Molecular Dynamics Simulations of DNA Solvation Dynamics

PERSPECTIVE pubs.acs.org/JPCL

Molecular Dynamics Simulations of DNA Solvation Dynamics K. E. Furse and S. A. Corcelli* Department of Chemistry and Biochemistry, University of Notre Dame, Notre Dame, Indiana 46556

ABSTRACT Time-dependent Stokes shift experiments of fluorescent probe molecules incorporated into DNA have uncovered a broad range of time scales from femtoseconds to tens of nanoseconds. A series of recent molecular dynamics simulations have investigated the longest solvation dynamics time scales with sometimes conflicting interpretations. The conclusions of these computational studies are reviewed along with the theoretical methodologies that are utilized to decompose calculated solvation responses in terms of the components present in the system: water, DNA, and ions. Extensive validation of one such decomposition procedure for the dye molecule Hoechst 33258 bound to DNA reveals that the long time scale measured experimentally, 19 ps, is due to DNA. Future opportunities and challenges from both a theoretical and experimental perspective are also briefly highlighted.

ime-dependent Stokes shift (TDSS) experiments of fluorescent dyes in the condensed phase provide valuable information about the dynamics of the environment in the immediate vicinity of the probe molecule. For example, TDSS measurements have been used extensively to characterize and understand the dynamics of liquids.1,2 Conceptually, these experiments work by creating a perturbation in the charge distribution of the probe molecule via electronic excitation, to which the environment must respond. The response of the environment is reported by the time evolution of the frequency of maximum fluorescence, ν(t), which shifts to the red as the environment equilibrates to the excited-state charge distribution of the dye. Generally, a response function

T

SðtÞ ¼

νðtÞ - νð¥Þ νð0Þ - νð¥Þ

Time-dependent Stokes shift (TDSS) experiments of fluorescent dyes in the condensed phase provide valuable information about the dynamics of the environment in the immediate vicinity of the probe molecule. proteins3-26 and DNA.27-36 Interestingly, the response functions measured in these experiments contain long time components to their decay that are not present in bulk aqueous solution. In proteins, the long time TDSS decay can span from ∼10 ps to ∼10 ns, depending sensitively on the location of the probe with the protein. Abbyad et al. systematically incorporated a fluorescent synthetic amino acid at seven different sites within the B1 domain of streptococcal protein G and found qualitative differences between the long time components of the measured TDSS responses.3 When the probe was fully or partially solvent-exposed, the longest time responses were in the range from ∼10 ps to ∼100 ps. In contrast, the two sites that were fully buried exhibited responses up to ∼10 ns. A subsequent molecular dynamics (MD) investigation by Golosov and

ð1Þ

is constructed which exhibits several time scales in its decay to 0. A tremendous amount of experimental, theoretical, and simulation effort has been devoted to investigating the properties of S(t) for dye molecules in liquids, and as a result, the physical origins for the TDSS decay in single-component liquids are well-understood.1,2 In particular, the response function for organic dyes in aqueous solution typically reveals two time scales, a ∼200 fs decay that corresponds to librational motions of water molecules as they reorient their dipole moments to accommodate the excited-state charge distribution of the dye and a 1-2 ps decay that corresponds to a collective rearrangement of the water hydrogen bonding network in the vicinity of the dye. More recently, there have been numerous TDSS measurements for fluorescent probes incorporated within (or bound to)

r 2010 American Chemical Society

Received Date: April 14, 2010 Accepted Date: May 20, 2010 Published on Web Date: May 27, 2010

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DOI: 10.1021/jz100485e |J. Phys. Chem. Lett. 2010, 1, 1813–1820


Figure 1. (a) Disentangling the coupled motions present in biomolecular systems to assign the time scales measured in time-dependent Stokes shift experiments remains an important challenge for theory and simulation. (b) The inherent complexity of this challenge is captured schematically in this snapshot from a molecular dynamics simulation of Hoechst 33258 bound to DNA.

contributions to the TDSS response.4,37-44 However, the interpretation of TDSS measurements on proteins and DNA using MD simulations has been conflicting partly because a uniformly accepted protocol for decomposing the computed TDSS response for multicomponent systems has not yet fully emerged. In particular, this paper will focus solely on the four different decomposition methods that have been applied to MD simulations of TDSS responses in DNA, although the results and conclusions are also applicable to proteins. In order to understand the different decomposition strategies, the methodology by which total TDSS responses are typically computed must be briefly reviewed.

