The Interplay between Structural Stability and Plasticity Determines

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The interplay between structural stability and plasticity determines mutation profiles and chaperone dependence in protein kinases. Antonella Paladino, Filippo Marchetti, Luca Ponzoni, and Giorgio Colombo J. Chem. Theory Comput., Just Accepted Manuscript • DOI: 10.1021/acs.jctc.7b00997 • Publication Date (Web): 20 Dec 2017 Downloaded from http://pubs.acs.org on January 2, 2018

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Journal of Chemical Theory and Computation

The interplay between structural stability and plasticity determines mutation profiles and chaperone dependence in protein kinases.

Antonella Paladino1, Filippo Marchetti1, Luca Ponzoni2, and Giorgio Colombo1,3*

1) Istituto di Chimica del Riconoscimento Molecolare, CNR Via Mario Bianco 9, 20131 Milano (Italy)

2) Molecular and Statistical Biophysics, International School for Advanced Studies SISSA, I-34136 Trieste, Italy

3) Dipartimento di Chimica, Università di Pavia, V.le Taramelli 12, 27100 Pavia, Italy

*) Author to whom correspondence should be addressed Email: [email protected] Tel: +39-02-28500031

1

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Abstract We present a novel comparative analysis of representative protein kinases to characterize the main dynamic and energetic determinants of functional regulation shared among different families. The relationships between stability and plasticity are also used to rationalize kinase tendencies to interact with the molecular chaperone Hsp90. These questions are tackled through newly developed MDbased methods of analysis of internal energy and dynamics applied to a total of 37 different systems, which represent wild type and mutated proteins, including active and inactive states. Energetic decomposition analysis is coupled to multiple structural alignments and dynamic decomposition methods and identifies, across different families, common elements that underlie fold stabilization and conformational regulation. This analysis also exposes which substructures play a key role in determining chaperone dependence. Overall, the results highlight common interaction networks that underpin kinase stabilization, are modulated by mutations, even if located at a distance, and underlie their tendencies to act as clients or non-clients of Hsp90.

2


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Introduction Biomolecules are not static entities but dynamic objects that continuously interconvert between dynamic states with varying energies. Such states can be defined as functionally oriented structural ensembles with internal motions in specific frequency and amplitude ranges and with distinct coordination patterns that can be modified by e.g. ligand attachment or mutations, even in the absence of major conformational changes. Transitions between dynamic states underlie the (de)activation of functions and interactions in the cell1. In this framework, sequence properties, structural stability, conformational dynamics, molecular recognition, and functional regulation are fundamental aspects of protein biology so intimately connected that have arguably been evolutionarily conserved across various protein families

2-9

. The importance of these links is vividly

portrayed by protein kinases, a class of biomolecules that operate in distinct signaling pathways where control of catalytic activities and interactions with binding partners is fundamental for cell development and survival 10-13. However, many molecular details of these interdependencies are still elusive. Kinase catalytic domains display a common bi-partite structure, composed by the small N-lobe coupled to the compact C-lobe, mainly helical, with six conserved segments (αD-αI) that appear in all members of the family (Figure 1). Crystal structures of diverse kinases in different activation states show conformational changes that favor adaptation to changing interaction partners. Kinase activities are also finely regulated by allosteric mechanisms 11, 12, 14-16. In contrast, they can be dysregulated by mutations that often result in enzymatic over-activation with severe impacts on the mechanisms controlling cell death: it is thus no surprise that many mutations are associated with cancer phenotypes and kinases are one of the major classes of targets for which drugs are being developed 17-20

.

A common aspect of kinases’ interaction landscape is represented by their connection with the molecular chaperone heat shock protein 90 (Hsp90), a highly dynamic ATP-regulated machine that oversees the folding quality control and activation of a large and diverse number of client protein substrates

21, 22

. Hsp90, together with co-chaperones (such as Cdc37), plays a unique role among

molecular chaperones by promoting the evolution of heritable new traits, favoring the activation of otherwise unstable/metastable clients

23, 24

. This capacity becomes manifest in the stabilization of 3



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hyper-activated kinase mutants in cancer, allowing malignant cells to thrive in the face of adverse challenges, including the effects of kinase-targeted anticancer drugs. In fact, since mutated kinases able to escape drug effects may in general be less stable than their native counterparts, they depend on Hsp90’s folding-control activities for their function

25-30

. Moreover, the stabilization buffer

determined by interaction with the chaperone enables to evolve and maintain resistance in developing cancer cells

23, 25

. It comes as no surprise that Hsp90 has become the target of several

efforts aimed at the pharmacological inhibition of its actions25,

31-35

. However, the roles of kinase

sequence, structure and dynamic mechanisms in determining interactions with Hsp90, as well as the relevance of these properties in defining the recognition by co-chaperones (e.g. Cdc37) that select clients for subsequent entry into the chaperone cycle, are still partially elusive36. Data from the Lindquist lab show that Hsp90::kinase interactions vary continuously over a 100-fold range: in wild-type clients, Hsp90 does not bind particular sequence motifs, but rather associates with intrinsically unstable kinases. Interestingly, thermodynamic parameters have been shown to correlate with client binding within a specific kinase family. Hsp90 has indeed been defined as a ‘thermodynamic sensor’, apt to bind client kinases less thermally stable than non-clients

