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• W4313651000 abstract "Article Figures and data Abstract Editor's evaluation Introduction Results Discussion Materials and methods Data availability References Decision letter Author response Article and author information Metrics Abstract Inactive conformations of protein kinase catalytic domains where the DFG motif has a “DFG-out” orientation and the activation loop is folded present a druggable binding pocket that is targeted by FDA-approved ‘type-II inhibitors’ in the treatment of cancers. Tyrosine kinases (TKs) typically show strong binding affinity with a wide spectrum of type-II inhibitors while serine/threonine kinases (STKs) usually bind more weakly which we suggest here is due to differences in the folded to extended conformational equilibrium of the activation loop between TKs vs. STKs. To investigate this, we use sequence covariation analysis with a Potts Hamiltonian statistical energy model to guide absolute binding free-energy molecular dynamics simulations of 74 protein-ligand complexes. Using the calculated binding free energies together with experimental values, we estimated free-energy costs for the large-scale (~17–20 Å) conformational change of the activation loop by an indirect approach, circumventing the very challenging problem of simulating the conformational change directly. We also used the Potts statistical potential to thread large sequence ensembles over active and inactive kinase states. The structure-based and sequence-based analyses are consistent; together they suggest TKs evolved to have free-energy penalties for the classical ‘folded activation loop’ DFG-out conformation relative to the active conformation, that is, on average, 4–6 kcal/mol smaller than the corresponding values for STKs. Potts statistical energy analysis suggests a molecular basis for this observation, wherein the activation loops of TKs are more weakly ‘anchored’ against the catalytic loop motif in the active conformation and form more stable substrate-mimicking interactions in the inactive conformation. These results provide insights into the molecular basis for the divergent functional properties of TKs and STKs, and have pharmacological implications for the target selectivity of type-II inhibitors. Editor's evaluation This important paper provides a convincing mechanism for the relative binding specificity of Type II inhibitors to kinases. The combination of a sequence-derived Potts model with experimental dissociation constants and calculated free energies of binding to the DFG-out state is highly compelling and goes beyond the current state-of-the-art. Given the importance of kinases in pathophysiological processes, the results will be of interest to a broad audience and, in addition, the combination of computational methods can be applicable to a wide variety of other biophysical processes that involve conformational rearrangements. https://doi.org/10.7554/eLife.83368.sa0 Decision letter Reviews on Sciety eLife's review process Introduction The human genome contains approximately 500 eukaryotic protein kinases which coordinate signaling networks in cells by catalyzing the transfer of a phosphate group from ATP to serine, threonine, or tyrosine residues (Manning, 1995; Modi and Dunbrack, 2019a). The GO (gene ontology) database identifies 351 (~70%) of these enzymes as serine/threonine kinases (STKs) and 90 (~18%) as tyrosine kinases (TKs). STKs are an ancient class of protein kinases that predate the divergence of the three domains of life (bacteria, archaea, eukaryote) (Stancik et al., 2018), whereas TKs are a more recent evolutionary innovation, having diverged from STKs about 600 million years ago during early metazoan evolution (Miller, 2012; Sebé-Pedrós et al., 2016). Kinases are important therapeutic targets in a large number of human pathologies and cancers. Both TKs and STKs share a striking degree of structural similarity in their catalytic domains, owing to evolutionary selective pressures that preserve their catalytic function; in particular, the location and structure of the ATP binding site are highly conserved which raises significant challenges for the design of small-molecule ATP-competitive inhibitors that are both potent for their intended target(s) and have low off-target activity for unintended kinase targets. The latter is referred to as the ‘selectivity’ of an inhibitor, a property which is difficult to predict and control but is nonetheless very important for developing drugs with minimal harmful side effects. A particular class of ATP-competitive kinase inhibitors which were proposed to have a high potential for selectivity are called ‘type-II inhibitors’ which only bind when the kinase adopts an inactive ‘DFG-out’ conformation. ‘DFG’ (Asp-Phe-Gly) refers to a conserved catalytic motif located at the N-terminus of an ~20 residue-long ‘activation loop’ that is highly flexible and controls the activation state of the kinase and the structure of the