Two-Dimensional Linear Dichroism Spectroscopy for Identifying

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Two Dimensional Linear Dichroism Spectroscopy for Identifying Protein Orientation and Secondary Structure Composition Guozhen Zhang, Jun Li, Peng Cui, Tao Wang, Jun Jiang, and Oleg V. Prezhdo J. Phys. Chem. Lett., Just Accepted Manuscript • DOI: 10.1021/acs.jpclett.7b00311 • Publication Date (Web): 15 Feb 2017 Downloaded from http://pubs.acs.org on February 16, 2017

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The Journal of Physical Chemistry Letters

Two Dimensional Linear Dichroism Spectroscopy for Identifying Protein Orientation and Secondary Structure Composition Guozhen Zhang,†,1 Jun Li,†,1 Peng Cui,1 Tao Wang,1 Jun Jiang,*,1 Oleg V. Prezhdo2 1

Hefei National Laboratory for Physical Sciences at the Microscale, Collaborative Innovation Center of

Chemistry for Energy Materials, School of Chemistry and Materials Science, University of Science and Technology of China, Hefei, Anhui 230026, P. R. China. 2

Departments of Chemistry and Physics and Astronomy, University of Southern California, Los Angeles, California 90089, United States.

Abstract: Quantitative measurements of protein orientation and secondary structure composition are of great importance for protein biotechnology applications and disease treatments, and yet they are technically challenging for a spectroscopic study. Based on quantum mechanics/molecular mechanics simulations, we demonstrate that two dimensional (2D) linear dichroism spectroscopy is capable of probing the direction of α-helix motifs in proteins. Compared to the conventional linear dichroism (LD) spectra, 2D spectra double the measurable range of orientation of secondary structures. In addition, by calculating the ratio of transverse ππ* signals to longitudinal ππ* signals in 2D spectra, we can achieve quantitative measurement of the fraction of α-helix content in a protein.

Protein structure determination is crucial for understanding the physiological activities of proteins and their biotechnology applications due to close ties between function and structure.1 Especially, knowledge of dynamic protein orientation and secondary structure composition plays a key role in studying fibrillationinduced diseases, ion channel proteins, membrane protein topology, and bioelectronics.2-6 Determining amyloid and amyloid-like fibril structures and their direction of growth is indispensable for disclosing the disease etiology and casting insight into treatment and prevention of many serious diseases, such as Alzheimer’s disease, Huntington’s disease, and genomic CAG repeats.2-3 Deep understanding of how an ion channel works requires investigating the orientation changes of the ion channel protein in response to changes in its environment. 4 Studying biological behavior of membrane proteins relies on knowledge of their topology, including their orientation relative to the membrane.5 For bioelectronics, manipulation of charge transport within a protein transistor requires precise control of protein orientation in the molecular junction.6 In recent years, several groups have developed new techniques that can be used for studying protein orientations. Stupp et al. have achieved sophisticated fibril orientation control through X-ray triggered self-assembling, making it possible to guide amyloid fibril growth to examine the dependence of fibril function on orientation.7 Ye et al. have reported a spectroscopic study of ion channel gating action, in which they tuned membrane potential and pH value to alter the orientation of a model ion channel.4 Richards et al. have developed a simple method to preserve orientation of membrane proteins during protein transfer from cell to supported bilayers using cell blebs as an intermediate.8 Chen et al. realized orientation control of a protein using chemical binding between the residue of the protein and the monolayer on a metal electrode surface.6 Proteins involved in the above mentioned studies typically are dynamic, non-crystallized, or insoluble (for fibrils). These

