Single Mutations Reshape the Structural Correlation Network of the

Subscriber access provided by UB + Fachbibliothek Chemie | (FU-Bibliothekssystem)

Article

Single Mutations Reshape the Structural Correlation Network of DMXAA-Human STING Complex Xing Che, Xiao-Xia Du, Xiaoxia Cai, Jun Zhang, Wen Jun Xie, Zhuoran Long, Zhaoyang Ye, Heng Zhang, Lijiang Yang, Xiaodong Su, and Yi Qin Gao J. Phys. Chem. B, Just Accepted Manuscript • DOI: 10.1021/acs.jpcb.6b12472 • Publication Date (Web): 08 Feb 2017 Downloaded from http://pubs.acs.org on February 9, 2017

Just Accepted “Just Accepted” manuscripts have been peer-reviewed and accepted for publication. They are posted online prior to technical editing, formatting for publication and author proofing. The American Chemical Society provides “Just Accepted” as a free service to the research community to expedite the dissemination of scientific material as soon as possible after acceptance. “Just Accepted” manuscripts appear in full in PDF format accompanied by an HTML abstract. “Just Accepted” manuscripts have been fully peer reviewed, but should not be considered the official version of record. They are accessible to all readers and citable by the Digital Object Identifier (DOI®). “Just Accepted” is an optional service offered to authors. Therefore, the “Just Accepted” Web site may not include all articles that will be published in the journal. After a manuscript is technically edited and formatted, it will be removed from the “Just Accepted” Web site and published as an ASAP article. Note that technical editing may introduce minor changes to the manuscript text and/or graphics which could affect content, and all legal disclaimers and ethical guidelines that apply to the journal pertain. ACS cannot be held responsible for errors or consequences arising from the use of information contained in these “Just Accepted” manuscripts.

The Journal of Physical Chemistry B is published by the American Chemical Society. 1155 Sixteenth Street N.W., Washington, DC 20036 Published by American Chemical Society. Copyright © American Chemical Society. However, no copyright claim is made to original U.S. Government works, or works produced by employees of any Commonwealth realm Crown government in the course of their duties.

Page 1 of 29

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

The Journal of Physical Chemistry

Single Mutations Reshape the Structural Correlation Network of DMXAAHuman STING Complex Xing Che1,3, Xiao-Xia Du1,3, Xiaoxia Cai1, Jun Zhang1, Wen Jun Xie1, Zhuoran Long1, Zhao-Yang Ye2, Heng Zhang2, Lijiang Yang1, Xiao-Dong Su2*, Yi Qin Gao1* 1. Institute of Theoretical and Computational Chemistry, College of Chemistry and Molecular Engineering, and Biodynamic Optical Imaging Center, Peking University, Beijing 100871, China 2. State Key Laboratory of Protein and Plant Gene Research, and Biodynamic Optical Imaging Center, School of Life Sciences, Peking University, Beijing 100871, China 3. These authors contribute equally to this work. Correspondence Author: [email protected], [email protected]

ABSTRACT Subtle changes in protein sequence are able to alter ligand-protein interactions. Unraveling the mechanism of such phenomena is important for understanding ligand-protein interaction, including the DMXAA-STING interaction. DMXAA specifically binds to mouse STING instead of human STING. However, the S162A mutation and a newly discovered E260I mutation endow human STINGAQ with DMXAA sensitivity. Through MD simulations, we revealed how these single mutations alter the DMXAA-STING interaction. Compared to mutated systems, structural correlations in the interaction of STINGAQ with DMXAA are stronger and the correlations are cross-protomer in the dimeric protein. Analyses on correlation coefficients lead to the identification of two key interactions that mediate the strong cross-protomer correlation in DMXAA-STINGAQ interaction network: DMXAA-267T-162S* and 238R-260E*. These two interactions are partially and totally interrupted by S162A and E260I mutation, respectively. Moreover, a smaller number of water molecules are displaced upon DMXAA binding to STINGAQ than to its mutants, leading to a larger entropic penalty for the former. Considering the sensitivity of STINGAQ and two of its mutants to DMXAA, a strong structural correlation appears discourage DMXAA-STING binding. Such an observation suggests that DMXAAderivatives which are deprived of hydrogen bond interaction with both 162S* and 267T are potential agonists of human STING.

1

ACS Paragon Plus Environment


1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

Page 2 of 29

Introduction STING (stimulator of interferon genes, also known as TMEM173, MPYS, MITA and ERIS) is a vital protein involved in immune defenses to cytosolic double strand DNA 1. Upon binding with endogenous cyclic dinucleotides (CDNs)2, STING translocates and activates TANK binding kinase (TBK1) and IkB kinase (IKK). TBK1 and IKK then phosphorylate and activate interferon regulatory factor 3 (IRF3) and nuclear factor kappa B (NF-κB), respectively, which then trigger type I interferon (IFN) response 3-6. Through dimerization, the carboxyl-terminal domain of STING, consisting of about 250 residues, constitutes a binding pocket. The homo-dimer of CTD STING takes the shape of a pair of wings. Upon binding, the dimer transforms from an open to a closed state. The distance between the apexes of two wings is shortened by about 20Å 7-9. Besides binding with endogenous second messengers, mouse STING also responds to drug-like compounds—CMA (10-carboxymethyl-9-acridanone)10 and DMXAA (5,6-dimethylxanthenone-4acetic acid, Vadimezan, AS404). The xanthenone derivative compound DMXAA shows an antivascular effect to tumors and induces cytokine production in mice11. However, combined with firstline platinum-based chemotherapy, DMXAA failed in the clinical trial of advanced non-small-cell lung cancer (NSCLC)12. Further investigation revealed that mouse STING (mSTING) but not human STING (hSTING) shows a response to DMXAA13, which gives an explanation to the phase III clinical trial failure. Recent study discovered that hSTINGS162A, a single point mutation in hSITNGH232 and hSTINGR232, can be activated by DMXAA and induces IFN-β production8. Later, G230I and Q266I have been identified to render hSTING with DMXAA sensitivity through sequence comparison between mSTING and hSTING14. According to the crystal structure of DMXAA-hSTINGG230I (pdb code: 4QXP), 162S locates deeply in the binding pocket and is speculated not to interfere directly with DMXAA binding, given its size and relative position to DMXAA. Therefore, the mechanism of how such a single mutation S162A alters the sensitivity of human STING to DMXAA is intriguing. In fact, it is very common for proteins similar in sequence and structure to have divergent functions. Either single or multiple point mutations have the capacity to alter protein flexibility, dynamics and, ultimately, function. It has been recognized that dynamics is important in populating available protein conformational states; rates and probabilities of states redistribution upon ligand binding15 and proteins shift conformational populations through cooperative motions while enabling their functions16. For example, in cyclophilin A enzyme-catalyzed reaction, rates of substrate turnover strongly correlate with the rates of conformational dynamics of 2