substrate binding surface. The precise arrangement of catalytic residues and the structural organization of large regulatory elements, such as the activation loop and nearby ‘αC-helix’, are strongly coupled to the conformation of the DFG motif and the DFG-1 residue preceding it, which is well described by regions on the Ramachandran map occupied by the Asp, Phe, and DFG-1 residues (beta-turn, right-handed alpha-helix, left-handed alpha-helix) and the χ1 rotamer state of the DFG-Phe sidechain (trans, gauche-minus, gauche-plus). Recently, Dunbrack and co-workers identified eight major conformational states in the Protein Data Bank (PDB) based on these metrics (Modi and Dunbrack, 2019b). The most common state, which is evolutionarily conserved in all kinases, corresponds to the active ‘DFG-in’ conformation. In this conformation all structural requirements for catalysis are typically met, e.g., a complete hydrophobic spine, a salt bridge between the conserved β3-Lys and αC-Glu residues, and an extended activation loop which forms the substrate binding surface. Inactive kinases in the PDB are most frequently seen in an ‘Src-like inactive’ conformation where the DFG is ‘in’, but the αC-helix is swung outward, breaking the β3-Lys → αC-Glu salt bridge and disassembling the hydrophobic spine. Disassembly of the hydrophobic spine caused by αC-helix rotation increases the cavity volume around the DFG-Phe residue, allowing it to pass through the Src-like inactive conformation and completely ‘flip’ from DFG-in to DFG-out (Levinson et al., 2006; Shan et al., 2013). The classical DFG-out conformation, targeted by type-II inhibitors, displays a highly reorganized activation loop that is folded away from the αC-helix, projecting toward solvent or forming stable secondary structure and substrate-mimicking interactions. We refer to these states of the activation loop collectively as ‘folded’, to describe its ~17 Å reorganization relative to the active ‘extended’ conformation, wherein the substrate binding surface has been ‘folded up’ toward the kinase N-terminal lobe and away from the αC-helix. In both TKs and STKs, the activation loop undergoes this large-scale conformational change when the DFG motif flips from the active ‘DFG-in’ conformation to the classical DFG-out conformation. The DFG flip swaps the positions of DFG-Phe and DFG-Asp, opening a hydrophobic ‘back pocket’ that is connected to the conserved ATP binding site through the ‘gatekeeper’ residue. Type-II inhibitors typically have a long chemical fragment that allows them to bind across the gatekeeper and form interactions with residues in the back pocket. In contrast, type-I inhibitors (the majority of kinase drugs) occupy the ATP pocket but not the back pocket and can bind to either DFG-in or DFG-out. For these reasons, it has been proposed that type-II inhibition holds greater potential for the design of highly selective drugs (Vijayan et al., 2015; Davis et al., 2011; Anastassiadis et al., 2011); it has been shown that different kinase sequences have different propensities to adopt DFG-out in the absence of inhibitor (Haldane et al., 2016; Hari et al., 2013), and the DFG-out back pocket has been suggested to have a lesser degree of sequence and structural homology between kinases (Liu and Gray, 2006). However, the notion that type-II inhibitors developed to-date are more selective than type-I inhibitors has been brought into question (Zhao et al., 2014; Klaeger et al., 2017), suggesting that further consideration of the energetic contributions described above is required. In order to fully exploit the target-selective potential of type-II inhibitors it is necessary to understand the underlying sequence-dependent principles that control the conformational preferences of their kinase targets, and the extent to which this has been diversified by evolution. This can, in principle, be directly approached using free-energy simulations to estimate the reorganization free-energy required for different kinases to adopt DFG-out, although this is computationally very expensive and of uncertain reliability for conformational changes involving large-scale loop reorganizations, such as the ~17 Å ‘folding’ of the activation loop that accompanies the transition from active DFG-in to the inactive, classical DFG-out state. To accommodate this limitation, we employ modern sequence-based computational methods to characterize the conformational selection process over the entire kinome and combine the sequence-based results with structure-based free energy simulations with the goal of identifying evolutionarily divergent features of the energy landscape that control the preference of individual kinases for the active (DFG-in) vs inactive (DFG-out ‘folded activation loop’) states. To this end, we report evidence that TK catalytic domains have a molecular evolutionary bias that shifts their conformational equilibrium toward the inactive ‘folded activation loop’ DFG-out state in the absence of activation signals. In