Insight into the mechanism of allosteric activation of PI3Kα by oncoprotein K-Ras4B

Journal Pre-proof Insight into the mechanism of allosteric activation of PI3Kα by oncoprotein K-Ras4B

Xinyi Li, Jinyuan Dai, Duan Ni, Xinheng He, Hao Zhang, Jian Zhang, Qiang Fu, Yaqin Liu, Shaoyong Lu PII:

S0141-8130(19)37606-8

DOI:

https://doi.org/10.1016/j.ijbiomac.2019.12.020

Reference:

BIOMAC 14047

To appear in:

International Journal of Biological Macromolecules

Received date:

19 September 2019

Revised date:

2 December 2019

Accepted date:

3 December 2019

Please cite this article as: X. Li, J. Dai, D. Ni, et al., Insight into the mechanism of allosteric activation of PI3Kα by oncoprotein K-Ras4B, International Journal of Biological Macromolecules(2018), https://doi.org/10.1016/j.ijbiomac.2019.12.020

This is a PDF file of an article that has undergone enhancements after acceptance, such as the addition of a cover page and metadata, and formatting for readability, but it is not yet the definitive version of record. This version will undergo additional copyediting, typesetting and review before it is published in its final form, but we are providing this version to give early visibility of the article. Please note that, during the production process, errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.

© 2018 Published by Elsevier.

Journal Pre-proof

Insight into the mechanism of allosteric activation of PI3Kα by oncoprotein K-Ras4B Xinyi Lia, || , Jinyuan Daib, || , Duan Nia , Xinheng He a , Hao Zhanga , Jian Zhanga , Qiang Fuc,* , Yaqin Liud,*, Shaoyong Lua,d,*

Department of Pathophysiology, Key Laboratory of Cell Differentiation and

oo f

a

Apoptosis of Chinese Ministry of Education, Shanghai Jiao Tong University,

Chemical Engineering and Technology, School of Chemical Engineering, East China

e-

b

pr

School of Medicine, Shanghai, 200025, China

c

Pr

University of Science and Technology, Shanghai 201424, China Department of Orthopedics, Shanghai General Hospital, Shanghai Jiao Tong

Medicinal Bioinformatics Center, Shanghai Jiao Tong University, School of

rn

d

al

University, School of Medicine, Shanghai 200080, China

||

Jo u

Medicine, Shanghai, 200025, China

These authors contributed equally to this work

*Corresponding authors at: Medicinal Bioinformatics Center, Shanghai Jiao Tong University, School of Medicine, Shanghai, 200025, China E-mail addresses: [email protected] (S. Lu), [email protected] (Y. Liu) Department of Orthopedics, Shanghai General Hospital, Shanghai Jiao Tong University, School of Medicine, Shanghai 200080, China E-mail address: [email protected] (Q. Fu).

1

Journal Pre-proof

Abstract Ras is a key member in the superfamily of small GTPase. Transforming between GTP-bound active state and GDP-bound inactive state in response to exogenous signals, Ras serves as a binary switch in various signaling pathways. One of its downstream effectors is phosphatidylinositol-4,5-bisphosphate 3-kinase α (PI3Kα), which phosphorylates phosphatidylinositol 4,5-bisphosphate into phosphatidylinositol 3,4,5-trisphosphate in the PI3K/Akt/mTOR pathway and mediates an array of

oo f

important cellular activities including cell growth, migration and survival. Hyperactivation of PI3Kα induced by the Ras isoform K-Ras4B has been unveiled as

pr

a key event during the oncogenesis of pancreatic ductal adenocarcinoma, but the underlying mechanism of how K-Ras4B allosterically activates PI3Kα still remains

e-

largely unsolved. Here, we employed accelerated molecular dynamic simulations and

Pr

allosteric pathway analysis to explore into the activation process of PI3Kα by K-Ras4B and unraveled the underlying structural mechanisms. We found that

al

K-Ras4B binding induced more conformational dynamics within PI3Kα and triggered its step-wise transition from a self- inhibited state towards an activated state. Moreover,

rn

K-Ras4B binding markedly disrupted the interactions along the p110/p85 interface,

Jo u

especially the ones between nSH2 in p85 and its nearby functional domains in p110 like C2, helical, and kinase domains. The altered inter-domain interactions exposed the kinase domain, which promoted the membrane association and substrate phosphorylation of PI3Kα, thereby facilitating its activation. In particular, the community networks and allosteric pathways analysis further revealed that in PI3Kα/K-Ras4B system, allosteric signaling regulating p110/p85 interaction was rewired from the helical domain to the kinase domain and several important residues and their related allosteric pathways mediating PI3Kα autoinhibition were bypassed. The obtained structural mechanisms provide an in-depth mechanistic insight into the allosteric activation of PI3Kα by K-Ras4B as well as shed light on its drug discovery.

2

Journal Pre-proof Keywords: Ras; Allosteric regulation; Allosteric communication; Allosteric effects; PI3K; Molecular dynamics simulations; Protein-protein interactions

1. Introduction Ras proteins, quintessential members within the family of small GTPases, function as binary switches through cycling between the GTP-bound active state and the GDP-bound inactive state [1–4]. By interacting with downstream effectors, including

phosphatidylinositol-4,5-bisphosphate

3-kinases

(PI3Ks)

in

the

oo f

PI3K/Akt/mTOR pathway [5,6], Raf in the Raf/MEK/ERK pathway, and RalGDS in the RalGDS/Ral pathway [6–8], Ras could mediate a plethora of cellular activities.

pr

Upon insulin and growth factor stimulation [9], Ras would be activated through the exchange of GDP with GTP under the catalysis of guanine exchange factors (GEFs)

e-

[10]; while its deactivation process is mediated by GTPase-activating proteins (GAPs)

Pr

in a stepwise manner, during which the intrinsic GTPase activity of Ras is enhanced, and thus the GTP is accelerated to hydrolyze back to GDP [11–13].

al

As principal Ras effectors, PI3Ks belong to the lipid kinase family which serve as essential components in signal transduction networks promoting cell proliferation and

activation,

PI3Ks

recruit

and

phosphorylate

phosphatidylinositol

Jo u

Upon

rn

survival, and it can be activated by interacting with the GTP-bound Ras [9,14,15].

4,5-bisphosphate (PIP2 ) into phosphatidylinositol 3,4,5-trisphosphate (PIP3 ) on the cell membrane, and thus signals regulating cell proliferation and survival are delivered through the PI3K/Akt/mTOR pathway [14,16,17]. Hyperactivation of PI3Ks is a distinguishing feature in both the development and the maintenance of cancers [18–20]. Class I PI3K, which has four known isoforms, α, β, γ and δ, is the best characterized member in the PI3K family. Each isoform in Class I PI3K primarily consists of a p110 catalytic subunit and a p85 regulatory subunit. These two subunits can be further divided into several functional domains (Figure 1). For p110 subunit, there is an amino-terminal adaptor-binding domain (ABD), a Ras-binding domain (RBD), a protein-kinase-C homology-2 (C2) domain, a helical domain and a

3

Journal Pre-proof carboxyl-terminal kinase domain, while the nSH2 domain and iSH2 domain execute the main regulatory functions of p85 subunit cooperatively [2,21–24]. The catalytic activity of p110 is inhibited by p85 through direct inter-subunit interactions in the absence of pro-growth signals, rendering PI3K in an autoinhibited form [23]. Upon binding of Ras, the inhibition exerted by p85 is readily relieved through dissociation of the two chains, during which nSH2 domain in p85 subunit plays a pivotal role [25–

Jo u

rn

al

Pr

e-

pr

oo f

27].