conditions pose a great challenge for making quantitative measurements or for control of protein real-time orientation and structural composition using conventional experimental tools, such as atomic force microscopy (AFM), X-ray diffraction (XRD) or nuclear magnetic resonance (NMR).1, 9 Great efforts have been made to develop alternative spectroscopic approaches (e.g. Fourier transform IR, sum frequency generation, and polarized Raman) that are able to detect protein orientation by measuring the angular dependence of a specific spectral signal on the basis of a reference coordinate frame that is defined in advance.4, 10-17 Albeit substantial progress has been made in the measurement of protein orientation using spectroscopic techniques, due to limitations peculiar to each method, no single method is capable of tackling all tasks required to measure protein orientation. Therefore, there is enough space for new methods to complement these extant techniques for exploring protein conformation and orientation. Polarized ultraviolet (UV) and visible Raman spectroscopies are useful in determining protein sidechain orientation by probing the transition moment of a specific amino acid,18-19 but the difficulty of assigning every chromophore hinders the identification of the global structure of a protein. Linear dichroism (LD) spectroscopy is a powerful approach for determining the global orientation of proteins. Indeed, femtosecond LD spectroscopy has been successfully applied to biomolecules and inorganic surfaces.20-22 Recent flow LD studies have shown exceptional results for measuring the orientation of protein secondary structural motifs, the structure and orientation of membrane proteins interacting with guest molecules and of amyloid fibrils, and for determining how agents dock with enzymes or fibers.23 However, local information is often missing due to sensitivity to interactions with adjacent chromophore transitions. To study local and global structures simultaneously, it is necessary to go beyond the conventional one-dimensional spectroscopies. Twodimensional (2D) resonant laser spectroscopy is capable of achieving

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the goal by monitoring vibrational and electronic excitations of molecules and their complexes.24 2D spectroscopies provide more information about structure and dynamics than 1D spectroscopies due to high sensitivity to electronic correlations and ultrafast electronic dynamics in proteins and other nano systems.25 Recent advances in intense UV femtosecond lasers with high repetition rates have led to successful capture of protein dynamic pictures by the 2D UV spectroscopy (2DUV).26 Because both LD and 2D spectroscopic techniques do not require crystallization of samples and are highly sensitive to the structural changes of proteins, one can use them to probe protein conformation and orientation as a complement to the existing techniques. In this work, by combining the advantages of the LD and 2D UV spectroscopies, we propose a new technique termed two-dimensional linear dichroism spectroscopy (2DLD). Similarly to LD, the signals of 2DLD are defined as the difference between the 2DUV signals produced by two perpendicular polarization configurations.25 We also define 2DRD as the ratio between the transverse and longitudinal 2D response signals of α-helix protein motifs. 2DLD and 2DRD respectively aim at the quantitative measurement of orientations of the secondary structures of proteins and the proportional occurrence of different secondary structural motifs under specific (e.g. dynamic, non-crystallized, or insoluble) conditions, expanding the power of 2D techniques in the area of protein structural determination. Two peptide bond transitions of n→π*(denoted as nπ* below) at 45500 cm-1 (~220 nm) and π→π* (denoted as ππ* below) at 53000 cm-1 (~190 nm), as depicted by Figure 1A, are the primary components of the ultraviolet adsorption spectra of the protein backbone.27 Polarization of the backbone transitions due to inter- and intra-molecular interactions causes spectral signals to be dependent on backbone orientation.28 Thus, the net polarization of transitions in proteins is strongly affected by the secondary structures that determine the allowed combinations of coupled local transitions. Other than random coil motifs, the most common ordered secondary structures are α-helices and β-sheets. Here, we ignore nπ* transitions which normally induce much weaker signals than ππ* transitions. It is known that amide ππ* transitions in α-helices and β-sheets have different orientational dependencies. Therefore, we can choose the ππ* transition as the probe in this study. Due to the Davydov splitting28 effect in α-helices, ππ* transitions are split into transitions polarized parallel to the helical axis (longitudinal transition) at the lower energy 48000 cm-1 (~210 nm), and transitions polarized perpendicular to the helical axis (transverse transition) at the higher energy 53000 cm-1 (~190 nm). Figure 1B displays a typical α-helical protein structure in the xyz coordinate space. The two ππ* transition components polarized parallel and perpendicular to the α-helical axis are defined as µL and µT, respectively. Meanwhile, the ππ* transition in β-sheets is characterized by µT alone, because of the lack of the parallel component of this transition. Figure 1. (A) UV transitions of a peptide bond on a protein backbone. (B) Schematic representation of typical α-helix and β-sheet proteins in the xyz coordinate space, in which the z axis represents the orientation axis.