contrast, STKs as a class have a more stable active conformation which is favored over the DFG-out state due to sequence constraints in the absence of other signals. As described below, our analysis of a previously published kinome-wide assay suggests that TKs have properties which privilege the binding of type-II inhibitors in comparison to STKs, which leads us to hypothesize an evolutionary divergence in their conformational energy landscapes. To investigate this, we used a Potts Hamiltonian statistical energy model derived from residue-residue covariation in a multiple sequence alignment (MSA) of protein kinase sequences to probe the active DFG-in ↔ classical DFG-out conformational equilibrium as previously described (Haldane et al., 2016). Using an approach that involves ‘threading’ a large number of kinase sequences onto ensembles of active DFG-in and classical DFG-out structures from the PDB and scoring them using the Potts Hamiltonian, we are able to view the evolutionary divergence in TK and STK conformational landscapes. This calculation only probes the free-energy difference between the active DFG-in and classical DFG-out conformations, and by construction does not consider alternative conformations (e.g. ‘Src-like inactive’) that might be important for analyzing the type-II binding pathway. As discussed below, the Potts calculations from this two-state model correlate well with the free-energy cost to adopt the classical DFG-out conformation. To validate our results, we used the Potts statistical energy threading calculations to guide target selection for a set of more computationally intensive free-energy simulations. These simulations use type-II inhibitors as tools to probe kinase targets that have already reorganized to DFG-out, allowing us to estimate the free-energy of reorganization (ΔGreorg) as the excess between the absolute binding free-energy (ABFE) calculated from simulations and the standard binding free-energy measured experimentally in vitro, which already includes the cost to reorganize. Although our methods avoid sampling the conformational change directly, we show how important structural determinants of the conformational change can be identified by analyzing residue-pair contributions to the Potts threading calculations, enabling us to reason about the molecular evolutionary basis for the differences in conformational behavior observed for TKs and STKs. Results Insights into the sequence-dependent conformational free-energy landscape The binding of type-II inhibitors is achieved once a protein kinase has reorganized to the DFG-out with activation loop folded conformation (classical DFG-out). We sought initial insight into the conformational equilibrium from type-II binding data available publicly in the form of literature-reported dissociation constants (Kd). From the binding assay reported by Davis et al., 2011, we report a ‘hit’ where an inhibitor binds to a kinase with Kd ≤10 μM. Using this criterion, a type-II inhibitor hit rate was calculated for each kinase. Analysis of the type-II hit rate distributions for STKs and TKs from the Davis assay (Figure 1A) indicates that STKs, on average, have an unfavorable contribution to the binding of type-II inhibitors relative to TKs. Figure 1 with 3 supplements see all Download asset Open asset Viewing the conformational landscape of the human kinome. (A) Hit rate distributions from kinome-wide experimental binding assays with type-II inhibitors for human serine/threonine kinases (STKs; blue, top) and tyrosine kinases (TKs; orange, bottom) with small gatekeepers (solid bars; sidechain volume <110 Å3) and large gatekeepers (hatched bars; sidechain volume >110 Å3). (B) PyMol (pymol, 2015) visualization of two conformational ensembles populated by Abl kinase from recent solution NMR (Nuclear Magnetic Resonance) experiments (Xie et al., 2020). The active DFG-in conformation where the activation loop is ‘extended’ (red, the Protein Data Bank [PDB]: 6XR6) and the inactive classical DFG-out conformation where the activation loop is ‘folded’ (blue, PDB: 6XRG) both exist in the absence of ligands, but there is a free-energy cost to transition between them (Xie et al., 2020). Type-II inhibitors preferentially bind to this folded DFG-out state. (C) Correlation between Potts DFG-out penalty (ΔE Potts) and hit rates for kinases with small gatekeepers only, to control for gatekeeper effects (Pearson correlation of –0.59, p<0.001). (D) Potts DFG-out penalties calculated for the human kinome and plotted using CORAL (Metz et al., 2018); the TK branch appears to have lower penalties relative to the rest of the kinome, which represent STKs. See Figure 1—source data 1 for values of the calculated type-II hit rates and Potts threaded-energy scores over the human kinome. Figure 1—source data 1 Type-II inhibitor hit rates and Potts threaded-energy penalties for DFG-out calculated for tyrosine kinases and serine/threonine kinases from the human