21, 22, 30

. In

this context, recognition by Hsp90 is related to conformational plasticity and stability of the client rather than being linked to elements of primary sequence: indeed, kinase structural stabilization by inhibitor binding decreases affinity for the chaperone. Finally, with regards to the role of cochaperones, Cdc37 has emerged as the protein that recruits kinases to the Hsp90 chaperone machine 37, 38

. The N-terminal domain of the co-chaperone (N-Cdc37) acts as a kinase scanning factor with

broad specificity, recognizing both clients and non-clients. Client selectivity is associated to the ability of N-Cdc37 to induce local unfolding only in client kinases. Interaction with non-clients does not alter their conformational properties, and the substrate dissociates. Cdc37 has been assessed to sense the thermal stability of kinase domains by subjecting them to a controlled stress test of conformational stability 39.

In this paper, we set out to develop a transparent comparative framework to investigate the common and differential traits of internal energetics and dynamics of wild type and mutated kinases that determine the regulation of stability and functionally oriented motions across various representatives 4


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of different families. In this context, we analyze whether stability and plasticity properties may be linked to the kinase tendencies to interact with Hsp90. We tackle these questions through newly developed methods of internal energy and dynamics analysis based on all-atom MD simulations40-46. Overall, we analyzed 27 crystals representative of open-active and closed-inactive states and ran 2 independent MD replicas for each of the molecules listed in Table 1 (in total, 37 simulation systems including wild types and mutations). The results of energetic analysis are coupled to multiple sequence alignments and rigid domain decomposition methods to identify common elements (across different families) that underlie stabilization and dynamic regulation, and define substructures that may have undergone common structural and/or functional selection mechanisms that in turn determine chaperone dependence. We show that it is possible to define simple energy- and structure-based descriptors that are able to capture the main conformational stabilization determinants of different kinases and that may define their tendencies to act as clients or non-clients of Hsp90. In this work, we also focus in particular on the Tyrosine Kinase (TK) family, the widest and most targeted group of molecules in the kinome, and identify possible relationships between stability and sequence evolution patterns 47, 48. The results highlight interaction networks that underpin Hsp90 recognition and suggest potential mechanisms of kinase evolvability and adaptation to changing conditions.

Results and Discussion Preliminary MD analysis. We first evaluate the structural stability of the kinases under the simulation conditions. Average RMSD is 0.25 nm ± 0.08; RMSF values are in general similar between the openactive and closed-inactive states, with the exception of the activation loops that show in general a markedly different behavior (Figure S1). Typical breathing motions of the N- and C-lobes are observed. Interestingly, when comparing open-active with closed-inactive structures, we observe that the distances between the Center of Mass (COM) of the two subdomains could populate similar ensembles, in agreement with the hypothesis that the two states belong to an ensemble of accessible conformations, separated by low energy barriers (Figure S2).

Table 1. List of protein kinases. PDB codes, names and mutants of simulated kinases. * indicates 5



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Hsp90 clients, according to Taipale et al. 21 Group