LD is the difference between the absorbance of light (LA) polarized respectively parallel and perpendicular to a specific orientation. Taking the z axis in Figure 1B as the orientation axis,

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absorption of light polarized in the z direction is the parallel LA (A||), while the average of the absorbances in the x and y directions accounts for the perpendicular LA (A⊥), equation (1) below. The orientations of the α-helix and β-sheet proteins with respect to the z axis are described by the angle θ, as shown in Figure 1B. The LDs of both motifs depend on the longitudinal (L) and transverse (T) transition dipole moments µL and µT, and consequently, have a 2θ dependence (See equation (2) and supporting information for details). A 2DUV signal is measured using four coherent short laser pulses, labeled by their wavevectors k1, k2, k3, k4. In the direction of k4 = -k1 + k2 + k3, the 2DUV signal is detected as a function of three delay times: t1 between pulses k1 and k2, t2 between pulses k2 and k3, and t3 between pulses k3 and k4. The 2D echo signals are determined by the two-dimensional Fourier transforms t1→Ω1 and t3→Ω3 with t2 set to zero. Calculations were performed using the protocol given in our previous work29-32 for the two polarization configurations zzzz and xxxx (i.e. four laser pulses with all polarizations along the z or x axis), and provide non-chiral 2DUV zzzz and xxxx spectra. More details of calculating the 2D photon echo signals are described in the supporting information. We then define the 2D linear dichroism (2DLD) spectrum as the difference between the 2DUV signals with zzzz and xxxx polarizations:

1 LD = A|| − A⊥ = Az − ( Ax + Ay ) 2 LD(µT ) ∝ µT2 (sin 2 θ − cos 2 θ ) ∝ − µT2 cos 2θ

(2)

LD(µ L ) ∝ − µ L2 (sin 2 θ − cos 2 θ ) ∝ µ L2 cos 2θ We find that the 2DLD signals are functions of cos2θ (see equation (3) and supporting information), which can be translated into protein orientation. Moreover, the sidebands resulting from coupling of different signals are dependent on orientation as well. Most proteins contain both α-helices and β-sheets. To characterize a protein with content fraction γ of α-helix structure, we calculate 2DRD, the ratio between the transverse ππ* signals at 53000 cm-1 and the longitudinal ππ* signals at 48000 cm-1 in the 2DUV zzzz spectra (shorted as T/L ratio):

2DRD =

2DUVzzzz ,T 2DUVzzzz , L

=

µT4 sin 4 θ + η1 γ µ L4 cos 4 θ + η2

(4)

4

where η1 and η2 represent the background signals that might be induced by the resonant tail of protein backbone and aromatic side-

2DLD = 2DUVzzzz − 2DUVxxxx 2DLD(µT ) ∝ µT4 (sin 4 θ − cos 4 θ ) ∝ − µT4 cos 2θ 4 L

4

4

(3)

4 L

2 DLD( µ L ) ∝ − µ (sin θ − cos θ ) ∝ µ cos 2θ chain transitions. We have studied the UV spectra of the following proteins: two αhelix-only proteins – tropomyosin (PDB code: 2d3e) and hemoglobin (PDB code: 1hda), two β-sheet only proteins – crystals of amyloid-like fibrils (PDB code: 1yjp) and lentil lectin (PDB code: 1les), two proteins containing both α-helices and β-sheets – FtsZ (PDB code: 1fsz) and pyruvate kinase (PDB code: 1mol), and the 32residue β-amyloid (Aβ9-40) fibril. The X-ray crystal structures were taken from the RSCB protein data bank and used as the starting geometry for each protein. To reduce the computation cost, we extracted essential fragments of these proteins for molecular simulations. For example, we trimmed a fragment of two helix chains (TROP1: SER215-LYS264 of chain A and B in 2d3e.pdb) taken from tropomyosin (2d3e), and a fragment of the single helix chain from hemoglobin (1hda), respectively, to investigate the


