes. A similar distribution was seen in the ABL-KD complex with GNF-2 (Figure 7B), showing that strategic structural position of these high centrality sites is preserved and further enhanced in the regulatory complex (Figure 7A). Interestingly, we found that the high centrality positions in the interaction networks featured high allosteric propensities and aligned with the effector centers of the PRS profiles. In network terms, the emergence of various high centrality sites that are spatially distributed typically implies a broad allosteric network with distinct and efficient communication routes connecting functional regions and binding sites.59-65 Notably, the number of high centrality peaks was markedly reduced in the ABL-DPH complex and protein residues featured only moderate centrality, likely due to the increased flexibility of this open kinase form (Figure 7C).


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Figure 7. Analysis of the network parameters in the ABL complexes. The residue centrality (betweenness) and the residue-based inter-community bridgeness profiles for the ABL-SH2SH3 complex with Asciminib (A,D), ABL complex with GNF-2 (B,E) and ABL-DPH complex (C,D). The profiles are shows in maroon-colored lines and positions of experimentally known functional sites and drug-resistant residues are annotated in (A-C) by filled green squares.

This observation is supportive of our assertion that the allosteric network in the ABL-DPH complex may be smaller and more diffuse, consequently producing an ensemble of suboptimal pathways propagating allosteric signal. The important result of this analysis is that many regulatory sites that dictate dynamic switching between the open and closed inhibitory states were closely aligned with the high centrality residues. This may explain why mutations in these positions are typically associated with a more severe phenotype. In general, our results


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consistently revealed a group of network-mediating centers in the ABL-inhibitor complexes that can control allosteric interactions and signal transmission, suggesting a global role of these sites in regulation of the ABL activity. Intriguingly, however, it did not escape our attention that there were several important regulatory residues from the SH2/SH3 domains and SH2 linker (particularly Y134, V138, S140, Y245, and P249)48 that displayed only an average level of centrality (Figure 7A, B). We assumed that these moderately flexible residues may play a different role in signal transmission,

serving as the inter-modular connectors linking

structurally stable clusters into a global allosteric network. Exploring Modularity of the Residue Interaction Networks: The Edge Centrality and the Inter-Community Bridgeness Disclose Allosteric Mechanism of the Regulatory Switches Using a modification of the Girvan-Newman method,92-94 the residue interaction networks were divided into local modules in which residue nodes are strongly interconnected both dynamic and coevolutionary correlations, whereas residues

through

that belong to different

communities may be only weakly coupled. After community maps were generated for each of the studied system, we used edge betweenness (or edge centrality) and the inter-community bridgeness parameters as a proxy for analysis of allosteric communications. The hierarchy of community organization in the ABL complexes with allosteric modulators was examined to quantify allosteric role of the inter-community links in these structures. In this formulation, the edges that link hubs within communities or interconnect dynamically coupled and proximal communities tend have the higher edge betweenness values. The strength of allosteric coupling and communication transfer between local communities in the protein structure can be measured by the edge betweenness of links that connect different communities. We projected the modular organization of the residue interaction networks onto community maps in which the


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thickness of the links connecting local communities is weighted by the average edge betweenness of the inter-community residue connections. The community analysis of the ABL complex with Asciminib revealed local modules responsible for stabilization of the inter-domain interfaces and regulatory assembly (Figure 8A). The key inter-domain community is formed between SH2 and KD regions (Com1: Y158V358-H394-Y345-V354-A356) . This community rigidifies the SH2-KD interactions and provides an autoinhibitory lock by coupling Y158 of the SH2 domain with the allosteric binding site (residues V354, A356). This inter-domain community is adjacent to another large SH2-KD community (Com4: L360-F516-I521-P484-E524) that engages the αI-helix motif (residues F516, I521, and E524). Together, these two local communities may be responsible for stabilization of the autoinhibitory ABL regulatory complex. The community map highlighted the strength of the information transfer between different communities and pointed to the importance of strong links in the catalytic domain of the regulatory complex (Figure 8D). We found a set of highly connected hub communities in the kinase domain (communities 1, 2, 4, 5 and 6) which constitute the core of the interaction network. By connecting a group of central communities, these links serve as mediating bridges for more peripheral modules (communities 2,7,8) that are less well-connected (Figure 8D). This analysis also revealed a modular network hierarchy where several central modules have the higher number of connections between them than they have with other communities, while the peripheral communities are linked to these hubs. According to our findings, the inter-community connections with the high edge betweenness can point to strong links and communication routes responsible for an allosteric cross-talk in the catalytic core.