Figure 1. Structural overview of the p110/niSH2 heterodimer. (A) Sketch map of domain organization. Linkers between domains are colored gray. Numbers underneath defines the residual range for each domain. (B) Cartoon diagram of PI3K α (PDB code 4L1B) from front and back, domains are colored according to scheme above. (C) Surface diagram of PI3Kα from front and back, domains are colored according to scheme above. 4

Journal Pre-proof

Among all four isoforms in Class I PI3K, PI3Kα is the only one harboring frequent oncogenic mutations [24], and previous studies indicated that it was implicated in the pathogenesis of pancreatic ductal adenocarcinoma (PDAC), a highly lethal cancer with extremely low survival rate and currently no effective therapy [28– 30]. During tumorigenesis, the oncogenic PI3Kα signaling is initiated upon its interaction with mutant Ras, especially K-Ras4B, the most frequent genetic lesions in

oo f

PDAC with a mutation rate over 95% [31–34]. As a splice variant of K-Ras, K-Ras4B distributes predominantly in the pancreatic tissue, and the notorious PDAC is mainly

pr

driven by its mutations [3,34–38]. Oncogenic mutations usually disrupt the GTP hydrolysis process of K-Ras4B and trap it in an abnormal active state. Thus, it will

e-

aberrantly interact with the RBD in PI3Kα, which triggers the pathogenic pro-growth

Pr

signals within cells, and ultimately contributes to tumorigenesis [39–42]. In this sense, revealing the interaction patterns between K-Ras4B and PI3Kα will shed light on the

al

detailed mechanisms underlying the pathogenesis of PDAC, as well as provide guidance for future related drug discovery.

rn

Recently, several excellent studies using molecular dynamic (MD) simulations to

Jo u

explore the dynamics within PI3Kα have found that the most frequent charge-reversal oncogenic mutation, H1047Rkinase, disrupts the hydrogen bonding network along the p110/p85 interface and alters the polarity within the kinase region [43], while other common oncogenic mutations such as E542K helical and E545K helical are found to induce a spontaneous detachment of nSH2 from the helical domain, whic h contributes to the abrogation of the autoinhibited state [44]. In the meantime, latest studies from Nussinov et al. revealed that upon protein-protein interaction (PPI), K-Ras4B would allosterically regulate PI3Kα, further thrusting it towards a PIP2-binding favored active state, and during this process the conformational changes of p110 triggered by nSH2 release were important [40,45]. On the other hand, we have recently uncovered

5

Journal Pre-proof a “hidden” binding pocket on Ras GTPase along its hydrolysis pathway, which would be a promising target for the modulation of the critical PI3Kα/K-Ras4B PPI [12,46]. However, the aforementioned studies were all based on conventional MD (cMD) simulations, which cannot capture the conformational dynamics efficiently in large biomolecular systems and is relatively restricted in regard of the simulation time scale. Therefore, most of these findings were limited to studying the effect of mutations or binding of upstream regulators towards PI3Kα fragments, and they could only cover

oo f

the dynamic process at nanosecond level. Consequently, long time scale studies on the interactions between full- length PI3Kα and its regulators are still challenging, and

pr

particularly, the mechanistic details underlying the PPI between PI3Kα and K-Ras4B remain elusive. In an attempt to combat such obstacles, accelerated MD (aMD)

e-

simulations were employed in our study [47]. aMD simulations are able to enhance

Pr

conformational sampling through providing boost energy to the simulation system and decreasing the energy barriers [48,49]. It has been successfully applied to the

al

research of bulky biomolecular systems such as muscarinic G-protein coupled

reliability.

rn

receptor [50], and opioid receptor [51], and exhibits convincing accuracy and

Jo u

Here, for the full- length PI3Kα system, we set out to explore its allosteric activation mechanism by K-Ras4B using aMD simulations. The computational results unraveled more significant conformational fluctuations in PI3Kα upon K-Ras4B binding. In the PI3Kα/K-Ras4B system, three energy-favored dominant structures emerged in a chronological order towards a less autoinhibited state, indicating more flexibility than apo PI3Kα system, which had only one dominant conformation. Furthermore, the interaction networks along the p110/p85 interface were disrupted considerably, especially the ones between nSH2 and its nearby functional domains like C2, helical and kinase domains. Several critical residues for PPI were also identified. In-depth analyses unraveled that the underlying mechanism may be attributable to the altered community network and allosteric pathway induced by

6

Journal Pre-proof K-Ras4B binding, during which residues from the kinase domain get more directly involved in signal transductions from RBD to p110/p85 interface. More significantly, analysis of the signal propagation patterns within the overall complex unveiled the critical role of helical domain and kinase domain in the maintenance of autoinhibition and in PI3Kα activation. The results in our study will shed light on the PI3Kα/K-Ras4B interaction patterns, promote better understanding towards the carcinogenesis of K-Ras4B-driven PDAC [52], and provide structural basis for future

oo f

PI3Kα-specific drug design, especially for allosteric modulators towards the

pr

pathological PPI [53,54].

2. Materials and methods

e-

2.1 Construction of systems for accelerated MD simulations

Pr

The structure of PI3Kα/K-Ras4B complex was created based on the H-Ras-PI3Kγ structure( PDB ID: 1HE8) [55] by aligning K-Ras4B (PDB ID: 3GFT)

al

and PI3Kα (PDB ID: 4L1B) [56] to H-Ras and PI3Kγ separately using Pymol [57]. The missing amino acids in PI3Kα were remodeled using Discovery Studio and the

rn

whole system was subsequently subjected to two rounds of 5000-step minimization

Jo u

using steepest descent algorithm in CHARMM forcefield [58,59]. The crystal structure for the free PI3Kα system was extracted from the aforementioned PI3Kα/K-Ras4B complex and underwent the same simulation and analysis procedures.

2.2 Conventional molecular dynamics simulations(cMD) The initial parameter files for minimization and simulation was prepared with Amber14 package using ff14SB force field and general amber force field (GAFF) [60]. Both the complexes were first solvated in an orthorhombic transferable intermolecular potential three point (TIP3P) water box [61,62], followed by adding Na+ and Cl- counterions to neutralize the system and to mimic the physiological condition. After that, two rounds of energy minimization were performed on all

7

Journal Pre-proof systems using steepest descent followed by conjugate gradient, first with the whole protein scaffold fixed for 10 ps and then without any constraint for 20 ps. After that two systems were put in a canonical ensemble (NVT) and underwent heating process from 0 K to 300 K within 300 ps with all protein atoms constrained followed by subsequent equilibration of 700 ps. After all these preparations, a total of 100 ns cMD was carried out upon the two complexes in an isothermal and isobaric ensemble (NPT) with periodic boundaries. Langevin dynamics using 1 ps-1 collision frequency was

oo f

applied to control the temperature of the two systems. Long-range electrostatic interactions were analyzed by Particle Mesh Ewald (PME) method while nonbonded

pr

cutoff was specified to be 10 Å to treat short-range electrostatics and van der Waals forces. Bond interactions involve hydrogen were constrained by SHAKE algorithm.

e-

2.3 Accelerated molecular dynamics (aMD) simulations

Pr

Accelerated molecular dynamics simulations(aMD) can reduce the energy barriers, enabling faster calculation and conformational sampling, thus allowing us to

al

efficiently get inspect into long- lived conformational transformations in large bimolecular complexes [47–49,60,63]. The general ideal of aMD is the introduction

rn

of bias potential ∆V(r) to the complex system at certain state to elevate the system

Jo u

energy out of potential basins, making the state to state evolution of complex system faster. The relationship between modified potential V* (r), true potential V(r), bias potential ∆V(r), and boost energy E can be depicted as: V(r) V(r)≥E V* (r)= { V(r)+∆V (r) V(r)
(1)

All aMD is performed on systems with modified potential V* (r), whose value correlates with that of true potential V(r) of the system by adding bias potential ∆V(r). When the value of V(r) is greater than that of the chosen boost energy E, bias potential ∆V(r)=0, under this condition, simulation is performed on the true potential, when V(r) is smaller than E, then the energy of the system gets increased by ∆V(r) which can be calculated through equation(2):

8

Journal Pre-proof 2

∆V(r)=

(E-V( r ))

α+(E-V( r ))

(2)

The parameter α determines the extent of modification we carried on the potential valley. The selection for both parameter α and boost energy E is not random, but through performing a total of 100 ns cMD simulations on both systems right after preparation in advance. As torsions are also involved in conformational changes to a relatively large extent, we employed a “dual boosting approach” to make

oo f

simultaneous sampling of internal and diffusive degrees of freedom possible. Gain from the 100 ns cMD, for K-Ras4B-PI3Kα complex, which consists of a total of 1497

pr

residues (N res) and 170323 atoms (N atom), the average total energy Eavg tot =-533424 kcal/mol, the average dihedral energy Eavg dih =19572 kcal/mol, hence the total boost

e-

parameters E(tot) and α(tot), as well as the dihedral boost energy E(dih) and α(dih)

Pr

can be calculated through:

E(tot)=Etot avg+Natom×0.16=-533424+170321×0.16=-506172 kcal/mol

E(dih)=Etot

al

α(tot)=N atom ×0.16=170323×0.16=27252 kcal/mol dih +N res×4=19572+1479×4=25560 kcal/mol

rn

α(dih)= N res×4/5=1479×4/5=1197.6 kcal/mol

Jo u

Similarly, the output for PI3Kα complex with 1328 residues (N res) and 173362 atoms (N atom) gives a result of -544989 kcal/mol for average total energy Eavg tot while 17437 kcal/mol for average dihedral energy Eavg dih , thus the parameters for aMD are: E(tot)=Etot avg+Natom×0.16=-544989+173362×0.16=-517251 kcal/mol α(tot)=N atom ×0.16=173362×0.16=27738 kcal/mol E(dih)=Etot

dih +N res×4=17437+1328×4=22749 kcal/mol

α(dih)= N res×4/5=1328×4/5=1062.4 kcal/mol A 500ns aMD simulation was hence carried on both systems based on parameters mentioned above under the same condition as that in cMD. The trajector y files of system snapshots are written out every 50ps.