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angular dependence of the 2DLD spectra on the orientation of the pure α-helix peptides. We have used a computational protocol based on an efficient quantum mechanics/molecular mechanics (QM/MM) algorithm called "exciton Hamiltonian with electrostatic fluctuations" (EHEF).29 In this theoretical framework for the 2DUV signal, the ππ* transitions of the protein backbone are described by the Frenkel exciton model (equation 5) derived from the Heitler-London and adiabatic approximations.33

where ma is the ath electronic transition on the mth peptide unit, (in our case, a = 2 for ππ*). B̂ma† is the creation operator that promotes the mth peptide unit into the ath excited state, and B̂ma is the conjugated annihilation operator. This couple of operators comply with the Pauli commutation rules [B̂ma, B̂nb†] = δmn(1 − 2B̂mb†B̂ma). The ground-state is represented by |0>, and its energy is given by = 0. In the case of single exciton, the mth singly-excited state energy is given as = εma, and the resonant coupling between two different excited states m and n is given by = Jma, nb. (See more description in the supporting information.) EHEF provides an interface for reading molecular dynamics (MD) simulation trajectories and generating QM charge distributions. MD simulations of proteins in aqueous solution were performed using the NAMD 2.7 software package34 with the CHARMM force field35. The protein of interest was set in the center of a rectangular simulation box under periodic boundary condition, and was surrounded by 10 Å thick layers of TIP3P36 water on all sides in three dimensions. Long-range electrostatic interactions were treated with the particle-mesh Ewald (PME) method.37 The real space cutoff for non-bonded interactions was set to 12 Å. The MD evolution time step was 1 fs. All MD simulations started with a 600 ps heating from 0 to 310 K that followed a 5000 step energy minimization. After a 2 ns equilibration run in the NPT ensemble at T = 300 K and P = 1 atm, we generated a 16 ns NPT trajectory, and recorded structures every 400 fs. The resulting NPT ensemble of MD geometric snapshots was used to compute the effective UV Hamiltonian and spectra. Parameters of the transition energies of the resonant couplings, and the electric and magnetic dipole moments for an isolated protein backbone amide units were extracted from the DichroCalc package,38 which itself is based on the excited states of Nmethylacetamide (NMA) computed by CASSCF/SCRF39 (the complete-active space self-consistent-field method implemented within a self-consistent reaction field) in MOLCAS40. The electronic charge distributions of amino side chains in proteins and surrounding water molecules were extracted from the EHEF program, which itself is based on density functional theory (DFT) calculations at the B3LYP/6-311++G** level with the GAUSSIAN03 package.41 Using the matrix method in the DichroCalc core we constructed effective fluctuating QM Hamiltonians. The SPECTRON code42 developed by the Mukamel group was used to compute the LA and LD spectra based on 2000 MD snapshots; computations of the 2DUV and 2DLD spectra were based on 2000 MD snapshots as well. The accuracy and predictive power of our simulation protocol have been validated previously for various proteins.29-32 There, we compared the computed LA and LD spectra with available experimental data, and achieved good agreements (See supporting information for details). Therefore, we employ the same simulations here. The definition of the orientation is as depicted in Figure 1. The