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We noticed that several important regulatory positions Y158, K238, P242, Y245, A363 in the ABL complex

are involved in bridging central community 1 (KD) with community 8 (SH2

domain), community 4 (KD) and community 6 (SH3 domain) (Figure 8A,D).

While the

average edge betweenness for links connecting these communities was relatively moderate, the inter-community bridgeness index for these residues was precisely aligned with the sharp peaks of the profile (Figure 7D). The SH2-linker module ( Com6: F91-V244-P242-T243-P131Y134) is linked with the KD core (Com3: H314-L317-A369-Y372-V398) via the high bridgeness residues T243 and Y245 (Figure 8A,D). This cluster of communities links the KD core with the regulatory positions of the SH2 linker (P242, T243, and P249)48 and phosphorylation site Y245. The inter-community bridgeness index for linking residues in the SH2/SH3-KD interfaces was appreciably larger as compared to bridges connecting local communities within the KD core. Strikingly, residues with the high inter-community bridgeness index (Y158, P242, T243, and Y245) that connect the SH2 and SH3 domains with KD regions are known as important regulatory switches of the kinase activity.48 In addition, several other functional sites served as inter-modular links. In particular, we observed that A356 residue links community 1 (Y158-V358-H394-Y345-V354-A356) and community 4 (L360-F516-I521-P484-E524), which enables propagation of structural changes in the αI-helix to the SH2 interface and KD core. An important functional position A363 is involved in linking community 3 (H314-L317-A369-Y372-V398) and community 4 ( L360F516-I521-P484-E524) (Figure 8A,D). These functional sites also serve as regulatory control points as evident from biochemical experiments

showing that mutations A356V, A363P,

P484S, and V487F may unlock the αI-helix interactions and relieve the autoinhibitory constraints to promote a population shift to the ABL activating form.55,56


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Figure 8. Community analysis of the ABL complexes. Structural mapping of local communities in the ABL complexes with Asciminib (A), GNF-2 (B) and DPH (C). The local communities are shown in residue-colored spheres according to the modular identity. The high bridgeness index residues are shown in black spheres and annotated.

The schematic community

maps for these ABL complexes are shown respectively in (D-F). The size of each community node is proportional to the cumulative density of the intra-community residue-residue links. The thickness of the inter-modular bonds is proportional to the average edge betweenness of the inter-community residue links. The community nodes are colored according to the coloring scheme of these clusters in the ABL structures. The communities shown for the ABL complex with Asciminib: Com 1 in orange (Y158-V358-H394-Y345-V354); Com 2 in sand color (W495-W449-M477); Com 3 in blue (H314-L317-A369-Y372-V398); Com 4 in light blue (L360-F516-I521); Com 5 in magenta (R502-E428-P499-K438-V441); Com 6 in salmon (F91V244-P4242-T243-P131-Y134); Com 7 in brown (F302-L292-F330); Com 8 in yellow (P235W146-F168-V205-V218-L184-L232); Com 9 in forest green color (N240-N393-L395).


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The community map in the ABL complex with GNF-2 inhibitor featured a globally distributed and dense map of many stable local modules (Figure 8B,E). As a result, the distribution of the inter-community bridgeness showed a number of peaks that were spread throughout the catalytic core (Figure 7E). These observations further supported the notion that allosteric interaction network in the ABL-inhibitor complexes could be large and produce distinct communication routes connecting binding sites. The community map revealed two distinct groups that could connect the allosteric site with the ATP binding site (Figure 8B). One line of communication from the allosteric site to the ATP site is mediated by C-lobe community 4 (Y345-C349-V354-Y454-V358), while a more direct route can be enabled by linking community 6 (A385-L343-E450) and community 1 (D382-R386-Y412-P421-L403-M407W424). These modules included several key functional residues such as D382 of the 380HRD-382 catalytic motif and phosphorylation site Y412 in the activation loop (Figure 8B). The high bridgeness sites V354 and Y454 connect community 3 (I521-L360-F516-P484-V487) and community 4 (Y345-C349-V354-Y454-V358). These modules may define one possible route of allosteric communication connecting the allosteric binding site and the ATP binding site. In the ABL-DPH complex, the distribution of the inter-modular bridges s displayed a shift towards the inter-lobe regions that are important for linking the ATP and substrate binding sites (Figure 7F).