9

Journal Pre-proof 2.4 Molecular Mechanics Poisson-Boltzmann Surface Area (MM/PBSA) Calculations To calculate the binding free energy between p85 and p110 subunit, Molecular Mechanics Poisson-Boltzmann Surface Area (MM/PBSA) plugin from MMPBSA.py in AMBER14 package was used in our analysis [60]. Since our primary interest lay in the interaction between p85 and p110, the K-Ras4B/p110 complex and p110 alone were regarded as the receptor for ligand p85 in PI3Kα/ K-Ras4B complex and PI3Kα,

oo f

respectively. Binding free energy ∆Gbinding were calculated based on: ∆Gbinding =∆Gcomplex -(∆Greceptor+∆Gligand )

(3)

pr

Whereas in equation (3), binding free energy was obtained based on the second thermodynamic law by calculating the sum of the entropy term (-T∆S) and enthalpy

e-

term( ∆H) upon the formation of the receptor- ligand complex, while the enthalpy term

Pr

(∆H) can be further divided in to the molecular mechanical energy (∆Emm ) contribution and the free energy released upon solvation(∆Gsol):

al

∆Gbinding =∆H-T∆S=∆Emm +∆Gsol -T∆S

(4)

∆Emm =∆Eint +∆EvdW +∆Eele

(5)

Jo u

rn

The molecular mechanical energy (∆Emm) consists primarily of three parts:

∆Eint term represented the intramolecular energy (bond, angle, torsion energy etc.), while the ∆EvdW (for van der Waals forces) and ∆Eele (for electrostatic forces) were the major energy sources generated from intermolecular interactions. For the ∆Gsol term in equation (4), as the Poisson-Boltzmann continuum solvent model was applied in our calculation, the solvation free energy ∆Gsol is thus obtained by summing up polar part (∆Gp ) and the nonpolar part (∆Gnp ): ∆Gsol =∆Gp +∆Gnp

(6)

The nonpolar term ∆Gnp in equation (6) can be solved by a simplified linear equation: ∆Gnp =γSASA+b

(7)

10

Journal Pre-proof In which SASA is short for solvent-accessible surface area, constant γ which has the dimension of surface-tension is set to be 0.00542kcal (mol-1 Å-2), while the solvation parameter b is 0.92kcal/mol. We left out the entropy term(-T∆S) in our calculation since the RMSD results, which represented the relative conformational change of the complexes during our simulation, were relatively small and showed little variation, thus indicating that the order of the system didn’t change significantly. Moreover, since our major focus was

oo f

on the allosteric regulation exerted by the binding of K-Ras4B to kinase PI3Kα, as well as taking the fact that the calculation of entropy term(-T∆S), which was usually

pr

calculated with quasi harmonic analysis, required a relatively long period, we omitted conformational entropy (-T∆S) in our calculation.

e-

2.5 Dynamic network analysis

Pr

Correlation between residues was represented by the dynamic cross-correlation matrix (DCCM) [64] and calculated through the program Carma from aMD

al

trajectories by Equation (8) as the cross-correlation coefficient Xi,j for Cα pairs:

rn

Xi,j =

〈(r i - 〈r i 〉)〉∙〈(r j - 〈r j 〉)〉

√〈(r i - 〈r i 〉)〉2 〈(r j - 〈r j 〉)〉2

(8)

Jo u

The correlation data obtained from Equation (8) were used to weight edges in the dynamic network and calculated the edge distance d i,j to define the possibility of information flow across a given edge by Equation (9): di,j =-log(|Ci,j |)

(9)

Cα of each residue was regarded as a node and edges would be drawn between two nodes if they stay within a cutoff distance of 4.5Å to each other for at least 75% of the aMD trajectories analyzed. The Floyd-Warshall algorithm was used to compute the shortest distance between all pairs of nodes in the network, while betweenness of an edge is indicated by the number of shortest paths that cross the edge. Community structure under the node network is defined and optimized by the Girvan-Newman algorithm using information from the betweenness of the edges. Shortest (optimal) 11

Journal Pre-proof paths between pairs of nodes were calculated as cross-community communication, which identified all edges connecting communities and those with highest betweenness were selected. All pathways within the distance of 25 residues were calculated.

3. Results 3.1 RMSD and RMSF analysis

oo f

500 ns aMD simulations were performed on both PI3Kα and PI3Kα/K-Ras4B complexes. Root- mean-square deviations (RMSDs) of Cα atoms in each residue of

pr

PI3Kα in the two systems with respect to the initial structures were calculated to reflect the conformational dynamics of PI3Kα during simulations. As shown in Figure

e-

2, PI3Kα in both systems reached equilibrium after ~25 ns simulations. For the

Pr

conformers in equilibrium (25-500ns), the RMSD values of PI3Kα in the K-Ras4B-ubound and -bound systems are 3.90±0.29 Å and 3.92±0.17 Å, respectively,

al

indicating no significant overall conformational differences in PI3Kα between the two

Jo u

rn

systems during the simulation process.

Figure 2: Root- mean-square deviations (RMSDs) of Cα atoms of PI3Kα in the K-Ras4B-unbound (black) and -bound (red) systems in 500 ns aMD simulations.

Root- mean-square fluctuations (RMSFs) of Cα atoms of PI3Kα were further calculated to reveal the difference of fluctuations of individual residues in local regions (Figure 3). Major functional domains of PI3Kα, including ABD (Figure 3A), RBD (Figure 3B) and kinase domain (Figure 3E) in p110, as well as nSH2 (Figure 3F) 12

Journal Pre-proof in p85, showed considerable changes in fluctuations in response to K-Ras4B binding. The fluctuations in the ABD and RBD are understandable, because K-Ras4B binding to PI3Kα requires the involvement of both domains, However, the fluctuations in the kinase domain of p110 and the nSH2 of p85 imply the changes of conformational dynamics in the p110/p85 interface upon K-Ras4B binding, which may favor the relieve of autoinhibition exerted by p85 and ultimately contribute to the activation of

Jo u

rn

al

Pr

e-

pr

oo f

PI3Kα.

Figure 3: Root- mean-square fluctuations (RMSFs) of Cα atoms of PI3Kα in the K-Ras4B-unbound (black) and -bound (red) systems, (A) adaptor-binding domain (ABD), (B) Ras-binding domain (RBD), (C) protein-kinase-C homology 2 (C2) domain, (D) helical domain, (E) Kinase domain in p110, as well as (F) nSH2 and (G) iSH2 in p85.

3.2 Principal component analysis (PCA) Principal component analysis (PCA) was applied to charac terize the dominant conformations in two systems after reaching equilibrium in simulations [60]. The results of PCA were projected to the 2D plane based on the first two principal

13

Journal Pre-proof components (PCs), PC1 and PC2, to represent the major conformational dynamics of PI3Kα. As shown in Figure 4, the conformational landscapes of PI3Kα differed markedly in the presence and absence of K-Ras4B, and the number of dominant conformers of PI3Kα increased from one to three upon K-Ras4B binding. This suggested that K-Ras4B binding would increase conformational dynamics of PI3Kα, further indicating the regulatory role of K-Ras4B in the relief of PI3Kα

al

Pr

e-

pr

oo f

autoinhibition.

rn

Figure 4: Projections of trajectories onto the two first PC1 and PC2 of PI3Kα in the

Jo u

K-Ras4B-unbound (A) and -bound (B) systems.

For the three dominant conformers of PI3Kα in the presence of K-Ras4B, D1-D3 (Figure 4B), they appeared with a decreased similarity with the initial structure in a chronological order of 54±3 ns, 241±3 ns, and 391±6 ns, which reflected the gradual transitions of PI3Kα away from its original autoinhibitory state. Significantly, by superimposing and comparing the overall structural similarity between the dominant conformer in the free PI3Kα (D0) (Figure 4A) and those in the K-Ras4B-bound PI3Kα (D1-D3) (Figure 4B), D0 that appears first in the three dominant conformers in the K-Ras4B-bound PI3Kα system, is most identical to D1, with the smallest RMSD of 1.77 Å between them. This led to the deduction that K-Ras4B initiated the

14

Journal Pre-proof conformational transitions of PI3Kα from its autoinhibitory state toward a less-inhibited state in a stepwise manner (D1→D2→D3). Further examination of the p110/p85 interface using PISA (Proteins, Interfaces, Structures and Assemblies) showed that the dynamics of PI3Kα in the presence of K-Ras4B disrupted the original interactions between p110 and p85. As shown in Table 1, a considerable decline in both the number of salt bridges and interfacial area was observed during the progression of PI3Kα from D1 to D2 and finally to D3 in the

oo f

presence of K-Ras4B. Fewer salt bridges and smaller interfacial area indicated weaker interactions between p110 and p85, which may contribute to the final detachment of

pr

p85 from p110 and the exposure of functional domains in p110. This was consistent with the previous findings that activation of PI3Kα requires to unmask the kinase

e-

domain of p110 [65,66]. Meanwhile, for the dominant conformer D0 in the free

Pr

PI3Kα system, the interaction network in the p110/p85 interface in the D0 was similar to that in the D1 of K-Ras4B-bound PI3Kα, further demonstrating the structural

al

similarities between the two conformers and supporting the role of K-Ras4B in

rn

initiating conformational transitions of PI3Kα.