Protein Faimly

PDB

Xray Mutations

Modeled Mutations

State

1

TK

LCK*

1QPC

open

2

TK

EphA2*

4TRL

open

3

TK

SYK

4XG2

open

4

TK

FGFR3*

4K33

K650E

5

TK

FGFR3*

4K33

K650E

6

TK

EGFR

2ITP

G719S

7

TK

EGFR

2ITP

G719S

8

TK

EGFR

2GS7

9

TK

EGFR

2GS7

10

TK

EGFR

2J6M

11

TK

EGFR

2J6M

L858R

open

12

TK

EGFR

2J6M

T790M / C797S /L858R

open

13

TK

EGFR

5HIC

14

TK

c-SRC

2SRC

15

TK

c-SRC

2SRC

E502D /T354M

closed

16

TK

c-SRC

2SRC

E502D

closed

17

TK

c-SRC

1YI6

18

TK

v-SRC*

1Y57

E502D/T354M

19

TK

v-SRC*

1Y57

E502D/T354M

D502E

20

TK

v-SRC*

1Y57

E502D/T354M

R318Q

21

TK

ABL1*

2GQG

22

TK

ABL1*

2GQG

open E650K

open open

S719G

open closed

G719S

closed open

T790M/L858R

open closed

open open open

open T315I

6


open

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23

TK

ABL1*

2G1T

closed

24

TK

ABL1*

3QRJ

25

TK

ErbB4*

2R4B

closed

26

TK

TRKA*

4PMM

intermediate

27

TKL

MLK1*

3DTC

open

28

TKL

BRAF*

4E26

open

29

TKL

TGFbR1

3TZM

open

30

CAMK

caMLCK*

2X4F

open

31

CAMK

PIM1

3A99

open

32

CMGC

CDK4*

3G33

open

33

CMGC

CDK9*

3MI9

open

34

CMGC

CLK3*

2EU9

open

35

CMGC

Erk5*

4IC7

open

36

STE

PAK4

2Q0N

open

37

AGC

AKT2*

2X39

open

T315I

open

Energy decomposition analysis. Here, we use the Energy Decomposition Method (EDM, described in Methods) to identify commondifferential contributions of specific substructures to the principal traits of structural stabilization in the different states of the kinases 40, 42, 46, 49-54. At the domain level, the simplified energy matrices derived from EDM clearly show a bipartite block character, reflecting the organization of the kinase catalytic domain into two lobes: the C-lobe appears to concentrate the maximal amount of stabilization energy, while the N-lobe appears to share a minimal part of the stabilization energy (Figure 2) 46. This general qualitative feature is shared by all simulated systems, independently of structural, sequence, and activation state classifications 7



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(Figure S3).

At a finer level of resolution, to detect and expose specific subregions from different structures that share a corresponding role in the stabilization of the 3D fold, we first proceed by identifying sets of residues that are contiguous in sequence and that can be put in one-to-one correspondence among the proteins by multiple structure alignments (See Methods). The goal is to select the groups of amino acids (blocks) that may define the principal networks of interactions at the basis of the stabilization of functional structures. Multiple structure alignments (see Methods) on the full panel of structures define 15 conserved blocks that cover on average 58% of the sequence length (up to 78% when only TKs are considered). Because this analysis combines conformations from different proteins with different activation profiles, the covered conformational breadth is large, and allows to identify the common conformation-independent structural blocks reported in Table 2 and highlighted on Figure 3. On the basis of this subdivision, we evaluate the amount of stabilization energy (Enb) contributed by each single block to the overall 3D fold. This measure is intended to identify the regions that determine kinase folding-stabilization and, as a consequence, the tendency of a particular kinase to be a client of Hsp90. Enb for each substructure (block) is calculated from the stabilization energy matrices obtained with the Energy Decomposition Method (EDM). Therefore, each block is linked to an Enb value that is filtered out from the total energy matrix (Figure 2, S4) and that represents its contribution to the stabilization of the protein (see Methods for details).

Table 2. Structural blocks common to protein kinases. Blocks 1-6 form N-lobe; blocks 7-15 correspond to C-lobe. Column 3 indicates the number of amino acids in the block. Block

Substructure (block) classification

# aa

1

β1

5

2

β2

9

3

β3

10

8


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4

αC

7

5

β4

10

6

β5

9

7

αD

9

8

αE

22

9

β6

16

10

β7+A-loop

11

11

αF-1

9

12

αF-2

20

13

αG

8

14

αH

20

15

αI

8

We rank the contributions to stabilization of the blocks by calculating their relative weight on the total non-bonded energy, Enbtot. Interestingly, kinases present significant differences in the N-lobe energetic profile. Indeed, only members of the CMGC and TKL families show energetically stabilizing regions in the N-lobe, while in all other groups the C-lobe appears to host the relevant interaction networks for fold stabilization (Non-TKs panel). In Figure 4A, outliers of Enb blocks distribution are highlighted. They correspond to kinases Pim1 (CAMK) and Akt2 (AGC) for αE, Pak4 (STE) for αF and caMLCK (CAMK) and Cdk9 (CMGC) for αH, that are in turn associated to lower N-blocks Enb contributions (see Table 1, Figure S4). Focusing on the 9 structural blocks identified in the C-lobe in Figure 4A, it is found that helices αE, αF and αH consistently provide the highest contributions to stabilization energy. At the same time they define distinct profiles among the various systems, with αF and αH contributions to stabilization allowing to discriminate between TKs and non-TKs (blocks 12 and 14 in Figure 4A). Interestingly, the residues defining the regulatory spine emerge as stabilizing hotspots 12, 14, 15 and cross the stabilizing sub-blocks in all structures observed. 9