We found that activator binding may promote global network changes, featuring

the weaker density of local communities which a reflection of a more dynamic kinase conformation (Figure 8C, F). A large community formed around DPH activator (Com2: F512F444-F505-V441-I508-L448) is linked to the key community in the catalytic core (Com1: D382-R386-Y412-P421-L403-M407-W424).

The local

community 1 (D382-R386-Y412-

P421-L403-M407-W424) and community 5 (L317-A369-V398-C388-V396-I366-H380-L383)


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couple

380-HRD-382 and 400-DFG-402 motifs with the substrate binding region (Com3:

W424-V446-P458-W442-W495) (Figure 8C,F). The inter-community bridges also overlapped with residues

L448, A384, D382 (HRD motif) and F401 (DFG motif). Due to a more sparse

community organization in the ABL-DPH complex,

the inter-modular bridge positions could

provide the essential links for propagation of the allosteric signal in the catalytic core between the ATP binding site and substrate binding region in the C-lobe (Figure 8C, F). To summarize, community analysis of the residue interaction networks revealed the hierarchical organization and provided evidence of strong intra- and inter-community links in the catalytic core enabling the effective signaling between binding sites.

We also found that the inter-

community bridgeness metric can characterize the unique connections in sparse regions that can be responsible for structural stability of the ABL regulatory complexes. In network terms, according to the ‘weak-strong tie’ hypothesis111-113 the deletion of weak links, which may be the only bridges connecting functional modules, could lead to disintegration of the network and loss of function, while the removal of a strong link may only weaken the inter-community communication without severely compromising the integrity of the global network. The central conclusion from our analysis is that the allosteric switch points in the ABL

structures play a

critical role in the modular network organization, serving as ‘weak’ inter-modular links connecting the SH2 and SH3 domains with the inter-domain linker and catalytic KD core into a fine-tuned functional complex. Our

findings of the sparser community maps featured in the

ABL-DPH complex are also consistent with the notion that ‘weak’ inter-modular ties may be particularly important in the progressively ‘island-like’ network structures, where these bridges become indispensable for establishing connections between different communities.113


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Modeling Allosteric Communication Pathways as Inter-Community Hopping Mediated by High Bridgeness Centrality Sites A communication pathway in this model could be viewed as migration between strongly interacting residues within a local community followed by the inter-community hopping event81 from a community leader featuring the highest bridgeness centrality index. By evaluating communication propensities of protein residues and

bridgeness of the community

leaders, we identified candidate residues for the inter-community hopping. We used community decomposition and detection of high centrality bridges to probe the ensembles of communication pathways in the ABL complexes. Using a community-hopping model, 81 we generated for each structure ~500 different paths originated from functional residues in different communities proximal to the allosteric binding site and ATP binding site (Figure S2, Supporting Information). The optimal pathways with the shortest path length were selected and clustered to obtain the most preferable routes connecting the binding sites. In the ABL-Asciminib complex, we observed the emergence of several dominant allosteric communication pathways that are mediated by key communities in the KD, SH2 domain and the SH2-SH2 linker (Figure S2, Supporting Information). The spatial organization of these pathways reflected a large allosteric network that favors several distinct ‘narrow tubes’ of paths

connecting the allosteric binding

site and ATP binding site. One of the dominant pathway tubes proceeds directly from the allosteric site to the SH2 linker via communities 1 and 4 (Figure 7B). Upon connecting to the SH2-linker community (Com6: F91-V244-P242-T243-P131-Y134) optimal pathways can reach the KD core (Com3: H314-L317-A369-Y372-V398) via high bridgeness residues T243 and Y245. This direct path may exploit network efficiency in propagating this signal through interactions between groups of rigid residues.