Z-axis is taken as the direction along the α-helix or β-sheet in the initial state. Using this as a reference point, we define an angle θ to represent the orientation relative to the Z-axis. Thus, θ=0° is for the initial orientation. What matters most is how the 2DLD signal changes in response to changes in θ. If the changes in the signal follow the same pattern for the two species, then the species likely experience similar changes in the orientation. The two pure α-helix protein fragments under investigation are (1) a fragment of two chains (TROP1: SER215-LYS264 of chain A and B in 2d3e.pdb) taken from tropomyosin (PDB code: 2d3e), and (2) a fragment of a single chain taken from hemoglobin (HEMO1, PDB code: 1hda). The 2DUV spectra of TROP1 in different orientations (Figure 2A) have been computed. The first panel in Figure 2B shows the 2DUV zzzz spectra of TROP1 in five orientations (θ=0°, 22.5°, 45°, 67.5°, 90°). For TROP1 along the z axis, the diagonal peak is centered at 48000:48000 cm-1 (i.e. Ω1=Ω3=-48000 cm-1), accompanied by two relatively weak positive cross-peaks (green to red). As the orientation of TROP1 changes from θ=0° to θ=90°, these three main peaks shift from lower to higher energy, with the diagonal peak becoming centered at the 53000:53000 cm-1 point for θ=90°.

Figure 2. (A): The structures of TROP1 as a function of θ, where θ is the angle between the TROP1 central axis and the z axis. (B): The 2DUV zzzz and 2DLD spectra of TROP1 (upper panel) in different orientations, from left to right: θ= 0°, 22.5°, 45°, 67.5°, 90°; and (bottom panel) the T/L ratio of the 2D zzzz signal, as a function of θ. For (C): TROP1, (D): HEMO1, 2D spectra obtained by averaging over 2000 MD snapshots.

The 2DLD signals calculated for TROP1 (Figure 2B) show richer spectral features and are distinctly different from the 2DUV zzzz signals. At θ=0°, there is a strong negative (blue) diagonal peak accompanied by two weak positive (red) sidebands, and a strong positive (red) diagonal peak with two weak negative (blue) sidebands at 48000 cm-1 and 53000 cm-1, respectively. As θ increases from 0° to 90°, the diagonal peaks change sign, while the sidebands experience opposite yet milder sign changes, as compared to the diagonal peaks. The behavior of the 2DLD positive and negative signal peaks is in agreement with that of the LD signals on the x and z axes. Figures 2C and 2D show the variation of the T/L ratio as a function of the orientation angle. There is a good trigonometric functional relation between the T/L ratio and the orientation angle for both TROP1 and HEMO1, showing a tan4θ dependence. In addition, because TROP1 and HEMO1 are both pure α-helix structures, the value of γ in equation (4) is 1, from which it follows that the value of µ T4 / µ L4 is between 0.10~0.11.


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The 2DUV zzzz and 2DLD spectra of the β-sheet protein protofibril were calculated in five different orientations (θ=0°, 22.5°, 45°, 67.5°, 90°) (Figures 3A and 3B). As the angle increases, the signal peak at 48000 cm-1 in the 2DUV zzzz spectra weakens, and the signal peak at 53000 cm-1 strengthens. This trend is not as distinct as for the 2DUV zzzz spectra of TROP1. Moreover, as θ increases from 0° to 90°, the side peaks of the protofibril show prominent positive signals. The 2DLD spectra exhibit different features than the 2DUV zzzz spectra. The 2DLD spectra show an asymmetrical distribution along the diagonal. At θ=0°, the 2DLD signal has a negative peak on the diagonal near ~48000 cm-1 accompanied by a weak positive side peak, while it has a positive peak on the diagonal near ~53000 cm-1

Figure 3. (A): The structures of protofibril 1yjp as a function of θ, where θ is the angle between the protofibril central axis and z axis. (B): The 2D signals (upper panel) of the protofibril in different orientations, from left to right: θ= 0°, 22.5°, 45°, 67.5°, 90°; and (bottom panel) the T/L ratio of the 2D zzzz signal as a function of θ. For (C): protofibril (1yjp), (D): protofibril (1les-b), 2D spectra obtained by averaging over 2000 MD snapshots