Page 3 of 29

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


enzyme17-18. It is also known that single mutation could exert influence on the ligand-protein interaction network. For proteins like GPCRs, two structural elements, rigid knobs and flexible regions, compose the allosteric communication network. Such a structure allows the protein to function like a machine, with information transmitted among flexible domains through the rigid knobs19. A diseaseassociated mutation that softens the rigid knobs, leads to flabby dynamics and disrupts dynamic allosteric regulation20. The fact that mutations locating far away from the binding pocket exert influence on ligand binding indicates that conformational transitions are communicated between the mutation and binding sites, resulting in pocket changes in volume and shape 21. In addition, the role of water in ligand binding has long been recognized. Studies have shown that structural water directly mediates ligand-protein interactions and water bridged ligand-protein hydrogen bond interactions have been found important in a number of systems22. Besides, waters around the hydrophobic residues in the binding pocket may lead to a large desolvation penalty23. Water-mediated interactions are also involved in sculpting protein functional landscapes24. Therefore, deciphering the influence of mutated residues on the protein-ligand interaction network, including the surrounding water molecules, will shed light on understanding the response of protein to ligand binding. The complicated energy landscape of ligand-protein complex in conjunction with solvent interactions makes it difficult to observe proteins dynamics at the atomic resolution in real time. Crystal structures provide useful but limited information on protein dynamics. Alternatively, molecular dynamics (MD) simulation becomes a widely-used approach to investigate protein dynamics at the atomic level. MD simulations provide details of motions as a function of time, which are difficult to access experimentally but critical for understanding protein functions 25. In this study, to investigate the influence of a single mutation on STING dynamics, we performed MD simulations on DMXAAhSTINGAQ and DMXAA-hSTINGAQ-S162A complexes. The analyses of the simulation results allowed us to understand the detailed interactions between DMXAA and STING as well as the intra-molecular interactions. Based on such understandings, we designed a new mutation E260I which was subsequently shown to endow hSTINGAQ with DMXAA sensitivity. We then investigated in detail the differences in interactions of STINGAQ and its two mutants through binding free energy calculations and structural correlation analyses. In addition, using the two-phase thermodynamic model, we further compared entropic penalty differences of pocket water in the three systems. Such molecular level understandings are expected to facilitate design of effective DMXAA derivatives.

3



1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

Page 4 of 29

Methods Molecular dynamics simulations The structure of DMXAA was extracted from the crystal structure of DMXAA-STINGAQ-S162A complex. Geometry optimization was performed using Gaussian26 on 6-311G* basis set using B3LYP. HF/6–31G* level of theory was used in partial charge calculation. The force filed parameters were obtained by ANTECHAMBER, using the general atom force field and RESP procedure to fit the point charges 27. STINGAQ and its two mutants, STINGAQ-S162A and STINGAQ-E260I, were simulated in this work. The simulation models of DMXAA-STINGAQ and DMXAA-STINGAQ-E260I complexes were built through replacing the corresponding residues of the DMXAA-STINGAQ-S162A crystal structure by the targeted ones. Amber99SB force filed parameters were used for proteins. Counter ions were used to neutralize the systems. All systems were solvated in cubic box of TIP3P water molecules with a buffer of 8 Å. The size of the final simulation systems is about 70.3×88.3×83.5 Å3, containing about 56400 atoms. A cutoff of 10.0 Å was applied for the calculations of pairwise interactions (van der Waals and direct Coulomb). The particle mesh Ewald method was employed for long-range electrostatic interaction calculations. The SHAKE algorithm was used to constrain all bonds involving hydrogen. 1000 steps of steepest descent minimization and 1500 steps of conjugate gradient minimization were performed to relieve any structural clash in the solvated systems, followed by a 500 ps heatingequilibration from 0K to 330K in the NVT ensemble and a 500 ps cooling-equilibration process from 330K to 300K in the NPT ensemble. The temperature was regulated by Langevin dynamics to be at 300K. The time interval between simulation steps was 2fs, and all the data were collected every 1.0 ps. Without specific notification, the STING protein in this article refers to human STING. The simulation time of the production run is about 260ns for each system and three independent trajectories of each system were obtained. Free energy decomposition Free energy calculations together with MD simulations yield ligand-protein binding energies quantitatively. Decomposition of binding energies further enables us to identify key residues in ligandprotein interaction. In our free energy decomposition analysis, we used the scheme “per-residue decomposition” provided by AMBERTOOLS, MMPBSA.py28 to analyze key residues in DMXAA4


Page 5 of 29

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


STING interactions. Free energies were decomposed into specific residue contributions, using implicit solvent models29-30. The first 100ns of MD trajectories were discarded in the calculation of binding free energy decomposition and snapshots used in the calculation were taken every 20ps. Correlation analyses Correlations in the DMXAA-STING interaction network are described by a series of distance correlations. The correlation coefficients were calculated as, =

〈 − − 〉

〈 − 〉 〈 − 〉

(1)

where represents the distance between atoms in the interaction network. In this work, the first 100 ns of MD trajectories were not used in the construction of the covariance matrix and the snapshots were taken every 5ps. Hydrogen bond criteria. Hydrogen bond is considered formed when the heavy atom distance of donor and acceptor is less than 3.2 Å and the angle cutoff is 135°. Entropy calculations We utilized the two-phase thermodynamic (2PT) model to calculate the absolute entropy of water in the binding pocket of STING. 2PT method, developed by Lin et al., has been successfully applied to calculate entropy of water and other fluids31-34. The model is based on the hypothesis that the liquid partition function can be decomposed into solid-like and gas-like components and the entropy is a weighted summation of the two components. The gas-like component models the anharmonic low frequency modes of fluid, and the solid-like component describes the harmonic properties of fluid. The entropy of water molecules is calculated as