kinome. https://cdn.elifesciences.org/articles/83368/elife-83368-fig1-data1-v2.csv Download elife-83368-fig1-data1-v2.csv The size of the gatekeeper residue is important for type-II binding as it controls access to a hydrophobic pocket adjacent to the ATP binding site that is traversed by type-II inhibitors (Zuccotto et al., 2010; Bosc et al., 2015; Liu et al., 1998; Ghose et al., 2008; van Linden et al., 2014), and the size of the gatekeeper residue is thought to negatively affect type-II binding (Zuccotto et al., 2010; Azam et al., 2008; Lovera et al., 2015; Yun et al., 2008). Because TKs tend to have small gatekeepers in comparison to STKs (Zuccotto et al., 2010; Taylor and Kornev, 2011), we considered this as a possible explanation behind the bias for TKs to have larger type-II hit rates. By plotting the hit rate distributions for STKs and TKs where the gatekeeper is either small or large (Figure 1A) we confirm that gatekeeper size has an important influence on type-II binding for both STKs and TKs (Azam et al., 2008). However, the hit rate distribution for TKs appears more sensitive to gatekeeper size than STKs. Even with small gatekeepers, there is a significant fraction of STKs that have hit rates of zero compared with TKs, suggesting the difference in hit rates between TKs and STKs cannot be accounted for primarily by the size of the gatekeeper residue. Recent solution NMR experiments with Abl kinase revealed two DFG-out conformational states (Xie et al., 2020) one where the DFG motif has flipped ‘DFG-in to DFG-out’ but the activation loop remains in a ‘minimally perturbed’ active-like conformation, and the other state is a classical ‘folded’ DFG-out conformation where the activation loop has moved ~17 Å away from the active conformation (Figure 1B), and the DFG motif is in a ‘classical DFG-out’ (Vijayan et al., 2015) or ‘BBAminus’ (Modi and Dunbrack, 2019b) state. Type-II inhibitors were shown to preferentially bind to this folded DFG-out state, confirming observations that Abl is almost always co-crystallized with type-II inhibitors in this conformation. This binding behavior is also exhibited by other kinases, suggested by the large number of activation loop folded DFG-out states seen in type-II bound co-crystal structures (Figure 1—figure supplement 1). Hence, the importance of large-scale activation loop conformational changes in type-II binding and the large number of residue-residue contact changes involved in this transition (Figure 1—figure supplement 2) suggests the sequence variation of the activation loop and the catalytic loop with which it interacts, might contour the conformational landscape differently for TKs compared with STKs. To investigate this, we used a Potts statistical energy model of sequence covariation to estimate the energetic cost of the active DFG-in (activation loop extended) → inactive DFG-out (activation loop folded) transition for human TKs and STKs (see Methods). Patterns of coevolution of amino acids at different positions in an MSA are thought to largely reflect fitness constraints for fold stability and function between residues close in 3D space (Lapedes et al., 2012; Hopf et al., 2015; Hopf et al., 2017; Morcos et al., 2014), and these coevolutionary interactions can be successfully modeled by a Potts Hamiltonian (Weigt et al., 2009; Lunt et al., 2010) which we inferred using Mi3-GPU, an algorithm designed to solve ‘Inverse Ising’ problems for protein sequences with high accuracy (Haldane and Levy, 2021). The pairwise interactions from the Potts model can be used as a simple threaded energy function to estimate energetic differences between two conformations, based on changes in residue-residue contacts in the PDB (Haldane et al., 2016). We have calculated the threading penalty for all kinases in the human kinome. Our calculations show the Potts predicted DFG-out penalty (ΔEPotts), which is dominated by large-scale reorganization of the activation loop to the folded DFG-out state, is correlated with type-II hit rates (Figure 1C) when controlling for gatekeeper size. From this, we determine that sequence variation of the activation loop and the contacts broken/formed by its large-scale conformational change (Figure 1—figure supplement 2) makes an important contribution to the binding affinity of type-II inhibitors. Notably, our calculations over the entire human kinome show that the large majority of kinases with large ΔEPotts (unfavorable conformational penalties) are STKs, and the large majority of low-penalty kinases are TKs (Figure 1D). To validate this finding, we next perform an independent and more computationally intensive prediction of the conformational reorganization energy of TKs and STKs for select kinase targets, chosen based on the kinome calculations of ΔEPotts and type-II hit rates shown in Figure 1, in which we use type-II inhibitors as probes in ABFE simulations as described in the following section. By comparing the conformational penalties predicted from these structure-based molecular dynamics (MD) free-energy simulations with the Potts conformational penalty scores, we also identify the scale of ΔEPotts in physical free-energy units. This allows us to predict physical conformational free energies based on Potts calculations which can be carried out at scale on large numbers of sequences, to evaluate the evolutionary divergence of the conformational penalty between STKs and TKs generally. Structure-based free-energy simulations guided by the sequence-based Potts model Relative binding free-energy simulations are now widely employed to screen potent inhibitors in large-scale drug discovery studies (Wang et al., 2015). These methods are used to determine the relative free energy of binding between ligands that differ by small substitutions, which permit one to simulate along an alchemical pathway that mutates one ligand to another. By leaving the common core scaffold unperturbed, the cost and difficulty of sampling the transition between unbound (apo) and bound (holo) states of the system are avoided (Wang et al., 2015; Hayes et al., 2022; Guest et al., 2022). Alternatively, alchemical methods to determine ABFEs, such as the ‘double decoupling’ method employed in this work, sample the apo → holo transition along a pathway that decouples the entire ligand from its environment. While more computationally expensive, the advantage of ABFE is that the computed ΔGbind can be directly compared with experimental binding affinities, and successful convergence does not rely on the structural similarity of compounds being simulated (Cournia et al., 2020; Li et al., 2020; Heinzelmann and Gilson, 2021; Lee et al., 2020; Gilson et al., 1997; Qian et al., 2019; Sun et al., 2022). Our alchemical ABFE simulations of type-II inhibitors binding to TKs and STKs simulate the apo and holo states of the kinase domains in the classical DFG-out conformation with the activation loop folded, starting from the experimentally determined co-crystal structure of the holo state. The apo state remains DFG-out with the activation loop folded throughout the simulations, and therefore the calculated ABFE (ΔGbindABFE) excludes the cost to reorganize from DFG-in (ΔGreorg). On the other hand, standard binding free-energies (ΔGexpo) determined experimentally from inhibition or dissociation constants (Equation 1) implicitly include the free-energy cost to reorganize. Therefore given the experimentally determined total binding free energy, ΔGexpo , ABFE simulations can be used to separate the free energy of ligand-receptor association in the inactive state (ΔGbindABFE) from the cost to reorganize from the active to inactive state, ΔGreorg (Equation 3; Deng et al., 2011; Lin et al., 2014; Lin et al., 2013). We calculated ΔGexpo (Equation 1) from literature reported IC50 or Kd values, where the standard concentration C0 is set to 1 M (1) ΔGexpo=kbT lnKd/C0 ΔGexpo can be expressed as the sum of the free-energy change to reorganize from the active to inactive state, ΔGreorg plus the free energy to bind to the inactive state ΔGbindABFE (Equation 2). ΔGreorg is therefore the excess free-energy difference between ΔGexpo and ΔGbindABFE (Equation 3). (2) ΔGexpo= ΔGreorg+ΔGbindABFE (3) ΔGreorg=ΔGexpo- ΔGbindABFE Type-II inhibitors generally bind when the activation loop is in a folded DFG-out conformation (Figure 1B), which presents major challenges for direct simulations to determine the free energy cost of the conformational change in contrast to the method employed here (Equation 3). Because the type-II inhibitor imatinib is co-crystallized in a type-II binding mode with MAPK14 (p38α), an STK, and several other TKs (e.g. ABL1, DDR1, LCK, CSF1R, KIT, and PDGFRA), we chose this inhibitor as an initial probe of our hypothesis that TKs evolved to have lower ΔGreorg than STKs (Figure 2). In this example we note that TKs bind strongly to imatinib (‘STI’ in Figure 2) with an average ΔGexpo of –9.3 kcal/mol, in contrast to the STK MAPK14 which binds this drug very weakly (ΔGexpo=-6.1 kcal/mol). At face value this appears consistent with our analysis from Figure 1D, where we calculated a large Potts DFG-out penalty for MAPK14 (ΔEPotts=5.2) and low penalties for TKs, suggesting that the weak binding of imatinib to MAPK14 is due at least partially to large ΔGreorg . To confirm this, we used ABFE simulations with the imatinib: MAPK14 complex to evaluate Equation 3, confirming that MAPK14 incurs a large penalty to adopt the DFG-out conformation with the activation loop folded (ΔGreorg=5 kcal/mol) (Figure 2). Figure 2 with 1 supplement see all Download asset Open asset Overview of the conformational landscapes between serine/threonine kinases (STKs) and tyrosine kinases (TKs) from absolute binding free-energy simulations, where we compare ΔGbind (hatched bars) from binding free-energy simulations with ΔGexp (solid bars) for the type-II inhibitors imatinib (the Protein Data Bank [PDB] code: STI) and BIRB-976 (PDB code: B96) vs several TKs (orange) and STKs (blue). Figure 2—source data 1 