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In this framework, it may be hypothesized that the differential contribution of structural elements to the overall stability may be one of the key factors determining Hsp90 dependence (clientness): focused analysis of members of the various families in the light of their activation and Hsp90 interaction profiles reveals interesting patterns. In the case of Src kinases, energetic profiles of the relevant structural blocks detected above are clearly modulated by the presence of mutations and by the type of activation state considered, regardless of the localization of the mutation. Importantly, mutations E502D, T354M, R318Q have been associated 29 to Src Hsp90 dependence. In details, double mutant E502D/T354M converts c-Src, with scarce affinity for Hsp90, into v-Src, a very strong client. Moreover, R318Q within the αC-β4 loop has been assessed to contribute to the loss of stability of c-Src 29. αE and αF (8 and 12 in Figure 4A, Figure 5, Table S1) are the most affected regions, with the former largely destabilized by the presence of activating (oncogenic) mutations. An analogous trend is also observed for Abl kinases, with αE and αH (8 and 14 in Figures 4A, 5, Table S1) responding to mutational perturbation with a marked destabilization. In EGFR, the contribution to stabilization of helix αF together with αE and αH is modulated in particular by mutations in the activation loop (L858R) at the interface between N- and C-lobe. The observed effect is maximal in the case of the double/triple mutant (Figure 5, Table S1). Interestingly, all such mutants, in which the stabilization of helices αE, αF, αH is affected, show an increased Hsp90 dependency 55. Further investigations of Src, EGFR and Abl - for which different activation states can be compared show that oncogenic mutations also affect the energetics of the small αG helix (Figure 6 and Table S2). αG of Hsp90-client kinases appears to be less energetically coupled to their respective cognate kinases becoming more prone to local unfolding and ultimately shifting the equilibrium toward Hsp90-dependent states. Indeed, in our energetic analysis, the small αG-helices in clients cluster at lower energy contributions compared to non-clients. This observation is in agreement with previous findings that associate functional conformational transitions in kinases to local unfolding of secondary structure elements 56-59. Recently, Gervasio and coworkers 60, 61 showed that the large flexibility of the αG helix facilitates conformational transitions upon binding of Imatinib to c-Src. 10


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Overall, these results highlight that the fundamental stabilization elements shared by different kinases are the three αE, αF and αH helices that define the core of the C-lobe. Interestingly, the contribution to the overall stabilization of these helices is modulated by oncogenic mutations, which can often be located at a distance. These core substructures can act then as sensors and reporters for the stabilization state of the fold. The small αG helix emerges as a minimally coupled element that can aptly support local unfolding facilitating structural transitions. These results, which link sequence changes to the modulation of the energetic stabilization of specific structural elements, are consistent with the “cracking” model

57

: the perturbation of the stability of core elements, coupled to local

unfolding events, can help lower the energy barrier for the structural transitions between active and inactive states. Moreover, these results are consistent with recent experimental findings by the Veglia group, who showed that protein kinase A activation relies on the synchronous coordination of motifs that are apparently isolated

11, 15, 62

. In vivo, less stable activated mutants may be protected from

unfolding/aggregation by increased affinity for the Hsp90 chaperone machinery. Summarizing, these results illustrate that, despite the diversities in the sequences and the initial conformations of the molecules, common structural elements underlie their mechanism of stabilization. In fact, αE, αF and αH helices from the C-lobe are highlighted in all proteins as energetic hotspots capable of modulating the stabilization of the kinase fold, which may ultimately reverberate in the Hsp90 dependence of a certain sequence. αG helix is the structural element that may modulate chaperone binding through local unfolding. Importantly, such key roles are not immediately evident from standard structural analyses of the proteins. Finally, it is worth noting that the relative contribution of helices αF and αH to stabilization is maximal in TKs, which represent the largest and most diffuse ensemble of kinase proteins.

Members of the TK family exhibit specific traits of intramolecular interactions Considering the relevance of TKs in cancer biology and as drug targets, and motivated by the above reported observations, we next focused our analysis specifically on this family. Multiple structure alignment only on TKs allows to further identify two main consistent sub-blocks in the C-lobe, 11



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separated by the activation loop, A-loop: the upper C-lobe made by αD, αE, β6 and β7 and the lower C-lobe, which includes αF1, αF2, αG, αH and αI, hereafter labeled C1 and C2, respectively (Figure 1). In terms of energetics, it is found that the TK decomposition into 3 main sub-blocks clearly separates their contribution to stabilization and reveals a polarized distribution of interaction intensities whereby the N-lobe and C2 subdomains provide the minimal and maximal contribution to the stabilization of the 3D fold, respectively. In this fold-polarization picture, the C-lobe acts as a primary folding/stabilizing nucleus, while the N-lobe would be most prone to unfold or host activitymodulating mutations that may not affect overall stability (Figure 4B).

Dynamic domain decomposition. Internal dynamics of proteins is increasingly recognized as one of the principal determinants of biological function

8, 41, 63-68

. On this basis, we ask whether the relevant energetic-coupling blocks

identified above are endowed with specific internal dynamic properties. To this aim, we apply to each MD simulation a decomposition scheme based on the analysis of residue-pair distance fluctuations to identify essential quasi-rigid domains (a.k.a. dynamic domains) that has previously been proved to capture the main structural deformations accompanying functional motions. The scheme is based on the definition of a quality score that can pinpoint significant subdivisions based on the balance of intra- and inter-domain distance fluctuations compared with a random reference 69, 70. Expectedly, the simplest and most direct subdivision identifies 3 dynamic domains, corresponding to the N-lobe, Clobe, A-loop. Pushing the subdivision to a larger number of subdomains further partitions the Cdomain into C1 and C2 subdomains (see above for TKs), coincident with the ones previously identified with the energy decomposition (for 12 out of 27 kinases crystal structures, 9 (out of 16) of which are TKs, see Table 1 (Figure S5 and Table S3)). Interestingly, typically beyond 5-6 partitions, quasi-rigid domain decomposition captures αG helix, as a small isolated dynamic domain, which can be independently displaced with respect to the whole C-lobe. Overall, these data connect fold stabilization and dynamics: fundamental interactions for a certain 3D organization, evident from the energy block analysis, define the architectural framework that supports dynamic units, which in turn trigger interconversion among dynamic states showing different Hsp90-dependence or activation profiles. 12