= +

(2)

where represents translational, rotational and vibrational components, is the Boltzmann constant

and is the weighting factor, is the frequency, and the superscript !, ", refer to liquid, solidlike and gas-like components, respectively. 5



1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

Page 6 of 29

In this study, the liquid DOS function ( ) is calculated by the Fourier transform of the

velocity autocorrelation function (VACF, # )

/

2 = lim # + ,-./ # % )→∞

(3)

,/

where % is the temperature of the simulation system. For translational and vibrational VACF, # is determined from the summation over mass-weighted center-of-mass velocities and intramolecular vibration velocities (01 ), respectively

while for rotational VACF, # is the summation of inertia-weighted angular velocities (?1 ) B

=>

(5)

where indicates the ith water molecule, C indicates the jth principle moment of inertial of a water

molecule, D is the total number of water molecules in the system and 9 is the mass of a water

molecule. The DOS of the gas-like component is represented by that of hard spheres =

1+F

G J 6DI

(6)

where is the DOS intensity of the real system at zero frequency. The fluidity factor I is obtained from 2∆,M⁄ I >O⁄ − 6∆,B I O − ∆,B⁄ I P⁄ + 6∆,B⁄ I O⁄ + 2I − 2 = 0

(7)

where the dimensionless diffusivity constant ∆ is defined by >

>

2 G % D B 6 B ∆= R S R S R S 9D 9 T G 6


(8)

Page 7 of 29

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


In Eq. 7, represents the DOS of liquid at zero frequency and T is the volume of the system. As the translational, rotational and vibrational DOS of water molecules are partitioned into gas-like and solid-

like components, is calculated as

= −

(9)

Furthermore, the weighting factors for the gas-like and solid-like contributions of entropy are determined by eq. (9) and eq. (10) respectively, =

UℎW − ln[1 − +XY−UℎW \ +XYUℎW − 1

(10)

]^ 3

(11)

=

where ℎ is the Planck constant; U equals 1/ %; ]^ is the hard-sphere entropy. Since the bond vibrations of the water molecules were constrained in the simulations, vibrational entropy was neglected in the 2PT calculation. The integration time step is 1 fs. Velocities and coordinates were saved every 4 fs. Trajectories of 400 ps simulations were used in 2PT calculation. The water entropy calculated in other biological systems show that 400 ps is enough for the convergence in 2PT calculation35-37. Errors were estimated as standard deviations of a series of entropy values calculated from the first 300 ps trajectories to the total 400 ps trajectories, accumulated by 20 ps in each calculation. To calculate bulk water entropy, we used a water box containing 2557 TIP3P water molecules and produced a 100 ps trajectory. Other simulation details are the same as described earlier.

Luciferase reporter assays Reporter assay was performed as previously described38. Briefly, HEK293T cells were seeded in 24-well plates and transiently transfected with an IFN-β luciferase reporter plasmid and indicated expression plasmids. After 12 hours, cells were stimulated with medium containing DMXAA. After an additional 12 hours, cells were lysed and the reporter activity was analyzed using the Dual-Luciferase Reporter Assay System (Promega).

7



1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

Page 8 of 29

Results A designed single mutation E260I endows hSTING with DMXAA response To unravel why the S162A mutant is able to respond to DMXAA, we first analyzed how STINGAQ-S162A interacts with DMXAA. Here, we utilized the free energy decomposition calculation to identify the key residues in DMXAA-STINGAQ-S162A interaction. The per-residue free energy decomposition scheme provides insights into the contribution of each residue in the ligand-protein interaction. Residues that contribute more than 1.5 kcal/mol to the binding free energy are considered as significant. Our analysis revealed 19 key residues and they are listed in Figure 1. These residues locate in the binding pocket (like 263T, 267T) and the lid region (such as 238R and 232R). Among them, two 238R residues play the most prominent role. This result indicates that the electrostatic interaction is critical for DMXAA binding. The failed response of STINGAQ-S162A to DMXAA with R238A mutation also supports the important role of the electrostatic interaction between 238R and DMXAA (Figure S1). Several other key residues, e.g., 263T, 267T, are expected to act as anchors in DMXAA binding. Moreover, an earlier study showed that G230I in the lid region and Q266I in the pocket region enable STING respond to DMXAA14, showing the critical roles of hydrophobic interactions. These observations shed light on the discovery of new mutations that would endow STING with DMXAA sensitivity. To further enhance the attractive electrostatic interactions or hydrophobic interactions between DMXAA and STING, a number of new mutations were designed in this study (more details in SI, section I). The responses of these STINGAQ mutants to DMXAA were examined by IFN-β activation through Luciferase reporter assays. One mutation type, E260I, is shown to respond to DMXAA and activate IFN-β production, as illustrated in Figure 2. Furthermore, we mutated 260E to other sixteen kinds of amino acids to evaluate the effect of mutation in this location. Luciferase reporter assays showed that E260Q, E260T and E260V also endow STINGAQ with DMXAA sensitivity, but with lower IFN-β readouts than E260I (Figure 3). In another perspective, the mutation of the negatively charged glutamic acid to apolar isoleiucine and valine leads to a better STING response to DMXAA than when it is mutated to polar, uncharged threonine and glutamine. Binding free energy comparison among STINGAQ and its mutants To understand how S162A or E260I exert influences on DMXAA-STING interaction, we compared the binding free energy differences among the three systems. Again, the contribution of each residue to the binding free energy was calculated. Residues of which the contribution to the binding 8


Page 9 of 29

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


free energy is greater than 1.5 kcal/mol are listed in Table 1 and Table S2 (resides with positive values are excluded). More residues contribute to the interaction with DMXAA significantly in mutated systems than in STINGAQ, as the number of key contributors in STINGAQ, STINGAQ-S162A and STINGAQ-E260I are 11, 15 and 20 respectively (Table 1). More specifically, compared to STINGAQ, 232R, 178R, 169R and 167Y in STINGAQ-S162A and STINGAQ-E260I contribute significantly in DMXAA-STING interaction. Another difference is that although the two 238R residues are dominant in DMXAA binding, their contributions are quite different among the three systems, increasing in the order of STINGAQ, STINGAQ-S162A and STINGAQ-E260I on average (Table 1). The value of per-residue free energy is a summation of four components: the electrostatic interaction, van der Waals interaction, polar and non-polar solvation free energy29. Therefore, we further compared the differences of the four components of 238R. Table 2 lists the contributions from electrostatic interaction and polar solvation free energy (the contributions from the other two parts are relatively small). It can be seen from Table 2 that the polar solvation free energies of the two 238R residues decrease in the order of STINGAQ, STINGAQ-S162A and STINGAQ-E260I.