Karplus supported the observation of heterogeneous responses that depend on the position of the probe molecule.37 A broad range of TDSS decay time scales have also been measured in DNA. Pal, Zhao, and Zewail measured the TDSS of Hoechst 33258 (H33258) bound in the minor groove of a dodecamer oligonucleotide d(CGCAAATTTGCG)2 and found two characteristic time scales for its decay, 1.4 and 19 ps.27 A similar long time scale, 12 ps, was reported by Pal et al. for the synthetic base analogue 2-aminopurine (2AP) incorporated near the center of the same DNA sequence, d(CGCA(2AP)ATTTGCG)2.28 Interestingly, when the hydration of the 2APDNAwas altered by binding pentamidine to the minor groove, the long TDSS time scale was roughly unchanged, 10 ps. Some longer time scales, approximately 80-150 ps, were also observed in the 2AP-DNA TDSS measurements, but these dynamics were attributed to charge transfer between the 2AP probe and adjacent adenine bases. Berg and co-workers, who were the first to perform TDSS measurements in DNA, focused on a synthetic base pair analogue, Coumarin 102 (C102), incorporated within DNA.29-34 In dramatic contrast to the H33258-DNA and 2AP-DNA TDSS results, C102-DNA exhibits decays that extend beyond 40 ns. The interpretation of the long time decay of the TDSS response in biomolecular contexts has been the subject of considerable debate.38 Unlike in neat liquids, where TDSS experiments can be understood in terms of specific motions of the solvent, biomolecular systems contain many different components (water, biomolecule, counterions, etc.) with intrinsically different abilities to adapt to the excited-state charge distribution of the probe. Moreover, the dynamics of the components are inextricably coupled, which could, in principle, make the assignment of the long time TDSS decay to a single component (water versus biomolecule, for example) impossible. The inherent complexity is depicted schematically in Figure 1. MD simulations are a powerful tool for unraveling the complex interplay of the components and their relative


MD simulations are a powerful tool for unraveling the complex interplay of the components and their relative contributions to the TDSS response. The most direct connection between MD simulations and TDSS measurement comes through a calculation of the nonequilbrium solvation response function2 SðtÞ ¼

ΔEðtÞ - ÆΔEæe ÆΔEæg - ÆΔEæe

ð2Þ

where ΔE = Ee - Eg is the difference in the solute-solvent interaction energy with the solute in its excited and ground electronic states. For the purposes of computing ΔE and for performing the MD simulations, the ground and excited states of the solute are modeled as two different collections of atomic-centered partial charges that are precomputed from

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quantum chemistry calculations. It is important to note that describing the properties of the ground and excited electronic states in terms of atomic centered partial charges is an approximation that must be scrutinized for individual fluorescent probe molecules. Such a description is not adequate if a probe molecule exhibits unique excited-state photochemical properties, including photoinduced proton transfer or large conformational changes. The overbar in eq 2 represents a nonequilbrium average, whereas the notation Æ 3 3 3 æg or e indicates an equilibrium average with the solute in either its ground (g) or excited electronic state (e). The equilibrium averages are computed from MD simulations of the solute modeled in its appropriate electronic state. The nonequilbrium averages require the sampling of a statistically significant number of initial configurations from an equilibrium simulation of the solute in its ground electronic state. For each configuration, the solute charges are switched to those of the excited electronic state, and ΔE(t) is computed from a MD simulation. ΔE(t) is then the average of the ensemble of ΔE(t) from each independent starting configuration. While eq 2 provides a rigorous method for computing the observable in TDSS experiments, it suffers from two problems that limit its practicality for biological systems. First, ΔE(t) requires extensive sampling of nonequilbrium trajectories to converge, and moreover, each of these trajectories needs to span the longest time scales in the experiments. Thus, studying TDSS responses that reach into the tens of nanoseconds requires thousands of ∼10 ns MD trajectories, which is computationally prohibitive for biomolecular system sizes. The second practical problem with implementing eq 2 is its extraordinary sensitivity to ÆΔEæe, which must necessarily be computed in a separate simulation. Statistical error in ÆΔEæe can distort the calculated TDSS time scales because the apparent baseline for the decay is incorrect. A second approach to computing TDSS responses from MD simulations involves the application of linear response theory to eq 2, which yields the equilibrium solvation time correlation function2,45,46 CðtÞ ¼

ÆδΔEð0ÞδΔEðtÞæg or e Cð0Þ

ð3Þ Figure 2. (a) Nonequilbrium solvation response function for H33258 bound to DNA (black) and its decomposition in terms of the components present in the simulation, water (red), DNA (green), and Naþ ions (blue). (b) Equilibrium solvation time correlation function for H33258 bound to DNA (black) and its linear response decomposition, eq 6, in terms of the components present in the simulation, water (red), DNA (green), and Naþ ions (blue). (c) Equilibrium solvation time correlation function for H33258 bound to DNA (black) and its decomposition in terms of auto- and cross-correlation functions, eq 5.

where δΔE(t) = ΔE(t) - ÆΔEæg or e. The calculation of C(t) enjoys a practical advantage over S(t) because it contains only equilibrium averages, which are computed from a single MD simulation with the solute modeled in either its ground or excited electronic state. Although C(t) is rigorously equal to S(t) within the linear response regime, there is no guarantee that linear response theory is valid for any given system. Therefore, caution must be exercised when employing eq 3 to connect MD simulations with TDSS measurements. It is also important to recognize that, even though eq 3 contains only equilibrium averages, achieving statistical convergence of C(t) at long times can be exceedingly computationally expensive. Generally, one must sample for at least 1 order of magnitude longer than the slowest time scale in the experiment that one is modeling. To access the greater than 10 ns TDSS responses observed in some protein and DNA experiments requires simulations in excess of 100 ns.