Jo u

Table 1: Summary of salt bridge numbers along the p110/p85 interface and the interfacial area in dominant conformers in the free PI3Kα (D0) and K-Ras4B-bound PI3Kα systems (D1-D3)

Salt bridges Interfacial area (Å2 )

D0 58 4355.2

D1 54 4168.1

D2 51 4009.5

D3 49 3788.4

3.3 Binding free energy analysis In order to quantify the influences of PI3Kα exerted by K-Ras4B on energetic terms, Molecular Mechanics Poisson-Boltzmann Surface Area (MM/PBSA) was employed to compute the binding free energy (∆Gbinding ) between p85 and p110 in both PI3Kα and PI3Kα/K-Ras4B systems. As shown in Table 2, the values of ∆Gbinding 15

Journal Pre-proof in the free PI3Kα system and K-Ras4B-bound PI3Kα were -204.07±26.84 kcal/mol and

-188.19±22.87 kcal/mol, respectively. The increase in ∆Gbinding by

15.89kcal/mol upon K-Ras4B binding indicated that the interactions between p110 and p85 in the PI3Kα/K-Ras4B complex were much weaker than that in the free PI3Kα system, and the p110/p85 interface in the PI3Kα/K-Ras4B complex was energetically unfavored. The weakened p110/p85 interactions in the presence of K-Ras4B could facilitate the separation of two chains and the relieve of PI3Kα

oo f

autoinhibition. A more detailed analysis of energetic contributions showed that the differences in ∆Gbinding caused by K-Ras4B mainly resulted from the decreased van

pr

der Waals forces and electrostatic interactions, which reflected the altered intermolecular interaction patterns between residues along the p110/p85 interface

Pr

e-

upon K-Ras4B binding.

Table 2: Free energy analysis (kcal/mol) between p85 and p110 in the PI3Kα and

rn

PI3Kα -404.53 (19.49) -2017.54 (122.62) 2280.26 (113.40) -62.26 (2.75) -2422.07 (120.99) 2217.99 (112.65) -204.07 (26.84)

Jo u

∆EvdW ∆Eele ∆EGB ∆Esurf ∆Ggas ∆Gsolv ∆Gbinding

al

PI3Kα/K-Ras4B systems*

PI3Kα/K-Ras4B -398.33 (16.74) -2010.51 (127.61) 2280.56 (120.52) -59.91 (2.08) -2408.84 (130.72) 2220.66 (125.30) -188.19 (22.87)

* numbers in the parentheses represent standard deviations; ∆EvdW: van der Waals force energy contribution; ∆Eele : electrostatic force energy contribution; ∆EGB for the Generalized-Born contribution;

∆Esurf

for solvent-accessible surface energy

contribution; ∆Ggas for gas free energy contribution; ∆Gsolv for solvation energy contribution; ∆Gbinding for binding free energy.

3.4 Insights into the p110/p85 interfacial residues Structural comparisons and free energy analysis revealed that K-Ras4B binding 16

Journal Pre-proof would alter the structural assemblies of PI3Kα as well as disrupt the p110/p85 interaction. We then set out to explore the detailed interaction patterns underlying this conformational change along the interface and mainly focused on nSH2 in p85 and its neighboring functional domains in p110 including C2, helical and kinase domains. In addition, given its role as the key constituent in the kinase activity of PI3Kα, special attention was also paid to the dynamics of the kinase domain. It has been established that in the p110/p85 interface, the C2, helical and kinase

oo f

domains of p110 contact mainly with the nSH2 of p85 (Figure 5A), and through their interplay, p85 can tightly dock on p110, further exerting its inhibitory effects [40].

pr

During our aMD simulation process, we found considerably decreased inter-domain surface area (Table 3) as well as altered interaction schemes between nSH2 and its

e-

nearby domains in the PI3Kα upon K-Ras4B binding. They would ultimately result in

Pr

the weak interplay between p110 and p85, and contributed to the exposure of kinase domain and the activation of PI3Kα. Moreover, we also observed dynamic changes

al

within the kinase domain upon K-Ras4B binding detailly discussed hereafter, which

Jo u

rn

may also promote PI3Kα activation.

17

Pr

e-

pr

oo f

Journal Pre-proof

Figure 5. (A) nSH2 as a core area for C2, helical and kinase domain attachment. (B)

al

Kinase/nSH2 interface. (C) Helical/nSH2 interface. (C) C2/nSH2 interface.

Jo u

rn

Table 3. Interfacial area (Å2 ) between kinase/helical/C2 and nSH2 domain Kinase/ nSH2 interface Helical/ nSH2 interface C2/ nSH2 interface

3.4.1

PI3Kα 408.0 621.2 445.0

PI3Kα/K-Ras4B 240.6 540.7 309.3

Difference 167.4 80.5 135.7

Interface between nSH2 and its neighboring domains

The binding of K-Ras4B disrupted the interface between nSH2 in p85 and the three neighboring functional domains (C2, helical and kinase domains) in p110. Consistent with previous interaction analysis (Table 3) that the kinase/nSH2 interface showed the maximum difference of 167.4 Å2 in interfacial area between the two systems, their interactions were also the most weakest upon K-Ras4B binding (Figure 6). When bound to K-Ras4B, nSH2 underwent remarkable movement (Figure 6A).

18

Journal Pre-proof Particularly, R348nSH2 was shifted away from D1017kinase for 6.1Å, resulting in the loss of interactions between them. Moreover, the salt bridges between K948 kinase and E342nSH2 or E345nSH2 displayed similar changes and their interactions were also compromised during simulations (Table S1). Interestingly, it should be noted that R348nSH2 , E342nSH2 , and E345nSH2 are located at the same helix and disruption of their interactions with the kinase domain in p110 directly led to prominent displacement of

Jo u

rn

al

Pr

e-

pr

oo f

the helix structure for 2.7 Å (Figure 5B).

Figure 6. Detailed interaction patterns between nSH2 and its neighboring domains. (A) Interaction between kinase domain and nSH2. (B) Interaction between helical domain and nSH2. (C) Interaction between C2 domain and nSH2. Residues from p110 are colored pink and residues from p85 are colored cyan. Salt bridges are indicated with dotted lines.

For the interplay between the helical and nSH2 domains, there were constantly six salt bridges in the free PI3Kα systems (Figure 6B), two are formed between

19

Journal Pre-proof E547helical and K382nSH2 , and the remaining four are between E542helical, a hotspot for oncogenic charge reversal mutations, and its neighboring residues R358 nSH2 and S361nSH2 (Table S1). The six salt bridges cooperatively stabilized the helical/nSH2 interface, further contributing to the autoinhibition originating from the interactions between p110 and p85. Nevertheless, upon K-Ras4B binding (Figure 6B), the interfacial area significantly declined to 540.7Å2 (Table 3), which was mainly due to the counterclockwise rotation of E542helical, disrupting the salt bridge between

oo f

E542helical and R358nSH2 . Meanwhile, the side chain of K382nSH2 twisted ~180∘ counterclockwise, disrupting its original interactions with E547helical. The reduction in

pr

both the number and the strength of salt bridges along the helical- nSH2 interface destabilized their original interactions, and eventually led to decreased interface area

e-

and increased average distance between two domains for 7.3Å (Figure 5C).