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In some contrast, in the ABL complex with DPH activator, we observed preferences for a wider ensemble of communication pathways connecting the allosteric binding site with the ATP pocket through dense kinase core. Through disengagement of the inhibitory lock with the SH2 domain, allosteric activator DPH switches the preferential pathways and leverages C-spine residues (C388, L389, and I451) for efficient communication of the allosteric site with the ATP site. Moreover, the obtained ensemble revealed a favorable route connecting the ATP binding site with the substrate binding region in the C-lobe. These routes are characteristic of the ABLDPH complex and were not observed in the ABL-inhibitor structures. Interestingly, DPH binding seemed to promote communication paths connecting the ATP and substrate binding site by engaging the R-spine residues H380, F401 and D440 (Figure S2, Supporting Information). The assembly and stabilization of the R-spine subnetwork is a recognized signature of kinase activation. Hence, our observations may indicate that DPH binding could prime the catalytic domain for acquisition of a catalytically active form. Hence, spatial organization of local communities and inter-modular bridges in the ABL-inhibitor complexes suggested a diverse allosteric network with several distinct and efficient communication paths connecting the binding sites. The long-range communications between the allosteric and ATP binding sites in the inhibitory state can critically depend on structural integrity of these high centrality mediating sites. In some contrast, allosteric activator DPH may induce a more dynamic kinase state and partly rewire modular organization of the interaction communities, leading a smaller network that mediates an ensemble of suboptimal routes between the ATP and substrate binding sites. To summarize the central findings and potential implications of our multi-faceted analysis, we discuss several key aspects of this approach that may be helpful in the development of more effective strategies to quantify allosteric signaling mechanisms. Central to our network approach


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is the construction and the hierarchical modular decomposition of the residue interaction networks, where local functional communities are formed by residues that are strongly linked through both dynamic and coevolutionary couplings that can be related to specific modular functions. We expanded our recent work by assuming that dynamic and coevolutionary residue correlations may act as synchronizing forces to enable efficient allosteric regulation. The role of coevolutionary couplings in strengthening local interacting modules was noticed in a different context by Baker and colleagues114 showing that directly coevolving residue pairs are typically spatially proximal. Consistent with our latest studies81, we found that integration of dynamic residue correlations and coevolutionary couplings

into a generalized correlation metric can

adequately describe the hierarchical modularity of allosteric interaction networks.81 The hierarchy of network and community organization in the ABL complexes with allosteric modulators was examined to quantify allosteric role of strong and weak inter-community links in these structures. By interpreting the computed network profiles of the ABL structures via a ‘weak-strong tie’ hypothesis111-113 a plausible rationale is presented that connects phenotypic significance of the regulatory control points to their

mediating network properties and the

inter-modular bridging role in the allosteric interaction network. It was shown that stronger ties in various networks may restrict signal transmission between communities by confining the information transfer among nodes.111 Our results offered a more balanced view on the role of weak and strong links in the protein network structures which may be important for the efficiency of allosteric signaling .115-117 The hierarchy of community organization in the ABL kinase may allow for synergistic roles of strong inter-community links in the catalytic core enabling efficient signal transmission between the binding sites and global network bridges acting as switches that coordinate allosteric transitions and modulate the kinase activity.


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Conclusions In this work, we performed atomistic modeling of the ABL regulation by allosteric inhibitors and activators using a multi-faceted computational approach that combined MD simulations, structural residue perturbation scanning and novel

network-centric community analysis of

protein structures. Using the developed network-centric approach, we provided evidence for hierarchical modularity of the residue interaction networks in ABL structures, showing that local communities may serve as functional building blocks that mediate allosteric changes. By employing

a novel community analysis and various global network metrics of allosteric

efficiency, we have shown that regulatory switches in the ABL structures may serve as unique inter-modular links connecting SH2 and SH3 domains with the linker and KD core. Using this theoretical framework, we have examined various regulatory scenarios that may be exploited by the allosteric ligands to modulate a cross-talk between the binding sites and functional regions. The proposed methodology has allowed for robust identification of allosteric hotspots and critical interactions responsible for regulation of the ABL kinase activity. Our results have also indicated that allosteric inhibitors and activators may exert differential control on allosteric signaling between binding sites. While and induce allosteric communications

inhibitor binding

can strengthen the inhibitory state

directed from the allosteric pocket to the ATP binding

site, DPH activator may induce a more dynamic kinase state and enhance allosteric couplings between the ATP and substrate binding sites. The emerging realization that allosteric inhibitors and activators can exploit distinct regulatory mechanisms and differentially modulate protein activities could open up new venues for probing signaling processes. Exploiting system-based relationships between protein dynamics

and allosteric

mechanisms can be central

for

advancing robust quantitative models of protein function and regulation at the molecular level.