Within the limit of pairwise additive interactions, the solute-solvent interaction energy decomposes into a sum, P ΔE = R ΔER(t), where R represents each component present in the system (DNA, water, counterions). Substituting this sum into eq 2 yields the first method for decomposing the calculated TDSS response4,39-41 X ΔER ðtÞ - ÆΔER æe X SðtÞ ¼ ¼ SR ðtÞ ð4Þ ÆΔEæg - ÆΔEæe R R

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time scales than that shown in Figure 2a for H33258-DNA.39 Pal et al.43 found significant contributions from all of the auto- and cross-correlation functions at longer times (except from the DNA-ion cross-correlation function). In particular, they observed a large anticorrelation between the ions and water. Their final interpretation of the results was that water and ions interacting with the DNA are largely responsible for the long time components in the TDSS experiments. In an effort to reconcile the different interpretations of Pal et al.43 and Furse and Corcelli,39 the auto- and cross-correlation functions for the solvation correlation function of H33258 bound to DNA are shown in Figure 2c. This calculation was based on 105 ns of MD simulation, and the time scales of the total C(t) are in near-quantitative agreement with experiment (1.37 ( 0.08 and 20.5 ( 0.9 ps compared to 1.4 and 19 ps).39 The decomposition results are very similar to those reported by Pal et al. for native DNA base solvation dynamics.43 With the exception of the DNA-ion cross-correlation function, which is nearly 0 at all times, all of the auto- and crosscorrelations functions exhibit long time scales. In addition, the water-ion and water-DNA cross-correlation functions are strongly negative, indicating an anticorrelation between these components. It is clear from these results that the dynamics of the various components are interrelated in a complex fashion, but equally apparent is the lack of even qualitative similarity between panels a and c of Figure 2. Recall that Figure 2a is the decomposition of the nonequilbrium solvation response function, which should be regarded as a benchmark. Although the auto- and cross-correlation functions contain important and detailed information about how the dynamics of the components are coupled, this decomposition method is not helpful in determining which motions are relevant to the interpretation of TDSS experiments. In a MD study of TDSS in the protein monellin, Nilsson and Halle proposed a different approach for decomposing the solvation time correlation function, C(t), in terms of the different components in the simulation.42 On the basis of the generalized fluctuation-dissipation theorem for a sum of perturbations derived by Bernard and Callen,47 Nilsson and Halle applied the linear response approximation to the individual components of the nonequilbrium solvation response. Their result was the following expression (see the appendix in the paper by Golosov and Karplus for a derivation)37

In eq 4, S(t) is seen to decompose into a sum of component responses, SR(t), which can unambiguously be interpreted as the contribution to the total response of each component R. Unfortunately, calculating the component response functions requires that each of the component baselines, ÆERæe, be computed with great statistical certainty, which is exceedingly computationally expensive. Figure 2a illustrates this point for H33258-DNA, where the authors have computed S(t) and its components from 5600 nonequilbrium trajectories each of length 100 ps (560 ns total), and the baselines were computed from approximately 160 ns of MD simulation.39 Despite such extensive statistical sampling, considerable noise and baseline errors are apparent. Nevertheless, the time scales for the total response are in reasonable agreement with experiment (2.6 ( 0.3 and 30 ( 7 ps compared to 1.4 and 19 ps). However, more importantly, the qualitative interpretation of the response in terms of the three components is readily evident. The ion and water curves are flat after about 10 ps, whereas the total and DNA responses are parallel and continue in their decay to considerably longer times. Thus, the long time scale in the TDSS experiment on H33258-DNA can be attributed to DNA dynamics.