Strong structural correlation of STINGAQ interacting with DMXAA The above analyses reveal that the interaction of 238R with DMXAA is significantly affected by the S162A and E260I mutations. Interestingly, 162A and 260E are about 11.9 Å (162A Cα-238R Cζ) and 8.2 Å (260E Cδ-238R Cζ) away from 238R in crystal structure of DMXAA-STINGAQ-S162A. Moreover, 260E is also distant from the binding pocket. An interesting question then arises: how does the distant single mutation on 162S or 260E affect the interaction of 238R with DMXAA? In many biological systems, it has been proved that such a large distance effect often relates to long-range structural correlations39-41. Therefore, we examined in detail the structural correlations in the three systems. To identify structural changes that relate to the mutant site, Pearson correlation coefficients of 34 pairs of distances of residues were calculated. The selected residues include those above-mentioned key residues and additional ones that locate on the pairs of α1- and α3-helices. The residues on the helices are included since they form a bundle that constitutes the U-shape binding pocket. According to the structural motif of dimeric STING, the 34 distances are categorized into four groups: i). distances between the pair of α1-helices (No. 1-No. 6), ii). distances between the pair of α3-helices (No. 7-No. 13), iii). distances between residues forming hydrogen bonds or salt bridges (No. 14-No. 19), and iv). distances between specified residues and DMXAA (No. 20-No. 34) (More details in SI, section IV). 9



1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

Page 10 of 29

The correlation map (Figure 4) illustrates that the structural correlations in DMXAA-STINGAQ are much stronger than those involve STINGAQ-S162A or STINGAQ-E260I. In STINGAQ, strong correlations exist between the two monomers, as the variation of distances between two pairs of α1- and α3-helices (region i and ii) are correlated. Besides, distances between the two pairs of helices also have a strong correlation with those in region iii and iv, which represent intra- and inter-molecular interactions. Unlike STINGAQ, there is no obvious cross-protomer correlation in STINGAQ-S162A and region iii and iv show a reduced structural correlation both in the magnitude and the range; whereas in STINGAQ-E260I, all the correlations are significantly weakened. We further examined how the strong cross-protomer structural correlation arises in STINGAQ. We built a correlation network according to the strength of the correlation coefficients mentioned earlier and the relative positions of the involved residues. Distances of which the magnitude of correlation coefficients greater than 0.5 and the corresponding residues with direct interactions, such as hydrogen bond interactions and forming salt bridges are considered directly coupled. The correlation network and the relative positions of corresponding residues in the complex structure are depicted in Figure 5. As illustrated in this figure, 267T and 162S* (* is used to represent the residue on the other protomer) at the bottom of the binding pocket interact with DMXAA through hydrogen bond. When the carbonyl group of DMXAA forms a hydrogen bond with 162S*, distances between 267T/263T and DMXAA increase. At the same time, DMXAA also moves towards 238R and 240Y and tends to form hydrogen bonds with them. The competitive hydrogen bond interactions between DMXAA-267T and DMXAA162S* serve to switch the interaction of DMXAA with the top of binding pocket and with the lid region. In such a manner, the perturbations at the bottom of the binding pocket propagate to the top and lid region. Moreover, when DMXAA forms a hydrogen bond with 238R or 240Y, the hydrogen bond between 238R and 260E* or 240Y and 166G is interrupted. Since 166G and 162S locate on α1-helix, 263T and 267T locate on α3-helix, respectively, structural correlation is established between the pairs of α1-helices and α3-helices. Therefore, there are two pathways of cross-protomer interactions in DMXAA-STINGAQ: DMXAA-267T-162S* and 238R-260E*. The S162A and E260I mutation directly eliminates the DMXAA-162S*-267T and 238R-260E* pathway respectively. Moreover, in DMXAA-STINGAQ-E260I, the E260I also interrupts the DMXAA162S*-267T pathway. Therefore, compared to the wild type, the structural correlation in DMXAASTINGAQ-S162A is weak and that in DMXAA-STINGAQ-E260I becomes even weaker. As shown in Table 3, in STINGAQ, DMXAA, 162S* and 267T can all form hydrogen bond with each other, while these types of hydrogen bond interactions do not form in STINGAQ-E260I. In the latter, strong interactions of 10


Page 11 of 29

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


DMXAA with residues 238R and 232R in the lid region and at top of the binding pocket 169R, 167Y, 163Y (Table 1) deprive the hydrogen bond interaction between DMXAA, 162S* and 267T at the bottom of the binding pocket. Changes induced by mutations to pocket water molecules Water molecules in the binding pocket have significant influences on protein-ligand binding 42-44. Even subtle structural and dynamical changes in protein are able to alter the water network in the binding pocket and thus influence the response of a protein to its ligands45. In this section, we investigate how pocket water molecules are influenced by mutations. First, we examined whether mutations would alter the interactions between water molecules and STING. We calculated correlation functions between the number of nearby-waters of residues and the distance between pairs of residues mentioned earlier. In these analyses, water molecules within 5Å of a residue are considered. As illustrated in Figure 6, in STINGAQ, the numbers of water molecules around 260Ea and 166Gb correlate relatively strongly with a number of residue-residue and residue-ligand distances (more details in SI, section IV). In STINGAQ-S162A, only water molecules around 260Eb showed weak correlations; whereas in STINGAQ-E260I, no distinct correlation can be observed. In this sense, the water molecules in the binding pocket of the wild type STING are more integrated into structural change upon ligand binding than the other two. We also investigated changes in the thermodynamic properties of pocket water molecules. The enthalpy was estimated by the interaction energy through MD simulation. The two-phase thermodynamic (2PT) model was used to calculate water entropy32-33, 46. In these calculations, water molecules within 5Å from both 167TYR and 262ALA on the two chains are considered as pocket waters. As listed in Table 4, the average interaction energies per pocket water molecule of STINGAQ, STINGAQ-S162A and STINGAQ-E260I are -8.5 kcal/mol, -7.1 kcal/mol and -6.5 kcal/mol, respectively. The interaction energy of a water molecule in bulk water is 3.4 kcal/mol, which is in a good agreement with experimental measurements47 and ab initio calculations48-49. The pocket waters in all three systems have lower entropies (per water molecule) than bulk water (Table 4). This is not surprising since in the bulk phase, water molecules translate with a less steric hindrance compared to those surrounded by protein residues and reorient freely through concerted large-amplitude angular jumps50-51. As listed in Table 4, the entropic penalty of individual water molecules decreases in the order of STINGAQ, STINGAQ-S162A and STINGAQ-E260I, which is 3.3 kcal/mol, 3.1 kcal/mol and 2.9 kcal/mol, respectively. Such a result reveals that the water molecules are constrained to varied degrees in the different binding 11