Pr

The interaction network along the C2/nSH2 interface also exhibited considerable changes upon K-Ras4B binding (Figure 6C) and their inter-domain distance has

al

increased for 5.4Å (Figure 5D). The original interactions included four salt bridges between E453C2 and R348nSH2 , four salt bridges between D454C2 and R373n SH2 and

rn

one salt bridge between D454C2 and D349n SH2 . They all displayed relatively high

Jo u

stability (Table S1) and stabilized the interactions between C2 and nSH2. The movement of the helix on which R348 nSH2 and R349nSH2 located upon K-Ras4B binding induced them to shift away from E453 C2 and D454C2 , leading to the loss of the original interactions. Meanwhile, the side chain of R348nSH2 projected toward D454C2 and made two unstable salt bridges (Table S1). Moreover, R373 nSH2 also moved 5.5 Å away, completely losing interaction with C2. The conformational changes aforementioned significantly attenuated the C2-nSH2 interactions, especially affecting the residues around E453 C2 . Previous studies have revealed the critical role of E453C2 and its salt bridge interaction with R574 iSH2 in the stabilization of the p110/p85 interface. Under normal circumstances, this salt bridge was buried by the adjacent residues and protected from solvent attack, but it would be dramatically

20

Journal Pre-proof affected upon K-Ras4B binding, further resulting in the disruption of the interactions between p110 and p85 and the subsequent abnormal activation of PI3Kα. 3.4.2

Dynamics of the kinase domain

The kinase domain of p110 has been recognized as an essential part for PI3Kα to associate with membrane and phosphorylate its substrates. Given its key functions, we thus examined the dynamics of the kinase domain throughout aMD simulations, and observed differential dynamics of the kinase domain in the free PI3Kα and

oo f

PI3Kα/K-Ras4B systems. As shown in Figure 7A and B, K-Ras4B binding triggered the prominent rotation of helix H940kinase-F945kinase, which induced the formation of a new salt bridges between K941 kinase and D464iSH2 as well as stabilized the original

pr

ones between K944kinase and D464iSH2 (Table S1). K942kinase (highlighted in hotpink,

e-

Figure 7) in helix H940kinase-F945kinase was reported as the key mediator for the

Pr

membrane association of PI3Kα, and the aforementioned salt bridge interactions together twisted the helix structure, and exposed the K942 kinase residue, thereby better

al

facilitating the relocation of PI3Kα along the membrane and enabling the aberrant PI3Kα signal transductions. Meanwhile, other important residues mediating

rn

membrane association such as E726 kinase and H1047kinase also showed considerable

Jo u

increase in their solvent accessible surface area (SASA) upon K-Ras4B binding (Table 4), predisposing PI3Kα to a state prone to membrane attachment [40], which was an indispensable step in PI3Kα full activation. For the kinase domain as a whole, more residues became exposed and extended outwards in the PI3Kα/K-Ras4B system (Table S2), contributing to the increase in total of SASA. This might due to the partial displacement of p85 and the higher flexibility within the kinase domain induced by K-Ras4B binding, demonstrated in our RMSF analysis (Figure 3E). Moreover, larger SASA may enable residues to associate with the membrane and bind substrates, which is the prerequisite for the catalytic phosphorylation process mediated by the kinase domain.

21

al

Pr

e-

pr

oo f

Journal Pre-proof

rn

Figure 7. Dynamics of the kinase domain. Detailed interactions between kinase and

Jo u

iSH2 domain in (A) the free PI3Kα and (B) PI3Kα/K-Ras4B systems. Salt bridges are indicated with dotted lines. Surface diagram of kinase domain with key residues E726, K942 and H1047 highlighted in hotpink in (C) the free PI3Kα and (D) PI3Kα/K-Ras4B systems. Kinase domain is colored yellow with the remaining domains in p110 colored light pink. p85 is colored cyan. Table 4. Solvent accessible surface area (Å2 ) of key residues in the kinase domain of p110 in the free PI3Kα and PI3Kα/K-Ras4B systems Residue E726kinase K942kinase H1047kinase

PI3Kα 52.57 29.79 14.12

PI3Kα/K-Ras4B 94.51 68.87 18.93

Difference 41.94 39.08 4.81

22

Journal Pre-proof 3.5 Community network analysis and allosteric pathway 3.5.1 Community network analysis We next explored the allosteric network in the two simulation systems with the help of community and pathway analyses. Residues within the cut-off distance of 4.5 Å for at least 75% of the aMD trajectory were categorized into the same community, which could be regarded as a synergistic functional unit within the protein structure. On the other hand, the information flow or communicational signaling between

oo f

communities was calculated based on graph theory and topology. It was defined as intercommunity edge connectivity, and was positively correlated with the strength of

pr

crosstalk between communities. To visualize the overall network, residues within the same community were illustrated with identical color, and we further simplified

e-

community network as colored circles connected by sticks of different thickness

Pr

proportional to the value of edge connectivity (Figure 8). There were 11 communities of PI3Kα in both systems and details on community configurations in the free PI3Kα

al

and PI3Kα/K-Ras4B systems and their domain compositions were shown in Figure S1 and Table S3. Consistent with their relatively small RMSD values, the general

rn

distribution of all 11 communities in the two systems shared considerable similarity,

Jo u

but some prominent differences still presented in the residual constitution of communities located at the p110/p85 interface. In the free PI3Kα system (Figure 8A), residues within the p110-p85 interface mainly constituted communities such as C, E, G, H, I and J. This indicated that a portion of residues along the p110/p85 interface stayed in great proximity throughout simulation, supporting the autoinhibition exerted by p85 on p110. On the contrary, upon K-Ras4B binding (Figure 8B), none of the 11 communities incorporated residues from both p110 and p85 (Table S3), implying the increased distance and decreased interactions along the interface. The relocation of residues in communities resided on the interface was in line with our previous structural analysis results that there was a prominent displacement of both nSH2 and iSH2 in p85 from their neighboring functional domains in p110.

23

rn

al

Pr

e-

pr

oo f

Journal Pre-proof

Jo u

Figure 8. Map of community network in (A) PI3Kα and (B) PI3Kα/K-Ras4B. Each community was represented by a circle whose area was in direct proportion to the residual number within community. Intercommunity connection was visualized by stick, the thickness of which was proportional to the value of edge connection.

More significantly, the intercommunity edge connections which denoted the interplay between communities changed considerably before and after K-Ras4B binding (Figure 8). We first focused on the community crosstalk along the p110 and p85 interface. In both PI3Kα and PI3Kα/K-Ras4B system, Community J covered the majority of the residues from nSH2 domains, while Community C was mainly composed of residues from the kinase domain. Upon K-Ras4B binding, the original strong connection between Community J and Community C was lost, which was 24

Journal Pre-proof consistent with the weak kinase-nSH2 interaction discussed previously. Moreover, Community E, constituted primarily of residues from the helical domain, became loosely connected with Community J upon K-Ras4B binding, which could also be explained by the greatly attenuated interplay between nSH2 and helical domains. Additionally, the direct information flow between Community I and Co mmunity C, which represented the crosstalk between iSH2 and kinase domains in PI3Kα, completely disappeared upon K-Ras4B binding. This might result from the

oo f

uncovering of p85 from the kinase domain, and underlay the full activation of PI3Kα. We next traced upward and quested whether it was the binding of K-Ras4B that

pr

triggered the aforementioned changes. We found that the direct interaction between RBD and K-Ras4B greatly altered the original potent information flow between

e-

Community A (mainly residues from RBD) and Community F (mainly residues from

Pr

helical domain), which was the only signal outflow of considerable strength from Community A. Nevertheless, the connection between Community A and Community

al

F was greatly attenuated upon K-Ras4B binding, accompanied by the significantly enhanced interaction between Community A and Community C. Meanwhile, the

rn

emerging strong edge connection between Community C and Community H may

Jo u

significantly reinforce interaction between kinase and helical domains, which could also be interpreted as a more compact assembly of functional domains within p110. Overall, the association of PI3Kα with K-Ras4B would affect the internal connection patterns within p110, leading to an altered communicational signaling between communities, as well as weakening the interfacial interactions between p110 and p85, which could contribute to the elimination of PI3K autoinhibition. 3.5.2 Allosteric pathway analysis We next quested how the signals from K-Ras4B binding could allosterically regulate the distal interaction patterns between p110 and p85 within PI3Kα. Previous studies from Nussinov et al. had demonstrated the critical roles of K206, T208, L209, K210, K227 and R230 within RBD in receiving signals from K-Ras4B interaction. To

25

Journal Pre-proof explore the signal propagation pathways within PI3Kα upon K-Ras4B binding, we analyzed the allosteric pathways from the six key residues in RBD down to t he important interfacial residues mediating the p110-p85 interaction uncovered previously (E453C2 , D454C2 , E542helical, E547helical, K948kinase and D1017kinase) using the NetworkView plugin in VMD tools. The reductionist scheme of the allosteric

Jo u

rn

al

Pr

e-

pr

oo f

pathways in terms of community was depicted in Figure 9.

Figure 9. Reductionist scheme of allosteric pathway between signal-receiving residues in RBD and important p110/p85 interfacial residues in C2 domain, helical domain, and kinase domain in the free PI3Kα (A, C, E) and PI3Kα/K-Ras4B (B, D, F) systems.