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SUPPORTING INFORMATION Supporting information contains analysis of functional dynamics and correlated motions in the crystal structures of the ABL kinase complexes and is presented in Figure S1. The structural maps of allosteric communication pathways connecting the allosteric binding site with other regulatory regions are presented in Figure S2 for the ABL-SH2-SH3 complex with Asciminib and ABL-DPH complex. This material is available free of charge via the Internet at http://pubs.acs.org.

AUTHOR INFORMATION * Corresponding Author Phone: 714-516-4586 Fax: 714-532-6048 E-mail: [email protected] The authors declare no competing financial interest. Acknowledgment This work was partly supported by institutional funding from Chapman University. ABBREVIATIONS SH3, Src homology 3; SH2, Src homology 2; KD, kinase domain.


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Atomistic modeling of the ABL kinase regulation by allosteric modulators using structural perturbation analysis and community-based network reconstruction of allosteric communications

Lindy Astl, Gennady M. Verkhivker

For Table of Contents Use Only


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Figure 1. Structural organization of the ABL complexes with allosteric modulators. (A) The structure of the ABL-SH2-SH3 complex with Nilotinib and Asciminib. The core domains are shown : SH3: Src homology domain 3 ( in magenta), SH2: Src homology domain 2 ( in orange), SH2-kinase linker (SH2 Linker) ( in cyan), and kinase domain (KD) (in red). The inhibitors are shown in sticks with atom-based coloring. (B) A close-up of the allosteric binding site shows bending of the αI-helix in the closed inhibitory form of ABL. (C) The structure of the ABL-KD complex with Imatinib and GNF-2 inhibitors that are shown in sticks. (D) A close-up of the allosteric site highlights bending of the αI-helix induced by GNF-2 binding. (E) The structure of the ABL-KD complex with Imatinib and allosteric DPH activator. (F) A close-up of binding of DPH to the myristate pocket shows unbending of the αI-helix that leads to disruption of the autoinhibitory constraints and activation of ABL kinase. 177x100mm (300 x 300 DPI)


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Figure 2. Conformational dynamics of the ABL kinase complexes with allosteric modulators. (A) The computed B-factors for the KD residues obtained from MD simulations of the ABL-Asciminib complex (in blue lines), ABL complex with GNF-2 (in red lines) and ABL-DPH complex (in green lines). (B) The dynamics profile of the ABL-SH2-SH3 complex with Asciminib. The computed B-factors for the SH3 domain residues are in blue lines, SH2 domain in red lines, and the SH2 linker in green lines and KD residues in maroon lines. (C) The cross-correlation matrices of residue fluctuations along the 10 low frequency modes are obtained from PCA computations of the ABL complex with GNF-2. (D) The cross-correlation heat maps for the ABLDPH complex. PCA computations based on the backbone heavy atoms (N, Cα, Cβ, C, and O) and Cα atoms resulted in similar cross-correlation heat-maps. For simplicity, we present the results of PCA computations using Cα atoms. The axes denote Cα atoms of the protein residues in sequential order, so that each cell in the plot shows the correlation of two residues in the protein. Cross-correlations of residue fluctuations vary between +1 (fully correlated motion, colored in green) and -1 (fully anti-correlated motions, colored in red). 177x100mm (300 x 300 DPI)



Figure 3. The distance fluctuation profiles for the ABL kinase complexes with allosteric modulators. (A) the residue-based distance fluctuation distribution for the ABL-SH2-SH3 complex with Asciminib is shown : SH3 domain residues are in blue lines, SH2 domain in red lines, and the SH2 linker in green lines and KD residues in maroon lines. (B) The distance fluctuation profiles for the KD residues obtained from MD simulations of the ABL-Asciminib complex (in blue lines), ABL complex with GNF-2 (in red lines) and ABLDPH complex (in green lines). (C) Structural mapping of the distribution peaks (high allosteric propensities) for the ABL-Asciminib complex. The putative allosteric sites with high distance fluctuation index are shown in red spheres. Structural projection of predicted allosteric residues for ABL complex with GNF-2 (D) and ABL-DPH complex (E). The bound inhibitors are shown in atom-based color coded sticks. 177x100mm (300 x 300 DPI)