1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

Page 12 of 29

pockets. In addition, both STING mutants in the bound state accommodate fewer pocket waters than STINGAQ; whereas in the apo state, the wild type contains the fewest pocket waters. The contribution of the enthalpic and entropic change to the binding free energy are estimated through eq (12) and eq (13) respectively, ∆` = D3a@ − D7@b4c ∗ e7bf − ea2@

(12)

−%∆ = −D3a@ − D7@b4c ∗ %7bf − a2@

(13)

where N is the number of pocket water molecules, E is the interaction energy per water molecule and S is the entropy per water molecule. The subscripts pro and bulk are used to indicate water molecules in

the binding pocket and in bulk water, respectively. The enthalpy changes ∆g of water molecules

upon ligand binding for STINGAQ, STINGAQ-S162A and STINGAQ-E260I are 20.4 kcal/mol, 62.9 kcal/mol and 62.0 kcal/mol, respectively. The corresponding entropy changes (−h∆i) are -4.8 kcal/mol, -23.8 kcal/mol and -32.0 kcal/mol, respectively.

Discussion Guided by free energy decomposition calculations on DMXAA-STINGAQ-S162A, we identified a new mutation-E260I endowing STINGAQ respond to DMXAA. It was also found that two crucial residues-arginine 238, dominate DMXAA binding which means the electrostatic interactions are essential for DMXAA binding. In addition, the contribution of 238R to the interaction energy are distinctively different in the interaction of DMXAA with STINGAQ, STINGAQ-S162A and STINGAQ-E260I, although there is only one residue different in the sequence of the three complexes. Based on structural correlation analysis, we identified pathways that affect this long-distance effect. DMXAA-STINGAQ showed a stronger structural correlation than the mutant systems. In STINGAQ, not only distances between residues which directly interact with DMXAA, but also distances between helices constituting the binding pocket are correlated. Two pathways mediate this cross-protomer correlation: i). the hydrogen bond interaction among DMXAA, 162S* and 267T; ii). salt bridge and hydrogen bond between 238R* and 260E. S162A mutation eliminates the DMXAA-162S*-267T pathway directly and DMXAA-STINGAQ-S162A shows a weak structural correlation. E260I mutation eliminates the 238R*260E pathway and disrupts the second pathway, DMXAA-162S*-267T, as DMXAA only forms hydrogen bond with 162S* but not with 267T ( Table 3). Therefore, there is no distinct structural correlation in DMXAA-STINGAQ-E260I. That is the way of how one single mutation is able to weaken the structural correlations of the ligand-substrate interaction in a large scale. 12


Page 13 of 29

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


Water molecules in the binding pocket of STING and its two mutants also present different thermodynamic properties. The translational and rotational properties of water molecules, characterized by entropies, can be very different in various environments. For example, the entropy penalty is ~ 0.1 kcal/mol for a water molecule on the lignin surface lipid bilayer and di-mannose

36

35

, ~ 0.7 kcal/mol for a water at the interface of

, and for major and minor groove of DNA waters, the value is ~ 0.5

kcal/mol and ~ 0.9 kcal/mol respectively37. In our systems, the corresponding entropy penalty is 3.3 kcal/mol, 3.1 kcal/mol and 2.9 kcal/mol in the binding pocket of STINGAQ, STINGAQ-S162A and STINGAQ-E260I respectively. From Table 4, one sees that the change in number of pocket water upon ligand binding is deterministic in the total entropic change. Although entropy penalties of individual pocket water molecule are similar among the three types of STING, our simulation revealed that the two mutants expel larger numbers of pocket water than STINGAQ upon binding, which are 4, 17, 20 for STINGAQ, STINGAQ-S162A and STINGAQ-E260I respectively on average. Consequently, the total entropic penalties of mutants are much larger than STINGAQ, greater than ~ 20 kcal/mol. Meanwhile, the total enthalpic changes compensate for these entropic penalties. Numerous physical mechanisms have been proposed to explain enthalpy-entropy compensation in ligand-protein interactions52. WaterMap53 and other statistical mechanical models54-55 have been used to demonstrate that solvent reorganization is one of the origins of this compensation. Here, we showed that changes in number of pocket water molecules upon ligand binding is a factor that contributes to enthalpy-entropy compensation. Since both of the mutants possessing DMXAA response show weaker structure correlations, it might be reasonable to speculate that the strong structural correlations in STINGAQ weaken the interaction between DMXAA and 238R and consequently discourage ligand binding. 260E and DMXAA competitively interact with 238R. Mutating 260E to isoleusine enhances the interaction between DMXAA and 238R. In DMXAA-STINGAQ, the correlation coefficient of distance between DMXAA and 162S* and that between DMXAA* and 238R* is -0.7. The interaction of DMXAA and 162S* weakens the interaction between DMXAA* and 238R*. Therefore, we hypothesize that the strong structural correlation of complex hinders protein structure adjustment, especially the adjustment of 238R, upon ligand binding. Based on such an argument, cross-protomer interactions such as hydrogen bond interaction among DMXAA, 162S* and 267T that intermediate the structural correlation is unfavorable for DMXAA binding. Such a hypothesis is consistent with experimental observations: the double mutation STINGAQ-S162A/T267A induces even higher IFN-β expressions than STINGAQ-S162A as shown by the luciferase assay (SI, section II).

Conclusion 13



1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

Page 14 of 29

The rational design of mutants based on computational analysis lead to the discovery of a new mutation, E260I, that endows STING with DMXAA sensitivity. Further detailed analysis facilitated an understanding of how subtle changes in protein structure influence the DMXAA-STING interaction. On the one hand, single mutations reshape the structural correlation network involved in the DMXAASTING interaction, thereby exerting an influence on distant interactions. On the other hand, single mutations disturb the coordinated motions of water molecules and change how much water are expulsed upon ligand binding. Moreover, molecular level analyses shed light on drug optimization. We expect that DMXAA derivatives with no interaction with 162S* and 267T are candidate compounds for binding human STING.