with a weak negative side peak nearby. As θ increases from 0 to 90°, all signals of the 2DLD spectra switch their signs: the negative signal peak on the diagonal near ~48000 cm-1 (due to the π→π* backbone transition) changes from negative to positive, while the positive signal peak near 53000 cm-1 changes from positive to negative. These changes are in line with those observed in the LD. The T/L ratios follow a sin4θ dependence in the two β-sheets as well. Figure 3 shows a clear sin4θ dependence of the T/L ratio in both lyjp and 1les-b. As the helical structure content is 0 for both 1yjp and 1les-b β-sheet proteins, γ is 0 in equation (6). It follows that the µT4 / η 2 value is between 3.05~3.44. This is a consequence of the fact that the Davydov splitting of the amide ππ* transition at ~190nm in β-sheets is only polarized perpendicular to the sheet. Through the study of the pure α-helix and β-sheet structures, we found that the 2DUV zzzz and 2DLD spectra show remarkable dependence on the orientation of the secondary structural motifs. Subsequently, we applied the analysis to two proteins containing both motifs: FtsZ (PDB code: 1fsz) and monellin (PDB code: 1mol). Figures 4A and 4B display the FtsZ structure in five orientations and the corresponding 2D signal spectra, respectively. The 2DUV zzzz

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spectral signal has broad negative bands along the diagonal, and its intensity along the diagonal at ~48000 cm-1 decreases with increasing θ. On the contrary, the intensity on the diagonal at ~53000 cm-1 increases with increasing θ. However, the relationship between 2DUV zzzz and θ for FtsZ is not as distinct as it is for either α-helix or β-sheet. The dependence of the 2DLD spectra on orientation (characterized by θ) is readily seen: at θ=0°, a prominent negative signal appears near 48000 cm-1 coupled with a pair of weak positive sidebands, and a prominent positive signal appears near 53000 cm-1, coupled with a pair of weak negative sidebands. From 0° to 90°, negative signals gradually turn to positive, and vice versa. The trends

apparent in Figure 4B follow the same pattern as those in Figure 2B and Figure 3B. This suggests that both α-helixes and β-sheets in FtsZ likely exhibit similar orientation changes, as described in Figures 1-3.

Figure 4. (A): The structures of FtsZ as a function of θ, where θ is the angle between the FtsZ central axis and the z axis. (B): The 2DUV zzzz and 2DLD signals for FtsZ (upper panel) in different orientations, from left to right: θ= 0°, 22.5°, 45°, 67.5°, 90°; and (bottom panel) the T/L ratio of the 2D zzzz signal as a function of θ. For (C): FtsZ (1fsz), and (D): monellin (1mol), 2D spectra obtained by averaging over 2000 MD snapshots.

Next, we studied the variation of the T/L ratio with respect to the orientation angle in two real systems: FtsZ (PDB code: 1fsz) and monellin (PDB code: 1mol). Figures 4C and 4D show a clear tan4θ dependence, the same as that found for the α-helix case. Equation (4) gives the content fraction γ equal to 0.50 in FtsZ and 0.27 in monellin. Comparing these values with the content fractions computed using the DSSP program,43-44 which produces 0.44 in FtsZ, and 0.22 in monellin, we conclude that the 2D signals can predict the helix content with high accuracy. Finally, we investigate amyloid fibrils, which are associated with more than 20 neurodegenerative diseases. The mechanism of amyloid fibril formation involves many intermediate states.30 Here, we choose two model structures of amyloid fibrils: one is composed of six Aβ9-40 molecules (Figure 5A) (denoted as A6 below) and the other contains two Aβ9-40 molecules (denoted as A2 below).