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Sequence pressure and evolution considerations To investigate whether a possible link exists between Hsp90-clientness, energetics and sequence evolution, and whether the substructures identified as important can be distinguished by specific evolutionary signatures or mutation patterns, we calculate sequence alignments scores for the abovedefined structural-energetic blocks, followed by the evaluation of the standard deviation of the Pamscore as a measure of the conservation of the “block sequence” between the proteins studied (a lower Std Dev means higher conservation, Figure 7A). We found higher residue conservation for blocks in the N-lobe of TKs compared to non-TKs proteins, while most of the sequence variation within TKs is concentrated around A-loop, αF and αH helices (see Figure 7A). Interestingly these common blocks correspond to the stabilizing building blocks in both clients and non-clients, in the energy decomposition framework. In other words, fold stability is controlled by a well-defined core of substructures and sequence variation modulates their relative contributions. Remarkably, these regions also share similar sequence conservation profiles. Next, we set out to map the distribution of oncogenic mutations onto the kinase structures. It must be noted here that most of annotated mutations refer to the case of TK sequences that are known to represent the most populated kinase family in humans and are the most characterized (and drugged) targets. Interestingly, the highest percentages of reported mutations concentrate on blocks αE, αF and αH, which largely overlap with the common stabilizing blocks shared across different kinase families, identified by energy analysis (Figure 7B). Note that TKs and non-TKs proteins share similar oncogenic mutation distribution. The regions with higher numbers of mutations with respect to the expected number reported for sub-blocks (αF and αH) coincide with the regions where energy stabilization is more intense. These findings are consistent with the hypothesis that structural stabilization, favored by the interaction of well-packing compact units, may favor increased tolerance to mutations that provide functional advantages (in this case increased activities) 71-73: such distribution of stabilizing units and the resulting extended interaction network could indeed buffer the potential destabilizing effects of gain-of-function mutations, and thus guarantee structural integrity71-73. 13



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In this scenario, it is not surprising that helices αE/αF/αH accumulate a number of mutations that is larger than the expected one. On the other hand, helix αG shows a minimal mutation percentage (less than expected, Figure 7B): this helix is in fact involved in the recruitment of the substrate so that its structural and sequence preservation may be a requirement to ensure optimal control over substrate recognition processes (Figure 6).

Mutation analysis: impact on allostery and conformational regulation. Based on the identification of relevant energetic blocks that may sustain the selection of functionally oriented dynamic states, we set out to characterize in more detail the energetic and dynamic impacts of mutations (scattered along the whole sequence) on the variants known to be oncogenic in our data set. Usually, oncogenic mutations are responsible for pushing the conformational equilibrium toward the open/active state, enhancing protein activity (gain of function). In this context, simulations starting from the open/active kinase X-ray structures show that the presence of a point mutation leads preferentially to an increase in the stabilizing interactions in the C-lobe (Figure 5, Table S1). This is particularly relevant in the important cases of the T315I mutation in Abl1 and of the G719S EGFR, where point mutations are located at N-lobe regions. In the Src system, mutation R318Q also affects the C-lobe energetics, in this case with reverberations on the N-lobe. It is interesting to note that the effects of mutations extend well beyond their specific points of localization, resulting in a long-range allosteric effect that overall translates in an increased stabilization of the activated/open state. In this context, Veglia and coworkers, using dynamic allostery-based community maps and NMR studies, showed that residues at the hydrophobic core of protein kinase A are allosterically connected with the entire kinase molecule and all internal residue motions impact catalytic activity 11, 62. Conformational switch between active/inactive states depends on the dynamic rearrangements of the αC helix to populate the optimal orientation in order to interact with the Activation loop (A-loop). Hence, where open and closed forms of the catalytic domain are available, analyses of the interaction energies between the αC helix and A-loop reveal in general increased energetic couplings between these two elements for the open/active kinases (see Table 3, Figure 1), corroborating previous observations 49. The concerted rearrangement (αC helix displacement, A-loop conformational change) 14


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of these two structural blocks is considered an important driver of the inactive-to-active switch of kinases: consequently, this ‘communication’ accounts for their well-known reciprocal role in determining the activation state of the protein kinase. The mechanism of regulation-at-a-distance, or allostery, that determines the impact of mutations on the activation state of kinases can be rationalized in the framework of the ensemble view of proteins 74-76