Figure 9A, 9C, 9E displayed the original signal propagation pattern the free PI3K system. Significantly, Community F, mainly composed of residues from the helical domain, served as the only route through which allosteric signals from Community A passed to the downstream communities resided on the interface. This was in accordance with our community interaction analyses scheme in which only the information outflow from Community A to Community F was of substantive strength 26

Journal Pre-proof in the free PI3K system. Nevertheless, the allosteric communication hub rewired from Community F to Community C upon K-Ras4B binding (Figure 9B, 9D, 9F), bypassing Community F the signal transduction. Therefore, we might hypothesize that a portion of the helical domain within Community F played an important role in the allosteric network within PI3Kα and was implicated in its autoinhibition, while the kinase domain, which is the main component of Community C, was cr itical towards the activation of PI3Kα induced by K-Ras4B binding. Moreover, upon

oo f

interacting with R41 and S39 in K-Ras4B, K206 and T208 in PI3Kα completely lost their involvements within the original allosteric pathways, which considerably

pr

hampered their normal regulatory functions, contributed to the weak interaction between p110 and p85, and stimulated the activation of PI3Kα. Harnessing allostery

e-

to tailor critical PPIs have already entered the main stream of modern drug discovery

Pr

[53, 67-69], and the allosteric pathways and the residual nodes for signal transduction revealed here not only supplied in-depth insights into the allosteric regulation of

al

K-Ras4B interaction on the PI3Kα, but also constituted the theoretical basis for future

Jo u

4. Discussion

rn

related allosteric drug design.

As the hub for intracellular signaling, the lipid kinase PI3Kα plays an important role in a variety of cellular activities, including cell growth, migration and survival [1–8]. Overactivation of PI3Kα is closely related to a series of pathological processes such as tumorigenesis [70], and it thus has long been regarded as a promising target for drug design [14]. In the case of the fatal PDAC, hyperactivation of PI3Kα mainly results from the abnormal interactions between PI3Kα and the oncogenic K-Ras4B, through which process persistent pro-growth signals are transduced and lead to malignancy [31–34]. Although there had been attempts to solve the interaction patterns between PI3Kα and K-Ras4B by means of cMD, the relatively low sampling efficiency and the complexity within the PI3Kα/K-Ras4B hindered a more profound

27

Journal Pre-proof understanding towards the system. leaving the detailed atomic mechanism underlying the activation of PI3Kα by K-Ras4B remain largely unresolved. Previous studies have demonstrated the self- inhibition within PI3Kα mainly originated from the regulatory p85 subunit, and the dissociation of p85 from the catalytic p110 subunit was an essential prerequisite for the activation of PI3Kα [23,24]. Herein, we performed 500 ns aMD simulations on the apo PI3K and PI3Kα/K-Ras4B systems to investigate the structural mechanisms underlying the

oo f

allosteric activation of PI3Kα by oncoprotein K-Ras4B. We found that high flexibility within PI3Kα was induced upon K-Ras4B binding, especially in the C2, helical and

pr

kinase domains which had previously been reported to contribute to the autoinhibitory state of PI3Kα, while the binding free energy calculations also suggested a less stable

e-

complex in the PI3Kα/K-Ras4B system, and this may contribute to the stepwise

Pr

conformational transitions of PI3Kα to the more active form. Important p110/p85 interfacial residues including E453C2 , D454C2 , E542helical, E547helical, K948kinase and

al

D1017kinase exhibited considerable changes in their corresponding interaction networks in the presence K-Ras4B, which weakened the interactions between nSH2

rn

and its neighboring domains in p110, further alleviating the inhibition on p110

Jo u

imposed by p85 subunit. Meanwhile, we also observed increased SASA and dynamics within the kinase domain, especially for the residues which had been reported to mediate the membrane association of PI3Kα such as E726kinase, K942kinase and H1047kinase [40]. This could be attributed to the overall structural displacements of both nSH2 and iSH2 from p110, and the resulting exposure of the kinase domain would facilitate the full activation of PI3Kα. The results from community network analysis suggested an altered community constitution and inter-community crosstalk triggered by K-Ras4B, in which no community contained residues from both p110 and p85 was present, indicating decreased interplay between the two subunits. Moreover, through performing allosteric pathway analysis, we found that the interaction between PI3K and K-Ras4B greatly attenuated the original strong

28

Journal Pre-proof intramolecular allosteric information flow from RBD to helical domain. As residues from the helical domain are major components in sustaining p110/p85 interactions, this altered inter-community crosstalk may be a reason accounting for the reorientation of interfacial residues in the helical domain that resulted in the less stable interface in the presence of K-Ras4B. Meanwhile, the kinase domain became more actively engaged in the mechanistic signaling transductions, which may be implicated in the membrane association process of activated PI3Kα. These results

oo f

together indicated the pivotal role of the helical domain in the maintenance of the autoinhibitory state of PI3Kα, as well as highlighted the critical role of kinase domain

pr

in PI3Kα activation.

e-

5. Conclusion

In summary, our computational investigation of the activation of PI3Kα by

Pr

K-Ras4B proposed a possible allosteric mechanism in

atomic level where the

signals from K-Ras4B/RBD interface propagated to the p110/p85 interface through

al

the kinase domain and disrupted the interfacial stability through inducing overall

rn

structural displacement of both nSH2 and iSH2 from their neighboring functional

Jo u

domains including C2, helical and kinase domain in p110. Residues along the interface, including E453C2 , D454C2 , E542helical, E547helical, K948kinase and D1017kinase were identified and their roles in the maintaining of autoinhibition as well as in the activation of PI3Kα were characterized. The results in our study shed light on the structural details of the PI3Kα allosteric activation and were instructive to the related pathophysiological research on PDAC. On the other hand, the important residues along the p110/p85 interface identified in our study and their related regulatory allosteric pathways would be promising pharmacological targets in future cancer therapy, especially providing a theoretical basis for further therapeutic agent development.

29

Journal Pre-proof Acknowledge ments: This work was supported by National Natural Science Foundation of China (21778037). Author contributions: Shaoyong Lu, Jian Zhang and Xinyi Li conceived and designed the experiments; Xinyi Li and Duan Ni performed the experiments; Xinyi Li, Jinyuan Dai, Duan Ni, Xinheng He and Hao Zhang analyzed the data; Shaoyong Lu, Jian Zhang, Qiang Fu and Yaqin Liu contributed reagents/materials/analysis tools: All authors wrote the paper.

oo f

Conflicts of Interest: The authors declare no conflict of interest.

Reference

4.

5. 6. 7.

8. 9.

10.

pr

e-

Pr

al

3.

rn

2.

Downward, J. The ras superfamily of small GTP-binding proteins. Trends Biochem. Sci. 1990; 15(12):469-472. Lu, S.; Jang, H.; Gu, S.; Zhang, J.; Nussinov, R. Drugging Ras GTPase: A comprehensive mechanistic and signaling structural view. Chem. Soc. Rev. 2016, 45(18):4929-4952. Bourne, H.R.; Sanders, D.A.; McCormick, F. The GTPase superfamily: conserved structure and molecular mechanism. Nature 1991, 349(6305):117– 127. Ni, D.; Song, K.; Zhang, J.; Lu, S. Molecular dynamics simulations and dynamic network analysis reveal the allosteric unbinding of monobody to H-Ras triggered by R135K mutation. Int. J. Mol. Sci. 2017, 18(11), E2249. Sheridan, C.; Downward, J. Inhibiting the RAS-PI3K Pathway in Cancer Therapy. In Enzymes; 2013; pp. 34 Pt. B:107-136. Cully, M.; Downward, J. SnapShot: Ras Signaling. Cell 2008, 133(7):1292-1292. De Luca, A.; Maiello, M.R.; D’Alessio, A.; Pergameno, M.; Normanno, N. The RAS/RAF/MEK/ERK and the PI3K/AKT signalling pathways: Role in cancer pathogenesis and implications for therapeutic approaches. Expert Opin. Ther. Targets 2012, 16 Suppl 2:S17-27. Smith, M.J.; Ikura, M. Integrated RAS signaling defined by parallel NMR detection of effectors and regulators. Nat. Chem. Biol. 2014, 10(3):223-230. Knight, Z.A.; Gonzalez, B.; Feldman, M.E.; Zunder, E.R.; Goldenberg, D.D.; Williams, O.; Loewith, R.; Stokoe, D.; Balla, A.; Toth, B.; et al. A Pharmacological Map of the PI3-K Family Defines a Role for p110α in Insulin Signaling. Cell 2006, 125(4):733-747. Margarit, S.M.; Sondermann, H.; Hall, B.E.; Nagar, B.; Hoelz, A.; Pirruccello, M.; Bar-Sagi, D.; Kuriyan, J. Structural evidence for feedback activation by Ras·GTP of the Ras-specific nucleotide exchange factor SOS. Cell 2003, 112(5):685-695.

Jo u

1.

30

Journal Pre-proof

13.

14. 15.

19.