The long time scale in the TDSS experiment on H33258-DNA can be attributed to DNA dynamics. Because of the sampling difficulties inherent in the calculation of the nonequilbrium solvation response function, S(t), and its components, SR(t), it is advantageous to develop decomposition schemes for the equilibrium solvation correlation function, C(t). Three such strategies have been applied to the study of solvation dynamics in the context of DNA. The first approach is to calculate the auto- and cross-correlation P functions that result from direct substitution of ΔE = R ΔER(t) into the definition of C(t), eq 3 X X ÆδΔER ð0ÞδΔEβ ðtÞæg or e CðtÞ ¼ Cð0Þ R β ¼

X X R

CRβ ðtÞ

ð5Þ

CðtÞ ¼

R

β

where δΔER(t) = ΔER(t) - ÆΔERæ. Pal et al. were the first to utilize this method to decompose the solvation correlation function computed from a 15 ns simulation of a 38 base pair sequence of native DNA.43 In their study, each of the middle 60 bases was regarded as a separate probe molecule. The range of time scales reported for the solvation time correlation functions for the bases, 1-2 and 20-30 ps, agreed fairly well with the 2AP-DNA28 and H33258-DNA27 experiments. A longer time scale was also observed (235.8 ps), but it had an amplitude representing just 4% of the total decay. The decomposition of the solvation correlation functions using eq 5 revealed a qualitatively different interpretation for the origin of the longer


X ÆδΔER ðtÞδΔEð0Þæ Cð0Þ

¼

X R

CR ðtÞ

ð6Þ

where δΔER(t) = ΔER(t) - ÆΔERæ. Within linear response theory, the component solvation correlation functions, CR(t), are rigorously equal to the component solvation response functions, SR(t). In Figure 2b, the component solvation response functions are shown for H33258-DNA computed from 105 ns of MD simulation.39 Despite the statistical limitations (discussed above) of the results for S(t) and its components in Figure 2a, the agreement between the results for SR(t) and CR(t) is unmistakable. This is perhaps not terribly surprising because the results for the total nonequilbrium solvation response function and the total equilibrium solvation time correlation are in reasonable agreement,39 which

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The conclusion of Sen et al.44 contradicts the interpretation of Furse and Corcelli39 that DNA is responsible for the long time scales in the TDSS experiment on H33258-DNA. One possible source of the discrepancy is an intrinsic difference between the two systems studied. Furse and Corcelli investigated H33258-DNA, whereas Sen et al. analyzed a simulation of native DNA. However, Pal et al.28 experimentally found very little change in the time scales of the TDSS response of 2AP in DNA with and without a pentamidine bound to the minor groove (∼12 versus ∼10 ps with pentamidine), which would seem to discount this source of the incongruity. This experimental finding also suggests that minor groove water molecules do not play a particularly special role in determining the long time TDSS response, despite exhibiting anomalous dynamics,48 because the pentamidine molecule would likely expel water from the minor groove in the vicinity of the 2AP probe.49 Further work is necessary to characterize the Sen et al. decomposition method and, in particular, to justify the approximations in eq 7. While it is certainly interesting that the cross-correlations between the calculated components of ΔE can be eliminated by constructing a suitable linear combination of the ΔER, this does not in itself guarantee that the resulting “intrinsic” components and the time scales for their fluctuations are physically meaningful. For example, the universality of the values of χRβ has not been established. Are the susceptibilities intrinsic properties of water, Naþ ions, and DNA that could be predicted from ab initio quantum chemistry calculations or measured experimentally? Would the same susceptibilities as those determined by Sen et al. for a model base in DNA cause the cross-correlation functions computed for H33258-DNA (Figure 2c) to vanish? These are pivotal questions that warrant future investigation. At present, the decomposition method of Sen et al. has not been sufficiently validated to understand the overall discrepancies between its conclusions and those of Furse and Corcelli for H33258-DNA. Although tremendous progress has been made in utilizing MD simulations to understand solvation dynamics measurements of DNA, fundamental challenges remain. One of the most immediate challenges is to reconcile two apparently disparate experimental results, the 10-12 ps time scales observed for 2AP-DNA28 compared with the 40 ns time scales for C102-DNA.29-34 This 3 orders of magnitude difference in the long time scales of the measured TDSS in these two systems remains unexplained. At first glance, the two systems appear very similar. In both cases, the fluorescent probe is covalently incorporated within the DNA base stack, and thus, the hydration of the two probes should be qualitatively similar. Moreover, when Pal et al.28 altered the hydration state of 2AP-DNA by binding a drug molecule to the minor groove, the long time scales of the TDSS response were nearly unchanged. Therefore, taken together, these experimental results are not consistent with water giving rise to the long time scales in the TDSS response. If it is then assumed that DNA dynamics are responsible for the 3 orders of magnitude difference in the solvation dynamics time scales for the two different covalently bound, base-stacking probes (2AP-DNA and C102-DNA), why are the DNA dynamics so different? Our