. According to the energy landscape theory, each equilibrium state can be viewed as an ensemble

of conformations populating basins of variable width on the free energy landscape. The ensemble may be characterized by 3D spatial arrangements of minimal free energy around which atomic positions are subjected to equilibrium fluctuations. Such spatial arrangements, in turn, are primarily determined by the strong interaction blocks identified above. Distinct states (active and inactive in this case) can in principle share the same fundamental intramolecular interaction determinants that stabilize the global 3D arrangement (αE, αF and αH helices in the C-lobe), while representing different conformations that are linked to distinct functions. Dynamic changes in structures that are not strongly coupled to the protein stabilization core may be sufficient to trigger functionally-oriented conformational changes, defining functional modules (αG, αC and A-loop). Moreover, the perturbation of the intramolecular networks of stabilization determined by mutations modulates the energetics of conformational transition that, in the case of oncogenic kinase mutations, translates in over-activation. This model entails considering allosteric proteins as modular structures that segregate the functional modules and stability determinants into different structural sub-domains. In this view, pioneered by Hilser and coworkers for ligand-dependent activation, the protein is hypothesized to be able to populate the activated and inactive states at equilibrium even in the absence of ligands or mutations 77-79

. In the minimal model, the functional modules (αC and A-loop) presiding activation may access

different states, e.g. low and high activity states, with different energies and dynamics. αG, in turn, controls interaction with Hsp90. The functional substructures then determine different interactions with the rest of the protein. As demonstrated by Hilser, the highest allosteric responses can be obtained by perturbing substructure stabilities without causing the protein to completely unfold. This model therefore suggests a complex view of the relationship between structural and dynamic modulation and allosteric effect, which is compatible with many observations discussed above 80, 81. 15



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Table 3. Total interaction Enb between  helix and A-loop. omparison between open/active and closed/inactive protein kinases. * indicates non-crystallographic systems. Open/Active Enb Egfr

G719S Abl c-Src v-Src

Closed/Inactive Enb

(kJ/mol)

(kJ/mol)

-3.60 ± 0.67 -4.89 ± 1.38* -3.87 ± 1.00 -13.89 ± 1.64 -6.70 ± 3.33

-0.97 ± 0.42* -1.13 ± 0.62 -1.22 ± 0.13 -3.72 ± 0.82 -4.37 ± 0.03

onclusions: A simple model for kinase stabilization and chaperone dependencies In general, the comparative analysis of different members and structures of the kinase family, which exploits a simplified yet viable general description of the protein internal energetics and dynamics, identifies fundamental structural and mechanical aspects that underlie kinase stabilization, activation and response to mutations. For all systems, corresponding shared core regions in the C-lobe are singled out as the determinants of stabilization of the protein, while accessory substructures preside conformational changes that lead to activation. On the basis of the contribution of C-lobe core regions to the overall stabilization of the kinase fold, we can qualitatively rank the tendency of different kinases (and their mutants) to depend on Hsp90 for structural maturation. This result also suggests that the chaperone machinery (including co-chaperones, such as Cdc37) may scan kinases as clients based on their conformational stability. The accessibility of partially unfolded or metastable states (pertaining to molecules with lower global stability) has been shown by Gelis and coworkers to increase affinity for Cdc37 and form stable complexes through a multidomain co-chaperone interface. In this picture, the interaction with more stabilized non-clients is not accompanied by conformational changes of the substrate and results in substrate dissociation 39. Finally, it has to be noted that our data implying the C-lobe as the domain that concentrates the maximal contribution to stability are also consistent with the mechanistic model emerging from the recent EM structure of the Hsp90-Cdc37-Cdk4 kinase complex: here, the two lobes of Cdk4 are completely separated with the β4-β5 sheet fully unfolded. Notably, β4 and β5 in our model contribute minimally to the stabilization of the structures and may thus be most prone to unfold and interact 16


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with the chaperone machinery, thus protecting the kinase from aggregation and degradation 30. In conclusion, our work allows to identify and rationalize the common molecular determinants that underlie the relationships between kinases and the Hsp90 machinery, the impact of mutations on the activation state of this important class of proteins, and defines a novel perspective in the analysis of the regulatory factors of the human kinome.

Methods Selected Models The Kinase structures studied in this paper were all X-ray structures retrieved from the protein data bank (PDB, http://www.rcsb.org/pdb/home/home.do). We selected protein systems for which crystal structures of both open and close conformations were resolved. Tyrosine kinases (TKs) included both receptor-type proteins (Egfr, Fgfr, ErbB4, EphA2, TrkA) and non-receptor proteins (Lck, Syk, Abl, Src). The data set consists of 27 protein structures, given by 9 members of TK family and 11 representatives of the other kinase families. Considering mutants, a total of 37 systems studied and simulated. Oncogenic proteins (wild type and mutants) were chosen keeping into account the availability of experimental data on their relevance for cancer, annotated in the Cosmic Database (http://cancer.sanger.ac.uk/cosmic) In the present study, we selected proteins for which experimental Hsp90 affinity data were available. Interaction data are taken from ref. 21, 22. Selected protein kinases are listed in Table 1.