Supporting Information Supporting information contains details of all the designed mutations, IFN-β expression of STINGAQ double mutations, the values of contributions of key residues to binding free energy and collective variables used in structural correlation analyses.

ACHNOWLEDGMENTS The authors thank support from the National Natural Science Foundation of China [21573006, U1430237, 21373016]. We also thank Peking University for providing the computational resources.

REFERENCES 1. Burdette, D. L.; Vance, R. E. STING and the Innate Immune Response to Nucleic Acids in the Cytosol. Nat. Immunol. 2013, 14, 19-26. 2. Burdette, D. L.; Monroe, K. M.; Sotelo-Troha, K.; Iwig, J. S.; Eckert, B.; Hyodo, M.; Hayakawa, Y.; Vance, R. E. STING is a Direct Innate Immune Sensor of Cyclic Di-GMP. Nature 2011, 478, 515-518. 3. Ishikawa, H.; Ma, Z.; Barber, G. N. STING Regulates Intracellular DNA-Mediated, Type I InterferonDependent Innate Immunity. Nature 2009, 461, 788-792. 4. Zhong, B.; Yang, Y.; Li, S.; Wang, Y. Y.; Li, Y.; Diao, F. C.; Lei, C. Q.; He, X.; Zhang, L.; Tien, P., et al. The Adaptor Protein MITA Links Virus-Sensing Receptors to IRF3 Transcription Factor Activation. Immunity 2008, 29, 538-550. 5. Keating, S. E.; Baran, M.; Bowie, A. G. Cytosolic DNA Sensors Regulating Type I Interferon Induction. Trends Immunol. 2011, 32, 574-581. 6. Paludan, S. R.; Bowie, A. G. Immune Sensing of DNA. Immunity 2013, 38, 870-880. 14


Page 15 of 29

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


7. Huang, Y.-H.; Liu, X.-Y.; Du, X.-X.; Jiang, Z.-F.; Su, X.-D. The Structural Basis for the Sensing and Binding of Cyclic Di-GMP by STING. Nat. Struct. Mol. Biol. 2012, 19, 728-730. 8. Gao, P.; Ascano, M.; Zillinger, T.; Wang, W. Y.; Dai, P. H.; Serganov, A. A.; Gaffney, B. L.; Shuman, S.; Jones, R. A.; Deng, L., et al. Structure-Function Analysis of STING Activation by c[G(2 ',5 ') pA(3 ',5 ')p] and Targeting by Antiviral DMXAA. Cell 2013, 154, 748-762. 9. Zhang, X.; Shi, H. P.; Wu, J. X.; Zhang, X. W.; Sun, L. J.; Chen, C.; Chen, Z. J. J. Cyclic GMP-AMP Containing Mixed Phosphodiester Linkages is an Endogenous High-Affinity Ligand for STING. Mol. Cell 2013, 51, 226-235. 10. Cavlar, T.; Deimling, T.; Ablasser, A.; Hopfner, K. P.; Hornung, V. Species-Specific Detection of the Antiviral Small-Molecule Compound CMA by STING. EMBO J. 2013, 32, 1440-1450. 11. Baguley, B. C.; Ching, L. M. DMXAA: An Antivascular Agent with Multiple Host Responses. Int. J. Radiat. Oncol. Biol. Phys. 2002, 54, 1503-1511. 12. Lara, P. N.; Douillard, J. Y.; Nakagawa, K.; von Pawel, J.; McKeage, M. J.; Albert, I.; Losonczy, G.; Reck, M.; Heo, D. S.; Fan, X. L., et al. Randomized Phase III Placebo-Controlled Trial of Carboplatin and Paclitaxel With or Without the Vascular Disrupting Agent Vadimezan (ASA404) in Advanced Non-Small-Cell Lung Cancer. J. Clin. Oncol. 2011, 29, 2965-2971. 13. Conlon, J.; Burdette, D. L.; Sharma, S.; Bhat, N.; Thompson, M.; Jiang, Z. Z.; Rathinam, V. A. K.; Monks, B.; Jin, T. C.; Xiao, T. S., et al. Mouse, but not Human STING, Binds and Signals in Response to the Vascular Disrupting Agent 5,6-Dimethylxanthenone-4-Acetic Acid. J. Immunol. 2013, 190, 5216-5225. 14. Gao, P.; Zillinger, T.; Wang, W. Y.; Ascano, M.; Dai, P. H.; Hartmann, G.; Tuschl, T.; Deng, L.; Barchet, W.; Patel, D. J. Binding-Pocket and Lid-Region Substitutions Render Human STING Sensitive to the Species-Specific Drug DMXAA. Cell Rep. 2014, 8, 1668-1676. 15. Henzler-Wildman, K.; Kern, D. Dynamic Personalities of Proteins. Nature 2007, 450, 964-972. 16. Bahar, I.; Lezon, T. R.; Yang, L. W.; Eyal, E. Global Dynamics of Proteins: Bridging Between Structure and Function. Annu. Rev. Biophys. 2010, 39, 23-42. 17. Eisenmesser, E. Z.; Bosco, D. A.; Akke, M.; Kern, D. Enzyme Dynamics during Catalysis. Science 2002, 295, 1520-1523. 18. McGowan, L. C.; Hamelberg, D. Conformational Plasticity of an Enzyme during Catalysis: Intricate Coupling between Cyclophilin A Dynamics and Substrate Turnover. Biophys. J. 2013, 104, 216-226. 19. Bhattacharya, S.; Vaidehi, N. Differences in Allosteric Communication Pipelines in the Inactive and Active States of a GPCR. Biophys. J. 2014, 107, 422-434. 20. Kumar, A.; Glembo, T. J.; Ozkan, S. B. The Role of Conformational Dynamics and Allostery in the Disease Development of Human Ferritin. Biophys. J. 2015, 109, 1273-1281. 21. Rose, R. B.; Craik, C. S.; Stroud, R. M. Domain Flexibility in Retroviral Proteases: Structural Implications for Drug Resistant Mutations. Biochemistry (Mosc.) 1998, 37, 2607-2621. 22. Ladbury, J. E. Just Add Water! The Effect of Water on the Specificity of Protein-Ligand Binding Sites and Its Potential Application to Drug Design. Chem. Biol. 1996, 3, 973-980. 23. Papoian, G. A.; Ulander, J.; Wolynes, P. G. Role of Water Mediated Interactions in Protein-Protein Recognition Landscapes. J. Am. Chem. Soc. 2003, 125, 9170-9178. 24. Materese, C. K.; Goldmon, C. C.; Papoian, G. A. Hierarchical Organization of Eglin C Native State Dynamics is Shaped by Competing Direct and Water-Mediated Interactions. Proc. Natl. Acad. Sci. U. S. A. 2008, 105, 10659-10664. 25. Karplus, M.; McCammon, J. A. Molecular Dynamics Simulations of Biomolecules. Nat. Struct. Biol. 2002, 9, 646-652. 26. Frisch, M. J.; Trucks, G. W.; Schlegel, H. B.; Scuseria, G. E.; Robb, M. A.; Cheeseman, J. R.; Scalmani, G.; Barone, V.; Mennucci, B.; Petersson, G. A., et al. Gaussian 09, Gaussian, Inc.: Wallingford, CT, USA, 2009. 27. Wang, J. M.; Wolf, R. M.; Caldwell, J. W.; Kollman, P. A.; Case, D. A. Development and Testing of a General Amber Force Field. J. Comput. Chem. 2004, 25, 1157-1174. 28. Miller, B. R., 3rd; McGee, T. D., Jr.; Swails, J. M.; Homeyer, N.; Gohlke, H.; Roitberg, A. E. MMPBSA.py: An Efficient Program for End-State Free Energy Calculations. J. Chem. Theory Comput. 2012, 8, 3314-3321. 15