demonstrates that the linear response approximation is robust for H33258-DNA. The results shown in Figure 2b are consistent with the interpretation that the long time TDSS response of H33258-DNA is due to DNA motions. The Nilsson and Halle42 method of decomposing the equilibrium solvation response function, eq 6, offers two important advantages. The first is the computational efficiency necessary to investigate TDSS in biomolecular contexts with conventional MD simulations. This is illustrated in Figure 2, where the results for C(t) and CR(t) (Figure 2b) are considerably more converged than the results for S(t) and SR(t) (Figure 2a), even though the nonequilbrium calculations are based on more than five times as much MD simulation data. More importantly, though, eq 6 provides a conceptually consistent link to the unambiguous decomposition of the nonequilbrium solvation response function, eq 4. When it is not practicable to employ the nonequilbrium methodology, the most rigorous alternative is the linear response approximation based decomposition of Nilsson and Halle, which is theoretically justified and has been validated empirically (albeit only for H33258 in aqueous solution and bound to DNA).39,40 Recently, a third method for decomposing the equilibrium solvation time correlation function has been developed by Sen et al.44 Their approach is physically motivated by the idea that the electric field of one component influences the other components through polarization (e.g., the ions can orient the surrounding water molecules, which changes the effective electric field of the water). Sen et al. then assume that the magnitudes of the intrinsic fields due to each component (EW, ED, and EI for water, DNA, and ions, respectively) can be obtained from the magnitudes of the calculated electric fields (Ew, Ed, and Ei) via the transformation EW ¼ Ew þ χwd Ed þ χwi Ei ED ¼ ð1 - χwd ÞðEd þ χdi Ei Þ EI ¼ ð1 - χwd - χdi þ χwd χdi ÞEi

ð7Þ

where χRβ is a susceptibility that represents the portion of the field ER induced by Eβ. ΔE(t) and its components, ΔER(t), are then calculated as the dot product of the intrinsic electric fields and their components with δμ̌, the change in the electric dipole moment of the solute between its ground and excited electronic states. In the study by Sen et al., the probe was modeled as one of the central adenine bases in the DickersonDrew dodecamer, d(CGCGAATTCGCG)2. The requisite electric fields were computed at a point near the center of the adenine base in a 46 ns MD simulation. Auto- and cross-correlation functions were calculated with eq 5, where the susceptibilities, χRβ, in eq 7 were regarded as empirical parameters. The susceptibilities were adjusted to eliminate completely all three cross-correlation functions at a single arbitrary value of time (5 ps). Amazingly, however, the cross-correlation functions were then observed to effectively vanish at all other values of time from 1 ps to 10 ns. With the cross-correlations removed, the autocorrelation functions were then used to interpret the long time scales of the simulated total solvation correlation function, C(t). On this basis, Sen et al. concluded that water is responsible for the long time scales in TDSS experiments on DNA.


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Biographies

preliminary results suggest that the difference relates to the fact that 2AP, a base replacement probe, retains hydrogen bonding interactions to its conjugate pyrimidine base, while C102, a base pair replacement probe, creates an abasic site in the DNA. This would mean that TDSS measurements provide valuable information on how DNA flexibility and dynamics change when subjected to damage, a finding that could have significant biological implications. Further experiments utilizing base-stacked probes that do not introduce an abasic site, like C102, and that are not susceptible to electron-transfer dynamics, like 2AP, will be necessary to fully resolve the potential discrepancy. Another important theoretical challenge is to characterize how unnatural fluorescent probe molecules perturb the native dynamics of water and ions in the vicinity of DNA. Future simulation-based work must also seek to connect other properties readily available in MD simulations, such as those recently investigated for native DNA by Jana et al.,48 to the solvation correlation function time scales. If TDSS experiments on biomolecular systems are truly measuring DNA and protein dynamics, as is the assertion of the authors, this presents numerous opportunities and an associated challenge. The opportunities are to use the technique to measure biomolecular dynamics in a variety of contexts. Because of the ultrafast time scales accessible in timedependent fluorescence measurements, TDSS experiments would complement other techniques for investigating biomolecular dynamics, like NMR. Such experiments will undoubtedly require detailed MD simulations to aid in their interpretation, which motivates continued method development to facilitate the connection of theory to experiment. The crucial challenge to the field, however, is to relate the measured dynamics to motions that have actual biological significance. This will require a synergistic interaction between experiment, theory, and simulation. Finally, while this paper focused on the interpretation of TDSS measurements in DNA, the conclusions are equally valid for TDSS studies in proteins.

Kristina E. Furse graduated from the University of Richmond with a bachelor's degree in Liberal Arts in 1996. Dr. Furse then spent several years as a freelance artist, theatrical scene painter, and multimedia designer before returning to academia in 2000 to pursue a Ph.D. in Chemistry at Vanderbilt University with Terry Lybrand. From 2006 to 2010, Dr. Furse was a postdoctoral research associate in the Corcelli laboratory. She is presently a Visualization Scientist at the Center for Research Computing at the University of Notre Dame. Steven A. Corcelli graduated with an Sc.B. in Chemistry from Brown University in 1997. In 2002, he was awarded his Ph.D. at Yale University under the guidance of John Tully as a National Science Foundation Graduate Research Fellow. Dr. Corcelli then trained with James Skinner at the University of Wisconsin with the support of a Ruth L. Kirschstein National Research Service Award. In 2005, he joined the faculty at the University of Notre Dame, where he has been the recipient of a Camille and Henry Dreyfus New Faculty Award, a Sloan Research Fellowship, and a National Science Foundation CAREER award.