MD simulations. MD simulations were carried out using the Gromacs software package (v.4.5.5) 82 with the Amber99 force field 83. Selected starting structures for protein kinases are summarized in Table 1. All kinase domains are simulated in their apo forms. The proteins were centered in triclinic boxes allowing a 0.9 nm distance from each box edge and solvated with TIP3P water molecules 84. Counterions were randomly added to ensure overall charge neutrality. Each system was first energy minimized using the steepest descent approach, followed by a 5 ns simulation in which the positions of the protein heavy atoms were restrained by a harmonic potential. Production trajectories were run for 100 ns at constant temperature of 300 K and a constant pressure of 1 atm 85. All simulations were run in two replicas. A cutoff radius of 0.9 nm for non-bonded van der Waals interactions was used in all simulations. Bond lengths involving hydrogens were restrained by the LINCS algorithm 86. Electrostatic interactions were treated using the particle mesh Ewald method 87. The time step was set to 2 fs and periodic boundary conditions were applied in all three dimensions. 17



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Energy Decomposition Method. The Energy Decomposition Method (EDM) 42, 46, 54 is based on the calculation of an interaction matrix Mij, obtained by averaging the interaction energies between residue pairs, comprising all the nonbonded inter-residue atomic energy components (namely, van der Waals and electrostatic), over a MD trajectory starting from the native conformation. In this calculation, diagonal elements, containing self-interactions, are neglected. The matrix Mij can be diagonalized and re-expressed in terms of eigenvalues and eigenvectors, in the form:

N

M ij = ∑ λk wik wkj

(1)

k =1

k

where N is the number of amino acids in the protein, λk is an eigenvalue, and wi is the i-th component of the associated normalized eigenvector. Eigenvalues are labelled following an increasing order, so that λ1 is the most negative. In the following we refer to the first eigenvector as the eigenvector corresponding to the eigenvalue λ1 . The total non-bonded energy Enb is defined as: N

E nb =

N

N

∑ M =∑ ∑ λ w ij

i , j =1

k

i , j =1

λk wik wk j

If the term contribution only:

~ M ij ≈ M ij = λ1wi1w1j

i

k

wk j

k =1

(2) 1

1 for k>1 is smaller than λ1 wi w j , each Mij can be approximated by the first

(3)

such that the total non bonded energy becomes: (4) This simplified energy matrix captures the contribution of residue pairs to the stabilization of the overall fold, as shown previously 42, 46, 52-54, 88 The simplified matrix for each protein is further filtered by the combining the contributions of consecutive residues in sequence, whose structures can be aligned among all the selected kinases; such substructures are defined as blocks. The cumulative Enb value associated to each block is then obtained averaging all the terms for each residue grouped in the block. To compare different systems, 18


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the ratio between the contribution of each block and the full Enbtot for the respective full length protein is calculated. . POSA alignment. Multiple protein structural alignment (MPStrA) is used to identify the conserved regions that form the common structural core of a protein family 89. The resulting aligned structural regions are used for analyses and constitute the blocks. Sequence alignment & Score (STD). The alignment score permits to quantify the similarity of two sequences. The score (s) of two sequences is obtained summing, for every position i, all the values of the scoring matrix m for the residue pair (ai,bi): s = Σim(ai,bi). The scoring matrix used is the common Point Accepted Mutation (PAM) 250 matrix. Mutation profiles. The number of oncogenic mutations is taken from the annotations in the COSMIC webserver (http://cancer.sanger.ac.uk/cosmic) 90. For every block in the sequence the fraction of mutation is computed, which is the number of annotations of the block over the number of mutation recorded in all the blocks. To discriminate whether a fraction is relevant or not, a threshold is defined as the number of residues of the block over the total number of residues of all the blocks (this holds for all proteins considering that they undergo a common subdivision). This ultimately represents the probability to have a mutation in a block if all the residues are equally probable to mutate. Dynamic Domains decomposition. The identification of quasi-rigid domains is obtained with the SPECTRUS method, a cluster algorithm that relies on the dimensional reduction (spectral clustering) of the inter-residue distance fluctuation matrix, computed on frames extracted from an MD simulation. A quality score is used to measure the robustness of the domain separation, i.e. how much the clusters are compact and distinct. This measure is achieved by computing for each residue the ratio between the distance with its own cluster center and the distance with the second closest cluster center. The local maxima of the reciprocal of this quantity, averaged over all residues, mark the decompositions with the most well-defined clusters and allow to identify, a posteriori, the optimal number of clusters to be considered. Details are given in 69.