1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

Page 16 of 29

29. Gohlke, H.; Kiel, C.; Case, D. A. Insights into Protein-Protein Binding by Binding Free Energy Calculation and Free Energy Decomposition for the Ras-Raf and Ras-RaIGDS Complexes. J. Mol. Biol. 2003, 330, 891-913. 30. Metz, A.; Pfleger, C.; Kopitz, H.; Pfeiffer-Marek, S.; Baringhaus, K. H.; Gohlke, H. Hot Spots and Transient Pockets: Predicting the Determinants of Small-Molecule Binding to a Protein-Protein Interface. J. Chem. Inf. Model. 2012, 52, 120-133. 31. Lin, S. T.; Blanco, M.; Goddard, W. A. The Two-Phase Model for Calculating Thermodynamic Properties of Liquids from Molecular Dynamics: Validation for the Phase Diagram of Lennard-Jones Fluids. J. Chem. Phys. 2003, 119, 11792-11805. 32. Lin, S. T.; Maiti, P. K.; Goddard, W. A. Two-Phase Thermodynamic Model for Efficient and Accurate Absolute Entropy of Water from Molecular Dynamics Simulations. J. Phys. Chem. B 2010, 114, 8191-8198. 33. Lai, P. K.; Hsieh, C. M.; Lin, S. T. Rapid Determination of Entropy and Free Energy of Mixtures from Molecular Dynamics Simulations with the Two-Phase Thermodynamic Model. Phys. Chem. Chem. Phys. 2012, 14, 15206-15213. 34. Pascal, T. A.; Goddard, W. A. Interfacial Thermodynamics of Water and Six Other Liquid Solvents. J. Phys. Chem. B 2014, 118, 5943-5956. 35. Petridis, L.; Schulz, R.; Smith, J. C. Simulation Analysis of the Temperature Dependence of Lignin Structure and Dynamics. J. Am. Chem. Soc. 2011, 133, 20277-20287. 36. Tian, J. H.; Sethi, A.; Swanson, B. I.; Goldstein, B.; Gnanakaran, S. Taste of Sugar at the Membrane: Thermodynamics and Kinetics of the Interaction of a Disaccharide with Lipid Bilayers. Biophys. J. 2013, 104, 622-632. 37. Jana, B.; Pal, S.; Maiti, P. K.; Lin, S. T.; Hynes, J. T.; Bagchi, B. Entropy of Water in the Hydration Layer of Major and Minor Grooves of DNA. J. Phys. Chem. B 2006, 110, 19611-19618. 38. Sun, W. X.; Li, Y.; Chen, L.; Chen, H. H.; You, F. P.; Zhou, X.; Zhou, Y.; Zhai, Z. H.; Chen, D. Y.; Jiang, Z. F. ERIS, an Endoplasmic Reticulum IFN Stimulator, Activates Innate Immune Signaling through Dimerization. Proc. Natl. Acad. Sci. U. S. A. 2009, 106, 8653-8658. 39. Foda, Z. H.; Shan, Y. B.; Kim, E. T.; Shaw, D. E.; Seeliger, M. A. A Dynamically Coupled Allosteric Network Underlies Binding Cooperativity in Src Kinase. Nat. Commun. 2015, 6, 5939. 40. Gasper, P. M.; Fuglestad, B.; Komives, E. A.; Markwick, P. R. L.; McCammon, J. A. Allosteric Networks in Thrombin Distinguish Procoagulant vs. Anticoagulant Activities. Proc. Natl. Acad. Sci. U. S. A. 2012, 109, 2121621222. 41. Allain, A.; de Beauchene, I. C.; Langenfeld, F.; Guarracino, Y.; Laine, E.; Tchertanov, L. Allosteric Pathway Identification through Network Analysis: from Molecular Dynamics Simulations to Interactive 2D and 3D Graphs. Faraday Discuss. 2014, 169, 303-321. 42. Lemmon, G.; Meiler, J. Towards Ligand Docking Including Explicit Interface Water Molecules. PLoS One 2013, 8, e67536. 43. Mikulskis, P.; Genheden, S.; Ryde, U. Effect of Explicit Water Molecules on Ligand-Binding Affinities Calculated with the MM/GBSA Approach. J. Mol. Model. 2014, 20, 2273. 44. Mancera, R. L. De Novo Ligand Design with Explicit Water Molecules: an Application to Bacterial Neuraminidase. J. Comput. Aided Mol. Des. 2002, 16, 479-499. 45. Davidson, R. M.; Lauritzen, A.; Seneff, S. Biological Water Dynamics and Entropy: A Biophysical Origin of Cancer and other Diseases. Entropy 2013, 15, 3822-3876. 46. Debnath, A.; Mukherjee, B.; Ayappa, K. G.; Maiti, P. K.; Lin, S.-T. Entropy and Dynamics of Water in Hydration Layers of a Bilayer. J. Chem. Phys. 2010, 133, 174704. 47. Rocher-Casterline, B. E.; Ch'ng, L. C.; Mollner, A. K.; Reisler, H. Communication: Determination of the Bond Dissociation Energy (D0) of the Water Dimer, (H2O)2, by Velocity Map Imaging. J. Chem. Phys. 2011, 134, 211101. 48. Shank, A.; Wang, Y.; Kaledin, A.; Braams, B. J.; Bowman, J. M. Accurate Ab Initio and "Hybrid" Potential Energy Surfaces, Intramolecular Vibrational Energies, and Classical IR Spectrum of the Water Dimer. J. Chem. Phys. 2009, 130, 144314. 16