Absolute binding free-energy results for kinases bound to imatinib and BIRB-796. https://cdn.elifesciences.org/articles/83368/elife-83368-fig2-data1-v2.csv Download elife-83368-fig2-data1-v2.csv Despite the large ΔGreorg predicted for MAPK14 by both the Potts model and the simulations with imatinib described above, highly potent type-II inhibitors have been successfully developed for this kinase. For example, BIRB-796 (Pargellis et al., 2002) binds to MAPK14 about 7 kcal/mol more strongly than imatinib. This stronger binding of BIRB-796 is captured by ΔGbindABFE from our simulations (Figure 2), and the calculated value of ΔGreorg for this complex (ΔGreorg≈4 kcal/mol) is very close to the corresponding estimate of ΔGreorg based on simulations with imatinib (Figure 2). Importantly, this result suggests that STKs can be potently inhibited by type-II inhibitors despite their large ΔGreorg. To support this, we performed additional ABFE simulations with BIRB-796 and calculated ΔGreorg for two additional STKs predicted to have large reorganization penalties (MAPK9 and BRAF, ΔEPotts≥4). We calculated ΔGreorg>8 kcal/mol for MAPK9 and BRAF, which is consistent with predictions from the Potts model, and comparison of ΔGexpo and ΔGbindABFE in Figure 2 confirms that BIRB-796 is able to overcome the large ΔGreorg of certain kinases to attain high experimental potencies (e.g. MAPK14 and MAPK9). To further validate this result, we calculated ΔGreorg via ABFE simulations of BIRB-796 binding to a TK predicted by the Potts model to have a low penalty (PTK2B, ΔEPotts<1), which again shows consistency with our Potts prediction of the conformational landscape (Figure 2). The relatively weak value of ΔGbindABFE for this kinase compared with MAPK14 is also consistent with observations of the BIRB-796: PTK2B co-crystal structure (PDB: 3FZS), where the binding mode in PTK2B is more weakly associated with the ATP pocket in comparison with MAPK14 (Han et al., 2009). The analysis above provides initial support for our hypothesis about the evolutionarily divergent STK and TK conformational landscapes. To further develop this approach, we identified five STKs and five TKs which are predicted by the Potts threading calculations to have large and small ΔGreorg, respectively, and for which there are sufficient experimental structural and inhibitory data (co-crystal structures and binding constants) to calculate an average ΔGreorg via Equation 3 for each target using multiple type-II inhibitor probes. For each of the TK and STK targets, these sets of calculations can be visualized as a linear regression of ΔGexpo vs ΔGbindABFE where the slope is constrained to one, consistent with Equation 2 (see Methods for details). We employed this workflow for the set of five TK and five STK targets by simulating 22 and 23 type-II inhibitor complexes, respectively. The result of this workflow for the set of five TKs and their type-II complexes revealed a low average ΔGreorg of <1 kcal/mol (Figure 3B), consistent with our initial predictions from Potts ΔEs and type-II hit rates (Table 1). On the other hand, the binding free-energy simulations for the set of five STKs and their type-II complexes show an average of ~6 kcal/mol of ΔGreorg is required for these kinases to adopt DFG-out conformation, which is also consistent with our initial predictions from the Potts model (Table 1). To verify that the large ΔGreorg identified for STKs is a property of conformational selection for DFG-out rather than systematic overestimation of ΔGbindABFE for these kinases, we performed ABFE simulations of type-I inhibitors binding to the same set of STKs (an additional 23 complexes). For the binding of type-I inhibitors, we expect there to be no reorganization penalty due to the lack of DFG-out conformational selection in their binding mechanism. As anticipated, the calculated values of ΔGbindABFE for type-I inhibitors are very close to their experimental binding affinities (ΔGexpo) (Figure 3A). Figure 3 with 1 supplement see all Download asset Open asset Using type-II inhibitors as tools to probe the conformational landscape of TKs and STKs. (A) The average ΔGreorg calculated via absolute binding free-energy (ABFE) simulations with 23 type-I (stars) and 23 type-II inhibitors (circles) complexes in the active DFG-in and inactive DFG-out state, respectively, computed from five serine/threonine kinase (STK) targets (Table 1) and (B) computed with 22 type-II inhibitors vs five tyrosine kinase (TK) targets in the DFG-out state (Table 1). (C) Kinome plot created with CORAL (Metz et al., 2018) illustrating the selection of five TKs and five STKs which are detailed in Table 1. Figure 3—source data 1 Absolute binding free-energy results for five tyros" @default.
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• W4313651000 title "Author response: Evolutionary divergence in the conformational landscapes of tyrosine vs serine/threonine kinases" @default.
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