ACKNOWLEDGMENT S.A.C. gratefully acknowledges support from the National Science Foundation (CHE-0845736) and insightful discussions with Professors Mark A. Berg and Steven G. Boxer. In addition, the authors are thankful for support from the Center for Research Computing at the University of Notre Dame.

REFERENCES (1) (2)

(3)

(4)

The crucial challenge to the field, however, is to relate the measured dynamics to motions that have actual biological significance. This will require a synergistic interaction between experiment, theory, and simulation.

(5)

(6)

(7)

(8)

AUTHOR INFORMATION Corresponding Author: *To whom correspondence should be addressed. Email: scorcell@ nd.edu.


(9)

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Fleming, G. R.; Cho, M. H. Chromophore-Solvent Dynamics. Annu. Rev. Phys. Chem. 1996, 47, 109–134. Stratt, R. M.; Maroncelli, M. Nonreactive Dynamics in Solution: The Emerging Molecular View of Solvation Dynamics and Vibrational Relaxation. J. Phys. Chem. 1996, 100, 12981– 12996. Abbyad, P.; Shi, X. H.; Childs, W.; McAnaney, T. B.; Cohen, B. E.; Boxer, S. G. Measurement of Solvation Responses at Multiple Sites in a Globular Protein. J. Phys. Chem. B 2007, 111, 8269–8276. Li, T. P.; Hassanali, A. A. P.; Kao, Y. T.; Zhong, D. P.; Singer, S. J. Hydration Dynamics and Time Scales of Coupled WaterProtein Fluctuations. J. Am. Chem. Soc. 2007, 129, 3376– 3382. Toptygin, D.; Gronenborn, A. M.; Brand, L. Nanosecond Relaxation Dynamics of Protein GB1 Identified by the Time-Dependent Red Shift in the Fluorescence of Tryptophan and 5-Fluorotryptophan. J. Phys. Chem. B 2006, 110, 26292– 26302. Zimmermann, J.; Oakman, E. L.; Thorpe, I. F.; Shi, X. H.; Abbyad, P.; Brooks, C. L.; Boxer, S. G.; Romesberg, F. E. Antibody Evolution Constrains Conformational Heterogeneity by Tailoring Protein Dynamics. Proc. Natl. Acad. Sci. U.S.A. 2006, 103, 13722–13727. Qiu, W. H.; Zhang, L. Y.; Okobiah, O.; Yang, Y.; Wang, L. J.; Zhong, D. P.; Zewail, A. H. Ultrafast Solvation Dynamics of Human Serum Albumin: Correlations with Conformational Transitions and Site-Selected Recognition. J. Phys. Chem. B 2006, 110, 10540–10549. Zhang, L. Y.; Kao, Y. T.; Qiu, W. H.; Wang, L. J.; Zhong, D. P. Femtosecond Studies of Tryptophan Fluorescence Dynamics in Proteins: Local Solvation and Electronic Quenching. J. Phys. Chem. B 2006, 110, 18097–18103. Qiu, W. H.; Kao, Y. T.; Zhang, L. Y.; Yang, Y.; Wang, L. J.; Stites, W. E.; Zhong, D. P.; Zewail, A. H. Protein Surface Hydration



(10)

(11)

(12)

(13)

(14)

(15) (16)

(17)

(18)

(19)

(20)

(21)

(22)

(23)

(24)

(25)

(26)