Page 17 of 29

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


49. Leforestier, C.; Szalewicz, K.; van der Avoird, A. Spectra of Water Dimer from a New Ab Initio Potential with Flexible Monomers. J. Chem. Phys. 2012, 137, 014305. 50. Laage, D.; Hynes, J. T. A Molecular Jump Mechanism of Water Reorientation. Science 2006, 311, 832835. 51. Young, T.; Abel, R.; Kim, B.; Berne, B. J.; Friesner, R. A. Motifs for Molecular Recognition Exploiting Hydrophobic Enclosure in Protein-Ligand Binding. Proc. Natl. Acad. Sci. U. S. A. 2007, 104, 808-813. 52. Chodera, J. D.; Mobley, D. L. Entropy-Enthalpy Compensation: Role and Ramifications in Biomolecular Ligand Recognition and Design. Annu. Rev. Biophys. 2013, 42, 121-142. 53. Breiten, B.; Lockett, M. R.; Sherman, W.; Fujita, S.; Al-Sayah, M.; Lange, H.; Bowers, C. M.; Heroux, A.; Krilov, G.; Whitesides, G. M. Water Networks Contribute to Enthalpy/Entropy Compensation in Protein-Ligand Binding. J. Am. Chem. Soc. 2013, 135, 15579-15584. 54. Grunwald, E.; Steel, C. Solvent Reorganization and Thermodynamic Enthalpy-Entropy Compensation. J. Am. Chem. Soc. 1995, 117, 5687-5692. 55. Lee, B.; Graziano, G. A Two-State Model of Hydrophobic Hydration that Produces Compensating Enthalpy and Entropy Changes. J. Am. Chem. Soc. 1996, 118, 5163-5168.

TABLE AND FIGURES LEGENDS

Table 1. Differences in the per-residue free energy contribution System Number of main contributors

STINGAQ 11

STINGAQ-S162A 15

STINGAQ-E260I 20

Contribution of 238R (kcal/mol)

-7.7/-3.5

-17.5/-9.8

-16.2/-15.0

238R, 263T, 267T, 166G, 264P

Same key residues

240Y, 169R, 167Y, 178R, 169R, 232R, 167Y, 239Y, 232R, 178R 163Y, 162S, 236K, 165I *The residues are sequenced by their contribution magnitude to binding free energy Different key residues *

162S

Table 2. Decomposition of free energy contribution from 238R (kcal/mol) System

238Ra

238Rb

Epolar

Eele

Etot

Epolar

Eele

Etot

STINGAQ

63.3

-70.5

-7.2

51.4

-54.3

-2.9

STINGAQ-S162A

55.9

-73.1

-17.2

43.5

-52.0

-8.5

STINGAQ-E260I

47.1

-62.9

-15.8

38.5

-52.9

-14.4

17



1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

Page 18 of 29

Table 3. Percentage of hydrogen bond formation among DMXAA, 162S* and 267T System

Interactive pairs

STINGAQ STINGAQ-E260I

Interaction group A

DMXa-162Sb

0.30

--

DMXa-267Ta

0.50

--

267Ta-162Sb

0.33

0.94

DMXb-162Sa

0.08

0.84

DMXb-267Tb

0.89

0.02

267Tb-162Sa

0.01

0.89

Interaction group B

Table 4. Thermodynamic properties of pocket water and bulk water at 300K STINGAQ

STINGAQ-S162A

STINGAQ-E260I

Bulk water

Napo

24±2

33±3

29±4

--

Nbound

20±0

16±1

9±0

--

E(kcal/mol)

-8.5±0.0

-7.1±0.0

-6.5±0.1

-3.4±0.0

∆g (kcal/mol)

20.4

62.9

62.0

--

TS (kcal/mol)

3.3±0.1

3.1±0.1

2.9±0.0

4.5±0.0

-4.8

-23.8

-32.0

--

−h∆i (kcal/mol)

Figure 1.

Key residues in DMXAA-hSTINGAQ-S162A interaction; where a and b represent different chain of

STING dimer.

18


Page 19 of 29

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


Figure 2. IFN-β expression of the designed mutations.

Figure 3.

IFN-β expression of STINGAQ with mutations to 260E

Figure 4.

Structural correlation maps (A) DMXAA- STINGAQ ; (B) DMXAA-STINGAQ-S162A and

(C) STINGAQ-E260I

19



1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

Figure 5.

Page 20 of 29

A. Interaction network. ‘a’ and ‘b’ represent protomer a and protomer b of STING dimer

respectively. Circles with two peripheries represent cross-protomer interactions; B. Location of the corresponding residues in the protein structure.

20


Page 21 of 29

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


Figure 6.

Correlation map of water numbers (# 35-67) and distances: (A) DMXAA- STINGAQ ;

(B) DMXAA-STINGAQ-S162A and (C) STINGAQ-E260I

21



1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

TOC Graphic

22


Page 22 of 29

Page 23 The of 29Journal of Physical Chemistry

1 2 3 4 5 6 7 8 9 10


The Journal of Physical Chemistry Page 24 of 29

1 2 3 4 5 6 7 8 9 10


Page The 25 Journal of 29 of Physical Chemistry

1 2 3 4 5 6 7 8 9 10



1 2 3 4 5 6 7 8 9 10 11


Page 26 of 29

Page 27 of 29

1 2 3 4 5 6 7 8 9 10 11 12 13



The Journal of Physical PageChemistry 28 of 29

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 ACS 36 Paragon Plus Environment 37 38

Page 29 TheofJournal 29 of Physical Chemistry

1 2 3 4 5 6


Single Mutations Reshape the Structural Correlation Network of the

Recommend Documents