20.

21. 22.

Jo u

18.

rn

al

17.

Pr

e-

16.

oo f

12.

Schweins, T.; Geyer, M.; Scheffzek, K.; Warshel, A.; Kalbitzer, H.R.; Wittinghofer, A. Substrate-assisted catalysis as a mechanism for gtp hydrolysis of p21ras and other GTP-binding proteins. Nat. Struct. Biol. 1995, 2(1):36-44. Lu, S.; Ni, D.; Wang, C.; He, X.; Lin, H.; Wang, Z.; Zhang, J. Deactivation Pathway of Ras GTPase Underlies Conformational Substates as Targets for Drug Design. ACS Catal. 2019, 9:7188–7196. Lu, S.; Banerjee, A.; Jang, H.; Zhang, J.; Gaponenko, V.; Nussinov, R. GTP binding and oncogenic mutations may attenuate hypervariable region (HVR)-catalytic domain interactions in small GTPase K-Ras4B, exposing the effector binding site. J. Biol. Chem. 2015, 290(48), 28887–900. Fruman, D.A.; Chiu, H.; Hopkins, B.D.; Bagrodia, S.; Cantley, L.C.; Abraham, R.T. The PI3K Pathway in Human Disease. Cell 2017, 170(4):605-635. Fan, Q.-W.; Knight, Z.A.; Goldenberg, D.D.; Yu, W.; Mostov, K.E.; Stokoe, D.; Shokat, K.M.; Weiss, W.A. A dual PI3 kinase/mTOR inhibitor reveals emergent efficacy in glioma. Cancer Cell 2006, 9(5), 341–349. Jiang, W.; Cheng, Y.; Zhao, N.; Li, L.; Shi, Y.; Zong, A.; Wang, F. Sulfated polysaccharide of Sepiella Maindroni ink inhibits the migration, invasion and matrix metalloproteinase-2 expression through suppressing EGFR- mediated p38/MAPK and PI3K/Akt/mTOR signaling pathways in SKOV-3 cells. Int. J. Biol. Macromol. 2018, 107:349-362. Costa, C.; Ebi, H.; Martini, M.; Beausoleil, S.A.; Faber, A.C.; Jakubik, C.T.; Huang, A.; Wang, Y.; Nishtala, M.; Hall, B.; et al. Measurement of PIP3 levels reveals an unexpected role for p110β in Early adaptive responses to p110α-Specific inhibitors in luminal breast cancer. Cancer Cell 2015, 27(1):97-108. Gupta, S.; Ramjaun, A.R.; Haiko, P.; Wang, Y.; Warne, P.H.; Nicke, B.; Nye, E.; Stamp, G.; Alitalo, K.; Downward, J. Binding of Ras to Phosphoinositide 3-Kinase p110α Is Required for Ras- Driven Tumorigenesis in Mice. Cell 2007, 129(5):957-968. Castellano, E.; Sheridan, C.; Thin, M.Z.; Nye, E.; Spencer-Dene, B.; Diefenbacher, M.E.; Moore, C.; Kumar, M.S.; Murillo, M.M.; Grönroos, E.; et al. Requirement for Interaction of PI3-Kinase p110α with RAS in Lung Tumor Maintenance. Cancer Cell 2013, 24(5):617-630. Murillo, M.M.; Zelenay, S.; Nye, E.; Castellano, E.; Lassailly, F.; Stamp, G.; Downward, J. RAS interaction with PI3K p110α is required for tumor- induced angiogenesis. J. Clin. Invest. 2014, 124(8):3601-3611. Dibble, C.C.; Cantley, L.C. Regulation of mTORC1 by PI3K signaling. Trends Cell Biol. 2015, 25(9):545-555. Zhao, Y.; Zhang, X.; Chen, Y.; Lu, S.; Peng, Y.; Wang, X.; Guo, C.; Zhou, A.; Zhang, J.; Luo, Y.; et al. Crystal structures of PI3Kα complexed with PI103 and its derivatives: New directions for inhibitors design. ACS Med. Chem. Lett. 2014, 5(2):138-142.

pr

11.

31

Journal Pre-proof

27.

28.

31.

32.

33.

34.

Jo u

30.

rn

al

29.

oo f

26.

pr

25.

e-

24.

Thorpe, L.M.; Spangle, J.M.; Ohlson, C.E.; Cheng, H.; Roberts, T.M.; Cantley, L.C.; Zhao, J.J. PI3K-p110α mediates the oncogenic activity induced by loss of the novel tumor suppressor PI3K-p85α. Proc. Natl. Acad. Sci. U. S. A. 2017, 114(27):7095-7100. Vadas, O.; Burke, J.E.; Zhang, X.; Berndt, A.; Williams, R.L. Structural biology structural basis for activation and inhibition of class I phosphoinositide 3-kinases. Sci. Signal. 2011, 4(195):re2. Zhou, S.; Shoelson, S.E.; Chaudhuri, M.; Gish, G.; Pawson, T.; Haser, W.G.; King, F.; Roberts, T.; Ratnofsky, S.; Lechleider, R.J.; et al. SH2 domains recognize specific phosphopeptide sequences. Cell 1993, 72(5):767-778. Weber, T.; Schaffhausen, B.; Liu, Y.; Günther, U.L. NMR structure of the N-SH2 of the p85 subunit of phosphoinositide 3-kinase complexed to a doubly phosphorylated peptide reveals a second phosphotyrosine binding site. Biochemistry 2000, 39(51):15860-15869. Yu, J.; Wjasow, C.; Backer, J.M. Regulation of the p85/p110alpha phosphatidylinositol 3’-kinase. Distinct roles for the n-terminal and c-terminal SH2 domains. J. Biol. Chem. 1998, 273(46), 30199–30203. Balmaña, M.; Duran, A.; Gomes, C.; Llop, E.; López-Martos, R.; Ortiz, M.R.; Barrabés, S.; Reis, C.A.; Peracaula, R. Analysis of sialyl-Lewis x on MUC5AC and MUC1 mucins in pancreatic cancer tissues. Int. J. Biol. Macromol. 2018, 112:33–45. Jiang, S.H.; Li, J.; Dong, F.Y.; Yang, J.Y.; Liu, D.J.; Yang, X.M.; Wang, Y.H.; Yang, M.W.; Fu, X.L.; Zhang, X.X.; et al. Increased Serotonin Signaling Contributes to the Warburg Effect in Pancreatic Tumor Cells Under Metabolic Stress and Promotes Growth of Pancreatic Tumors in Mice. Gastroenterology 2017, 153(1):277-291. Ebrahimi, S.; Hosseini, M.; Shahidsales, S.; Maftouh, M.; Ferns, G.A.; Ghayour-Mobarhan, M.; Hassanian, S.M.; Avan, A. Targeting the Akt/PI3K Signaling Pathway as a Potential Therapeutic Strategy for the Treatment of Pancreatic Cancer. Curr. Med. Chem. 2017, 24(13), 1321–1331. Eser, S.; Reiff, N.; Messer, M.; Seidler, B.; Gottschalk, K.; Dobler, M.; Hieber, M.; Arbeiter, A.; Klein, S.; Kong, B.; et al. Selective Requirement of PI3K/PDK1 Signaling for Kras Oncogene-Driven Pancreatic Cell Plasticity and Cancer. Cancer Cell 2013, 23(3), 406–420. Baer, R.; Pyronnet, S.; Guillermet-Guibert, J. Increase in PI3K signalling mimics mutated-Kras induction of pancreatic cancer. Clin. Res. Hepatol. Gastroenterol. 2013, 37(4), 320–321. Muzumdar, M.D.; Chen, P.Y.; Dorans, K.J.; Chung, K.M.; Bhutkar, A.; Hong, E.; Noll, E.M.; Sprick, M.R.; Trumpp, A.; Jacks, T. Survival of pancreatic cancer cells lacking KRAS function. Nat. Commun. 2017, 8(1):1090. Prior, I.A.; Lewis, P.D.; Mattos, C. A comprehensive survey of ras mutations in cancer. Cancer Res. 2012, 72(10):2457-2467.

Pr

23.

32

Journal Pre-proof

40. 41.

42.

44.

45. 46.

47.

48.

Jo u

rn

43.

oo f

39.

pr

38.

e-

37.

Pr

36.