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Figure 4. The rigidity-flexibility decomposition analysis of the ABL complexes. The frequency of unfolding nuclei (or weak spots) in the ABL-Asciminib complex (A), ABL complex with GNF-2 (C) and ABL-DPH complex (E). The frequencies of the ABL residues in the ABL-Asciminib complex (A) are shown for the SH3 domain in blue bars, for the SH2 domain in red bars, for the linker in green bars, and for the KD regions in maroon bars. In (C,E) the frequencies are shown for the N-lobe residues in blue bars and C-lobe in red bars. Structural mapping of the rigidity-flexibility decomposition in the ABL-Asciminib complex (B), ABL complex with GNF-2 (D) and ABL-DPH complex (F). The ABL conformations are colored according to the rigidity-flexibility spectrum using a color range from blue (rigid) to red (flexible). The known functional residues and drug resistant sites are shown in black spheres.

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Figure 5. The PRS effector profiles for the ABL kinase complexes with allosteric modulators. (A) The effector profile of the ABL-SH2-SH3 complex with Asciminib is shown : SH3 domain residues are in blue lines, SH2 domain in red lines, and the SH2 linker in green lines and KD residues in brown lines. The positions of functional sites and Asciminib-resistant residues are indicated by filled maroon squares. (B) Structural map of the PRS effector profile in the ABL-SH2-SH3 complex with Asciminib. The positions of functional residues corresponding to peaks of the effector profile (allosteric hotspots) are shown in black spheres. (C) The effector profile of the ABL complex with GNF-2 is shown in brown lines and functional positions are annotated as filled maroon squares. (D) Structural map of the PRS effector profile in the ABL complex with GNF-2. The predicted allosteric hotspots are shown in black spheres. The effector profile of the ABL-DPH complex ( E) and the structural mapping of sensor propensities (F) are annotated in a similar manner. The ABL structures in different complexes (D-F) are colored according to the effector potential with red-to-blue color spectrum reflecting the decreased in the effector capacity. 177x100mm (300 x 300 DPI)


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Figure 6. The PRS sensor profiles for the ABL kinase complexes with allosteric modulators. (A) The sensor profile of the ABL-SH2-SH3 complex with Asciminib is shown : SH3 domain residues are in blue lines, SH2 domain in red lines, and the SH2 linker in green lines and KD residues in brown lines. The positions of functional sites and Asciminib-resistant residues are indicated by filled maroon squares. (B) Structural map of the sensor profile in the ABL-SH2-SH3 complex with Asciminib. Functional residues corresponding to peaks of the effector profile (allosteric hotspots) are shown in black spheres. (C) The sensor profile of the ABL complex with GNF-2 (in brown lines) withy functional positions are annotated as filled maroon squares. (D) Structural map of the PRS sensor profile in the ABL complex with GNF-2. The sensor profile and structural map of sensor propensities for the ABL-DPH complex (E,F).

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Figure 7. Analysis of the network parameters in the ABL complexes. The residue centrality (betweenness) and the residue-based inter-community bridgeness profiles for the ABL-SH2-SH3 complex with Asciminib (A,D), ABL complex with GNF-2 (B,E) and ABL-DPH complex (C,D). The profiles are shows in marooncolored lines and positions of experimentally known functional sites and drug-resistant residues are annotated in (A-C) by filled green squares. 177x100mm (300 x 300 DPI)


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Figure 8. Community analysis of the ABL complexes. Structural mapping of local communities in the ABL complexes with Asciminib (A), GNF-2 (B) and DPH (C). The local communities are shown in residuecolored spheres according to the modular identity. The high bridgeness index residues are shown in black spheres and annotated. The schematic community maps for these ABL complexes are shown respectively in (D-F). The size of each community node is proportional to the cumulative density of the intra-community residue-residue links. The thickness of the inter-modular bonds is proportional to the average edge betweenness of the inter-community residue links. The community nodes are colored according to the coloring scheme of these clusters in the ABL structures. The communities shown for the ABL complex with Asciminib: Com 1 in orange (Y158-V358-H394-Y345-V354); Com 2 in sand color (W495-W449-M477); Com 3 in blue (H314-L317-A369-Y372-V398); Com 4 in light blue (L360-F516-I521); Com 5 in magenta (R502E428-P499-K438-V441); Com 6 in salmon (F91-V244-P4242-T243-P131-Y134); Com 7 in brown (F302L292-F330); Com 8 in yellow (P235-W146-F168-V205-V218-L184-L232); Com 9 in forest green color (N240-N393-L395). 177x99mm (300 x 300 DPI)



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