Mapped by Site-Specific Mutations. Proc. Natl. Acad. Sci. U.S.A. 2006, 103, 13979–13984. Pal, S. K.; Peon, J.; Zewail, A. H. Ultrafast Surface Hydration Dynamics and Expression of Protein Functionality: AlphaChymotrypsin. Proc. Natl. Acad. Sci. U.S.A. 2002, 99, 15297– 15302. Pal, S. K.; Peon, J.; Bagchi, B.; Zewail, A. H. Biological Water: Femtosecond Dynamics of Macromolecular Hydration. J. Phys. Chem. B 2002, 106, 12376–12395. Petushkov, V. N.; van Stokkum, I. H. M.; Gobets, B.; van Mourik, F.; Lee, J.; van Grondelle, R.; Visser, A. Ultrafast Fluorescence Relaxation Spectroscopy of 6,7-Dimethyl-(8-Ribityl)-Lumazine and Riboflavin, Free and Bound to Antenna Proteins from Bioluminescent Bacteria. J. Phys. Chem. B 2003, 107, 10934–10939. Bhattacharyya, S. M.; Wang, Z. G.; Zewail, A. H. Dynamics of Water near a Protein Surface. J. Phys. Chem. B 2003, 107, 13218–13228. Zhao, L.; Pal, S. K.; Xia, T. B.; Zewail, A. H. Dynamics of Ordered Water in Interfacial Enzyme Recognition: Bovine Pancreatic Phospholipase A2. Angew. Chem., Int. Ed. 2004, 43, 60–63. Pal, S. K.; Zewail, A. H. Dynamics of Water in Biological Recognition. Chem. Rev. 2004, 104, 2099–2123. Kamal, J. K. A.; Zhao, L.; Zewail, A. H. Ultrafast Hydration Dynamics in Protein Unfolding: Human Serum Albumin. Proc. Natl. Acad. Sci. U.S.A. 2004, 101, 13411–13416. Guha, S.; Sahu, K.; Roy, D.; Mondal, S. K.; Roy, S.; Bhattacharyya, K. Slow Solvation Dynamics at the Active Site of an Enzyme: Implications for Catalysis. Biochemistry 2005, 44, 8940–8947. Zhong, D. P.; Pal, S. K.; Zhang, D. Q.; Chan, S. I.; Zewail, A. H. Femtosecond Dynamics of Rubredoxin: Tryptophan Solvation and Resonance Energy Transfer in the Protein. Proc. Natl. Acad. Sci. U.S.A. 2002, 99, 13–18. Pal, S. K.; Peon, J.; Zewail, A. H. Biological Water at the Protein Surface: Dynamical Solvation Probed Directly with Femtosecond Resolution. Proc. Natl. Acad. Sci. U.S.A. 2002, 99, 1763–1768. Peon, J.; Pal, S. K.; Zewail, A. H. Hydration at the Surface of the Protein Monellin: Dynamics with Femtosecond Resolution. Proc. Natl. Acad. Sci. U.S.A. 2002, 99, 10964–10969. Riter, R. R.; Edington, M. D.; Reck, W. F. Protein-Matrix Solvation Dynamics in the Alpha Subunit of C-Phycocyanin. J. Phys. Chem. 1996, 100, 14198–14205. Homoelle, B. J.; Edington, M. D.; Diffey, W. M.; Beck, W. F. Stimulated Photon-Echo and Transient-Grating Studies of Protein-Matrix Solvation Dynamics and Interexciton-State Radiationless Decay in Alpha Phycocyanin and Allophycocyanin. J. Phys. Chem. B 1998, 102, 3044–3052. Jordanides, X. J.; Lang, M. J.; Song, X. Y.; Fleming, G. R. Solvation Dynamics in Protein Environments Studied by Photon Echo Spectroscopy. J. Phys. Chem. B 1999, 103, 7995–8005. Changenet-Barret, P.; Choma, C. T.; Gooding, E. F.; DeGrado, W. F.; Hochstrasser, R. M. Ultrafast Dielectric Response of Proteins from Dynamics Stokes Shifting of Coumarin in Calmodulin. J. Phys. Chem. B 2000, 104, 9322–9329. Stevens, J. A.; Link, J. J.; Kao, Y. T.; Zang, C.; Wang, L. J.; Zhong, D. P. Ultrafast Dynamics of Resonance Energy Transfer in Myoglobin: Probing Local Conformation Fluctuations. J. Phys. Chem. B 2010, 114, 1498–1505. Zhang, L. Y.; Yang, Y.; Kao, Y. T.; Wang, L. J.; Zhong, D. P. Protein Hydration Dynamics and Molecular Mechanism of Coupled Water-Protein Fluctuations. J. Am. Chem. Soc. 2009, 131, 10677–10691.


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(28)

(29)

(30)

(31)

(32)

(33)

(34)

(35)

(36)

(37)

(38)

(39)

(40)

(41)

(42)

(43)

(44)