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For the 2D signals (Figure 5B), both the 2DUV zzzz and 2DLD spectra of A6 show distinct changes in response to the change of orientation. At θ=0°, the 2DUV zzzz spectra are dominated by the negative diagonal ~48000 cm-1 peak accompanied by two very weak positive sidebands. As the angle increases, the signal weakens gradually at 48000 cm-1, while the negative signal intensity on the diagonal at ~53000 cm-1 is strengthened accordingly. At θ=90°, the signal peak intensity and positive sidebands at diagonal 53000 cm-1 all reach a maximum. The 2D zzzz signal at 53000 cm-1 is strengthened with the increase of the angle between the helical axis and the z axis, while the signal at 48000 cm-1 is weakened. At θ=0°, the 2DLD signal has a negative diagonal peak accompanied by two weak positive sidebands at 48000 cm-1 and a positive diagonal peak accompanied by two weak negative sidebands at 53000 cm-1. As the angle between the helical axis and the z axis increases, the feature signal at 48000 cm-1 turns positive, and that at 53000 cm-1 experiences the opposite change, which agrees with the inversion of the LD spectral signal peaks.

associated with orientation of the structures, suggesting a path towards a higher resolution spectroscopy. The reported analysis of the 2D spectroscopic signals as a function of orientation also allows us to obtain the fractional helical content of protein structures. The above results demonstrate the capability of the 2DLD spectroscopy to establish a quantitative relationship between the secondary structure composition (α-helix/β-sheet ratio) and protein orientation. This capability will be the very useful for future studies by allowing one to extract one type of information from other types and making protein secondary structure predictable from spectral information. The demonstrated methodology can both complement the existing spectroscopic techniques for protein structure determination, and open up new windows for probing amyloid fibrils and other structures, their dynamical fluctuations, and associated photochemical and photophysical processes, which are crucial for the understanding and manipulation of protein functionality. We hope that the presented theoretical results will encourage the corresponding 2DLD experimental studies on protein orientation in the near future.

ASSOCIATED CONTENT AUTHOR INFORMATION Corresponding Author *Email: [email protected]

ORCID Jun Jiang: 0000-0002-6116-5605

Notes The authors declare no competing financial interest. Author Contributions

† These authors contributed equally. ACKNOWLEDGMENTS Figure 5. (A): The structures of A6 as a function of θ, where θ is the angle between the FtsZ central axis and the z axis. (B): The 2D signals for A6 (upper panel) in different orientations, from left to right: θ= 0°, 22.5°, 45°, 67.5°, 90°; and (bottom panel) the T/L ratio of the 2D zzzz signal as a function of θ. For (C): A6 Fibril, (D): A2 Fibril, 2D spectra obtained by averaging over 2000 MD snapshots.

Changes in the T/L ratio with the orientation angle, as seen in Figures 5C and 5D, exhibit a tan4θ dependence for both A6 and A2. Insignificant differences are shown in the background signal (A6: η1=1.63, η2=0.11 and A2: η1=1.34, η2=0.06). Combining the results shown in Figures 5C and 5D and equation (4) we obtain γ=0.45. Since the amyloid fibril structure contains two parallel β sheets facing each other, our results suggest that an amyloid fibril can be treated as a semi-half-helix structure. Quantitative determination of protein orientation and secondary structure composition in real-time under non-crystallized or insoluble conditions is an important yet challenging capability for protein structure analysis. In this work, we showed that the 2DLD spectroscopy provides a significant advance towards this goal, because the 2DLD signals can be used to monitor the changes in orientation of helical, sheet, and amyloid fibril protein structures in solution. Different orientation dependencies for different structural motifs have been established simultaneously. Compared to onedimensional spectra, 2D spectra provide us with more information

This work was financially supported by the 973 Program (No. 2014CB848900), NSFC (No. 21633006), CAS Strategic Priority Research Program B (No. XDB01020000), Hefei Science Center CAS (2015HSC-UP011). OVP acknowledges support of the US National Science Foundation, grant no. CHE-1565704.

Supporting Information Available Linear absorption (LA) and linear dichroism (LD), exciton model for the ππ* transitions of the protein backbone, theory and simulation of the 2DUV signal, theory of 2DLD and 2DRD, and LA and LD spectra of the α-helix proteins, β-sheet proteins, mixed α-helix and βsheet proteins, and amyloid fibril protein. This material, is available free of charge via the Internet at http://pubs.acs.org

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Two-Dimensional Linear Dichroism Spectroscopy for Identifying

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