Acknowledgements The authors wish to thank Prof. Cristian Micheletti (SISSA, Trieste) for critical reading of the manuscript and for useful advice. This work has been supported by AIRC (Associazione Italiana Ricerca sul Cancro) through grants IG 15420 and 20019.

Supporting information The supplementary information file contains additional figures and tables cited throughout the text. The Supporting Information is available free of charge via the Internet at http://pubs.acs.org.

19



Author Contributions AP, FM, LP performed research. AP, GC designed research. AP, GC wrote the paper

20


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Figure aptions Figure 1. Representative structure for the simulated systems. 3D fold representation of Lck catalytic domain (pdb code: 1QPC). Main secondary structures are highlighted and labeled.

Figure 2. Enb matrix of a representative kinase catalytic domain (pdb code: 1QPC). The x- and y- axes report the protein sequence; green and magenta bars correspond to N-lobe and C-lobe, respectively. Scale unit is given in kJ/mol.

Figure 3. Mapping of aligned structural blocks over 3D kinase structure and corresponding Enb matrix per block. Representative protein belongs to the open/active Egfr – 2J6M. See Table 2 for blocks lengths and structural details.

Figure 4. A) Enb contribution per structural block (Table 2). Red and green boxes belong to TKs. Blue circles indicate outliers. B) TKs Enb contribution per subdomains N, C1, C2. Enb values on the y-axis are the ratio of Enb associated to each block over the total. See main text.

Figure 5. Abl, Egfr and Src protein kinases. TKs crystal structures (from left to right: 2GQG, 2J6M and 2SRC) are reported in green cartoons; αE is shown in blue, αF in magenta and αH in orange. Red spheres indicate corresponding point mutations (Table 1). See Table S1 in SI for mutation-dependent modulation of C-lobe α-helices Enb.

Figure 6. A) Enb matrix of c-Src (upper) and v-Src (bottom) divided per structural block. On x- and yaxes colored bars correspond to structural blocks that form the dynamic domains reported in B. B) vSrc dynamic domain decomposition (5 partitions). αG is evidenced in orange.

Figure 7. A) Sequence alignment score (Pam matrix) standard deviation and B) fraction of annotated oncogenic mutations per structural blocks are reported for TKs and nonTKs proteins. Threshold values (grey area) refer to the probability of finding a mutation in that particular block. Details are reported in Methods.

21



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Table Legends Table 1. List of protein kinases. PDB codes, names and mutants of simulated kinases. * indicates Hsp90 clients, according to Taipale et al. 21

Table 2. Structural blocks common to protein kinases. Blocks 1-6 form N-lobe; blocks 7-15 correspond to C-lobe. Column 3 indicates the number of amino acids in the block.

Table 3. Total interaction Enb between  helix and A-loop. omparison between open/active and closed/inactive protein kinases. * indicates non-crystallographic systems.

22


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Table of ontent Graphic

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Figure 1. Representative structure for the simulated systems. 3D fold representation of Lck catalytic domain (pdb code: 1QPC). Main secondary structures are highlighted and labeled. 85x93mm (300 x 300 DPI)



Figure 2. Enb matrix of a representative kinase catalytic domain (pdb code: 1QPC). The x- and y- axes report the protein sequence; green and magenta bars correspond to N-lobe and C-lobe, respectively. Scale unit is given in kJ/mol. 85x72mm (300 x 300 DPI)


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Figure 3. Mapping of aligned structural blocks over 3D kinase structure and corresponding Enb matrix per block. Representative protein belongs to the open/active Egfr – 2J6M. See Table 2 for blocks lengths and structural details. 173x108mm (300 x 300 DPI)



Figure 4. A) Enb contribution per structural block (Table 2). Red and green boxes belong to TKs. Blue circles indicate outliers. B) TKs Enb contribution per subdomains N, C1, C2. Enb values on the y-axis are the ratio of Enb associated to each block over the total. See main text. 173x64mm (300 x 300 DPI)


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Figure 5. Abl, Egfr and Src protein kinases. TKs crystal structures (from left to right: 2GQG, 2J6M and 2SRC) are reported in green cartoons; αE is shown in blue, αF in magenta and αH in orange. Red spheres indicate corresponding point mutations (Table 1). See Table S1 in SI for mutation-dependent modulation of C-lobe αhelices Enb. 173x57mm (300 x 300 DPI)



Figure 6. A) Enb matrix of c-Src (upper) and v-Src (bottom) divided per structural block. On x- and y-axes colored bars correspond to structural blocks that form the dynamic domains reported in B. B) v-Src dynamic domain decomposition (5 partitions). αG is evidenced in orange. 173x191mm (300 x 300 DPI)


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Figure 7. A) Sequence alignment score (Pam matrix) standard deviation and B) fraction of annotated oncogenic mutations per structural blocks are reported for TKs and nonTKs proteins. Threshold values (grey area) refer to the probability of finding a mutation in that particular block. Details are reported in Methods. 173x62mm (300 x 300 DPI)


The Interplay between Structural Stability and Plasticity Determines

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