Bryant, K.L.; Mancias, J.D.; Kimmelman, A.C.; Der, C.J. KRAS: Feeding pancreatic cancer proliferation. Trends Biochem. Sci. 2014, 39(2):91-100. Ying, H.; Dey, P.; Yao, W.; Kimmelman, A.C.; Draetta, G.F.; Maitra, A.; Depinho, R.A. Genetics and biology of pancreatic ductal adenocarcinoma. Genes Dev. 2016, 30(4):355-385. Ying, H.; Kimmelman, A.C.; Lyssiotis, C.A.; Hua, S.; Chu, G.C.; Fletcher-Sananikone, E.; Locasale, J.W.; Son, J.; Zhang, H.; Coloff, J.L.; et al. Oncogenic kras maintains pancreatic tumors through regulation of anabolic glucose metabolism. Cell 2012, 149(3):656-670. Ni, D.; Liu, D.; Zhang, J.; Lu, S. Computational Insights into the Interactions between Calmodulin and the c/nSH2 Domains of p85α Regulatory Subunit of PI3Kα: Implication for PI3Kα Activation by Calmodulin. Int. J. Mol. Sci. 2018, 19(1):E151. Lu, S.; Jang, H.; Muratcioglu, S.; Gursoy, A.; Keskin, O.; Nussinov, R.; Zhang, J. Ras Conformational Ensembles, Allostery, and Signaling. Chem. Rev. 2016, 116(11):6607-6665. Zhang, M.; Jang, H.; Nussinov, R. The structural basis for Ras activation of PI3Kα lipid kinase. Phys. Chem. Chem. Phys. 2019, 21(22):12021-12028. Ostrem, J.M.; Peters, U.; Sos, M.L.; Wells, J.A.; Shokat, K.M. K-Ras(G12C) inhibitors allosterically control GTP affinity and effector interactions. Nature 2013, 503(7477):548-551. Cherfils, J.; Zeghouf, M. Regulation of small GTPases by GEFs, GAPs, and GDIs. Physiol. Rev. 2013, 93(1):269-309. Gkeka, P.; Evangelidis, T.; Pavlaki, M.; Lazani, V.; Christoforidis, S.; Agianian, B.; Cournia, Z. Investigating the Structure and Dynamics of the PIK3CA Wild- Type and H1047R Oncogenic Mutant. PLoS Comput. Biol. 2014, 10(10):e1003895. Leontiadou, H.; Galdadas, I.; Athanasiou, C.; Cournia, Z. Insights into the mechanism of the PIK3CA E545K activating mutation using MD simulations. Sci. Rep. 2018, 8(1):15544. Zhang, M.; Jang, H.; Nussinov, R. The mechanism of PI3Kα activation at the atomic level. Chem. Sci. 2019, 10(12):3671-3680. Ni, D.; Li, X.; He, X.; Zhang, H.; Zhang, J.; Lu, S. Drugging K-RasG12C through covalent inhibitors: Mission possible? Pharmacol. Ther. 2019, 202:1-17. Pierce, L.C.T.; Salomon-Ferrer, R.; Augusto F. De Oliveira, C.; McCammon, J.A.; Walker, R.C. Routine access to millisecond time scale events with accelerated molecular dynamics. J. Chem. Theory Comput. 2012, 8(9):2997-3002. Hamelberg, D.; Mongan, J.; McCammon, J.A. Accelerated molecular dynamics: A promising and efficient simulation method for biomolecules. J. Chem. Phys. 2004, 120(24):11919-11929.

al

35.

33

Journal Pre-proof

52.

53.

54.

59.

60.

61. 62.

63.

rn

57. 58.

Jo u

56.

al

Pr

55.

oo f

51.

pr

50.

Bhattarai, A.; Miao, Y. Gaussian accelerated molecular dynamics for elucidation of drug pathways. Expert Opin. Drug Discov. 2018, 13(11):1055-1065. Kappel, K.; Miao, Y.; Andrew McCammon, J. Accelerated molecular dynamics simulations of ligand binding to a muscarinic G-protein-coupled receptor. Q. Rev. Biophys. 2015, 48(4):479-487. Wang, Y.-T.; Chan, Y.-H. Understanding the molecular basis of agonist/antagonist mechanism of human mu opioid receptor through gaussian accelerated molecular dynamics method. Sci. Rep. 2017, 7(1), 7828. Nussinov, R.; Wang, G.; Tsai, C.J.; Jang, H.; Lu, S.; Banerjee, A.; Zhang, J.; Gaponenko, V. Calmodulin and PI3K Signaling in KRAS Cancers. Trends in Cancer 2017, 3(3):214-224. Ni, D.; Lu, S.; Zhang, J. Emerging roles of allosteric modulators in the regulation of protein‐ protein interactions (PPIs): A new paradigm for PPI drug discovery. Med. Res. Rev. 2019, 39(6):2314-2342. Liu, X.; Lu, S.; Song, K.; Shen, Q.; Ni, D.; Li, Q.; He, X.; Zhang, H.; Wang, Q.; Chen, Y.; et al. Unraveling allosteric landscapes of allosterome with ASD. Nucleic Acids Res. 2019, doi: 10.1093/nar/gkz958. Pacold, M.E.; Suire, S.; Perisic, O.; Lara-gonzalez, S.; Davis, C.T.; Walker, E.H.; Hawkins, P.T.; Stephens, L.; Eccleston, J.F.; Williams, R.L.; et al. Crystal structure and functional analysis of Ras binding to its effector phosphoinositide 3-kinase gamma. Cell 2000, 103(6):931–943. Zhao, Y.; Zhang, X.; Chen, Y.; Lu, S.; Peng, Y.; Wang, X.; Guo, C.; Zhou, A.; Zhang, J.; Luo, Y.; et al. Crystal structures of PI3Kα complexed with PI103 and its derivatives: New directions for inhibitors design. ACS Med. Chem. Lett. 2014, 5(2):138-142. Stockwell, G. PyMOL tutorial. Biochemistry 2003, 42:225–232. Miyata, T. Discovery studio modeling environment. Ensemble 2015, 17(2):98-104. Brooks, B.R.; Brooks, C.L.; Mackerell, A.D.; Nilsson, L.; Petrella, R.J.; Roux, B.; Won, Y.; Archontis, G.; Bartels, C.; Boresch, S.; et al. CHARMM: The biomolecular simulation program. J. Comput. Chem. 2009, 30(10):1545-1614. Case, D.A.; Cheatham, T.E.; Darden, T.; Gohlke, H.; Luo, R.; Merz, K.M.; Onufriev, A.; Simmerling, C.; Wang, B.; Woods, R.J. The Amber biomolecular simulation programs. J. Comput. Chem. 2005, 26(16):1668-1688. Mark, P.; Nilsson, L. Structure and dynamics of the TIP3P, SPC, and SPC/E water models at 298 K. J. Phys. Chem. A 2001, 105(43):9954-9960. Jorgensen, W.L.; Chandrasekhar, J.; Madura, J.D.; Impey, R.W.; Klein, M.L. Comparison of simple potential functions for simulating liquid water. J. Chem. Phys. 1983, 79(2):926-935. Bucher, D.; Pierce, L.C.T.; McCammon, J.A.; Markwick, P.R.L. On the use of accelerated molecular dynamics to enhance configurational sampling in Ab

e-

49.

34

Journal Pre-proof

70.

oo f

pr

69.

e-

68.

Pr

67.

al

66.

rn

65.

Jo u

64.

initio simulations. J. Chem. Theory Comput. 2011, 7(4):890-897. Wang, L.; Zheng, G.; Liu, X.; Ni, D.; He, X.; Chen, J.; Lu, S. Molecular dynamics simulations provide insights into the origin of gleevec's selectivity toward human tyrosine kinases. J. Biomol. Struct. Dyn. 2019, 37(10):2733-2744. Burke, J.E.; Perisic, O.; Masson, G.R.; Vadas, O.; Williams, R.L. Oncogenic mutations mimic and enhance dynamic events in the natural activation of phosphoinositide 3-kinase p110α (PIK3CA). Proc. Natl. Acad. Sci. U. S. A. 2012, 109(38):15259-15264. Liu, P.; Gan, W.; Chin, Y.R.; Ogura, K.; Guo, J.; Zhang, J.; Wang, B.; Blenis, J.; Cantley, L.C.; Toker, A.; et al. Ptdins(3,4,5) P3 -dependent activation of the mTORC2 kinase complex. Cancer Discov. 2015, 5(11):1194-1209. Lu, S.; Zhang, J. Small molecule allosteric modulators of G-protein-coupled receptors: drug-target interactions. J. Med. Chem. 2019, 62, 24-45. Lu, S.; He, X.; Ni, D.; Zhang, J. Allosteric modulator discovery: from serendipity to structure-based design. J. Med. Chem. 2019, 62, 6405-6421. Lu, S.; Ji, M.; Bi, D.; Zhang, J. Discovery of hidden allosteric sites as novel targets for allosteric drug design. Drug Discov. Today 2018, 23, 359-365. Lu, S.; Jang, H.; Nussinov, R.; Zhang, J. The structural basis of oncogenic mutations G12, G13 and Q61 in small GTPase K-Ras4B. Sci. Rep. 2016, 6:21949.

35