1819

Pal, S. K.; Zhao, L. A.; Zewail, A. H. Water at DNA Surfaces: Ultrafast Dynamics in Minor Groove Recognition. Proc. Natl. Acad. Sci. U.S.A. 2003, 100, 8113–8118. Pal, S. K.; Zhao, L.; Xia, T. B.; Zewail, A. H. Site- and SequenceSelective Ultrafast Hydration of DNA. Proc. Natl. Acad. Sci. U.S.A. 2003, 100, 13746–13751. Andreatta, D.; Lustres, J. L. P.; Kovalenko, S. A.; Ernsting, N. P.; Murphy, C. J.; Coleman, R. S.; Berg, M. A. Power-Law Solvation Dynamics in DNA over Six Decades in Time. J. Am. Chem. Soc. 2005, 127, 7270–7271. Andreatta, D.; Sen, S.; Lustres, J. L. P.; Kovalenko, S. A.; Ernsting, N. P.; Murphy, C. J.; Coleman, R. S.; Berg, M. A. Ultrafast Dynamics in DNA: “Fraying” at the End of the Helix. J. Am. Chem. Soc. 2006, 128, 6885–6892. Berg, M. A.; Coleman, R. S.; Murphy, C. J. Nanoscale Structure and Dynamics of DNA. Phys. Chem. Chem. Phys. 2008, 10, 1229–1242. Brauns, E. B.; Madaras, M. L.; Coleman, R. S.; Murphy, C. J.; Berg, M. A. Measurement of Local DNA Reorganization on the Picosecond and Nanosecond Time Scales. J. Am. Chem. Soc. 1999, 121, 11644–11649. Brauns, E. B.; Madaras, M. L.; Coleman, R. S.; Murphy, C. J.; Berg, M. A. Complex Local Dynamics in DNA on the Picosecond and Nanosecond Time Scales. Phys. Rev. Lett. 2002, 88, 158101. Brauns, E. B.; Murphy, C. J.; Berg, M. A. Local Dynamics in DNA by Temperature-Dependent Stokes Shifts of an Intercalated Dye. J. Am. Chem. Soc. 1998, 120, 2449–2456. Lee, B. J.; Barch, M.; Castner, E. W.; Volker, J.; Breslauer, K. J. Structure and Dynamics in DNA Looped Domains: CAG Triplet Repeat Sequence Dynamics Probed by 2-Aminopurine Fluorescence. Biochemistry 2007, 46, 10756–10766. Qiu, W. H.; Zhang, L. Y.; Kao, Y. T.; Lu, W. Y.; Li, T. P.; Kim, J.; Sollenberger, G. M.; Wang, L. J.; Zhong, D. P. Ultrafast Hydration Dynamics in Melittin Folding and Aggregation: Helix Formation and Tetramer Self-Assembly. J. Phys. Chem. B 2005, 109, 16901–16910. Golosov, A. A.; Karplus, M. Probing Polar Solvation Dynamics in Proteins: A Molecular Dynamics Simulation Analysis. J. Phys. Chem. B 2007, 111, 1482–1490. Halle, B.; Nilsson, L. Does the Dynamic Stokes Shift Report on Slow Protein Hydration Dynamics?. J. Phys. Chem. B 2009, 113, 8210–8213. Furse, K. E.; Corcelli, S. A. The Dynamics of Water at DNA Interfaces: Computational Studies of Hoechst 33258 Bound to DNA. J. Am. Chem. Soc. 2008, 130, 13103–13109. Furse, K. E.; Lindquist, B. A.; Corcelli, S. A. Solvation Dynamics of Hoechst 33258 in Water: An Equilibrium and Nonequilibrium Molecular Dynamics Study. J. Phys. Chem. B 2008, 112, 3231–3239. Li, T. P.; Hassanali, A. A.; Singer, S. J. Origin of Slow Relaxation Following Photoexcitation of W7 in Myoglobin and the Dynamics of Its Hydration Layer. J. Phys. Chem. B 2008, 112, 16121–16134. Nilsson, L.; Halle, B. Molecular Origin of Time-Dependent Fluorescence Shifts in Proteins. Proc. Natl. Acad. Sci. U.S.A. 2005, 102, 13867–13872. Pal, S.; Maiti, P. K.; Bagchi, B.; Hynes, J. T. Multiple Time Scales in Solvation Dynamics of DNA in Aqueous Solution: The Role of Water, Counterions, and Cross-Correlations. J. Phys. Chem. B 2006, 110, 26396–26402. Sen, S.; Andreatta, D.; Ponomarev, S. Y.; Beveridge, D. L.; Berg, M. A. Dynamics of Water and Ions near DNA: Comparison of Simulation to Time-Resolved Stokes-Shift Experiments. J. Am. Chem. Soc. 2009, 131, 1724–1735.



(45)

(46)

(47)

(48)

(49)

Carter, E. A.; Hynes, J. T. Solvation Dynamics for an Ion-Pair in a Polar-Solvent ; Time-Dependent Fluorescence and Photochemical Charge-Transfer. J. Chem. Phys. 1991, 94, 5961–5979. Laird, B. B.; Thompson, W. H. On the Connection between Gaussian Statistics and Excited-State Linear Response for Time-Dependent Fluorescence. J. Chem. Phys. 2007, 126, 211104. Bernard, W.; Callen, H. B. Irreversible Thermodynamics of Nonlinear Processes and Noise in Driven Systems. Rev. Mod. Phys. 1959, 31, 1017–1044. Jana, B.; Pal, S.; Bagchi, B. Enhanced Tetrahedral Ordering of Water Molecules in Minor Grooves of DNA: Relative Role of DNA Rigidity, Nanoconfinement, and Surface Specific Interactions. J. Phys. Chem. B 2010, 114, 3633–3638. Edwards, K. J.; Jenkins, T. C.; Neidle, S. Crystal-Structure of a Pentamidine Oligonucleotide Complex;Implications for DNA-Binding Properties. Biochemistry 1992, 31, 7104–7109.


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Molecular Dynamics Simulations of DNA Solvation Dynamics

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