Investigations of conformational transitions in proteins and RNA using 2DCOS Raman and 2DCOS Raman optical activity spectroscopies

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Journal of Molecular Structure 883–884 (2008) 187–194 www.elsevier.com/locate/molstruc

Investigations of conformational transitions in proteins and RNA using 2DCOS Raman and 2DCOS Raman optical activity spectroscopies Lorna Ashton, Alison Hobro, Graeme L. Conn, Mansour Rouhi, Ewan W. Blanch * Manchester Interdisciplinary Biocentre and Faculty of Life Sciences, University of Manchester, 131 Princess Street, Manchester M1 7DN, UK Received 30 October 2007; received in revised form 3 December 2007; accepted 6 December 2007 Available online 15 December 2007

Abstract Coupling the structural sensitivity of vibrational spectroscopies, such as Raman scattering and Raman optical activity (ROA), with the analytical insight provided by 2D generalized correlation analysis provides a new and exciting set of tools for structural biology. These new tools of 2DCos Raman and 2DCos ROA have the potential to provide new information on the mechanisms of conformational transitions of proteins and ribonucleic acids (RNA), such as those for folding and misfolding. However, the complexity of biomolecular transitions and the practicalities of spectroscopic data collection can make the analysis of 2DCos Raman and 2DCos ROA contour plots difficult. In this paper we present a summary of our methodology for obtaining reliable 2DCos contour maps and for their interpretation for folding and misfolding transitions in polypeptides, proteins and RNA. We demonstrate that our protocols for data pre-treatment greatly improve the quality of 2DCos contour maps, revealing the large amount of structural information that they contain, and then show that a moving window analysis is required to adequately follow biomolecular conformational transitions in detail. Ó 2007 Elsevier B.V. All rights reserved. Keywords: 2D correlation; Raman; Raman optical activity (ROA); Protein folding; RNA folding

1. Introduction The function and behaviour of biomolecules such as proteins and ribonucleic acids (RNA) are determined by their structure. From the synthesis and subsequent folding of a protein or RNA molecule, and throughout its functional existence until degradation by normal cellular processes, the conformational changes or transitions undergone by the protein or RNA molecule regulate its interactions with the environment and other molecules. Therefore, it is fundamentally important to understand these structural changes, and this is a common research theme of structural biology. Knowledge of the mechanisms of conformational transitions is also important for protein *

Corresponding author. Tel.: +44 0161 306 5819; fax: +44 0161 306 8918. E-mail address: [email protected] (E.W. Blanch). 0022-2860/$ - see front matter Ó 2007 Elsevier B.V. All rights reserved. doi:10.1016/j.molstruc.2007.12.002

engineering and investigations of diseases resulting from the misfolding of proteins, such as Alzheimer’s, Parkinson’s and type II diabetes mellitus [1–3]. The principal high resolution techniques used by biologists to investigate the structures of proteins and RNAs, namely X-ray diffraction and nuclear magnetic resonance (NMR), are often inapplicable to the non-native structures exhibited by biological molecules undergoing conformational transitions. Vibrational spectroscopies are particularly incisive probes of conformational transitions as they are sensitive to a large amount of structural information, are nondestructive and are applicable to all non-native states supported by proteins and RNAs [4–6]. Specifically, Raman and Raman optical activity (ROA) spectroscopies have been used to monitor conformational changes between native states of folded proteins and their partially or fully unfolded denatured states [7–10]. ROA spectra can be acquired either by measuring a small difference in inten-

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sity of Raman scattering from chiral molecules in rightand left-circularly polarized incident light (ICP) or, equivalently, by measuring a small circularly polarized component in the scattered light using unpolarized incident light (SCP) [11–14]. The Raman spectra of proteins and RNAs contain many bands arising from side chains of residues and bases, respectively, while their corresponding ROA spectra are dominated by bands arising from the polypeptide or phosphodiester backbone, respectively [15,16]. Consequently, Raman and ROA spectra, which can be measured simultaneously under the same experimental conditions, provide complementary information about conformational transitions in proteins and RNAs. However, the high information contents of Raman and ROA spectra of proteins and RNAs can make their analysis difficult. Generalized two-dimensional (2D) correlation spectroscopy, which is based on a cross-correlation analysis of previously measured spectra as a function of two independent wavenumber positions, thereby spreading the spectra over an additional dimension, is an attractive approach to studying structural changes. This generalized approach

was discovered by Noda and can be applied to any electromagnetic probe monitoring dynamic spectral variations of a system induced by an external perturbation [17]. 2D correlation analysis has been successfully applied to vibrational spectroscopic data of proteins including spectra from infrared (IR) [18] and near infrared (NIR) absorbance [19], vibrational circular dichroism (VCD) [20], conventional Raman [10,21–23] and ROA spectroscopies [24–26]. Detailed structural information and new insights into the mechanisms of conformational transitions has been revealed in several of these studies but the analysis of 2D contour maps for biological molecules is more complicated than for simpler chemical species. In this paper we describe the protocols that we have developed for the production of reliable 2D correlation maps for Raman and ROA spectra of proteins and RNAs, and their interpretation. 2. Experimental The synchronous and asynchronous 2DCos Raman and 2DCos ROA correlation plots shown in Figs. 1–4 were pro-

Fig. 1. 2D Raman correlation asynchronous plots generated from temperature-dependent spectral intensity variations of poly(L-lysine) recorded within the temperature range 4–52 °C in 8 °C intervals with (a) no data pretreatments, (b) after baseline subtraction, smoothing and normalization standardizing spectral acquisition time to a data collection period of 7 h and (c) after baseline subtraction, smoothing, normalization and further baseline subtractions. Adapted from [24].

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Fig. 2. 2D ROA correlation asynchronous plots generated from temperature-dependent spectral intensity variations of poly(L-lysine) recorded in temperature range 4–52 °C in 8 °C intervals with (a) no data pretreatments, (b) after electronic baseline subtractions applied with Origin 7.5 software to each individual spectrum and carried out across the entire spectrum and (c) after baseline subtraction, smoothing and normalization standardizing spectral acquisition time to a data collection period of 7 h. Adapted from [24].

Fig. 3. (a) Synchronous 2D Raman correlation plots generated from pretreated temperature-dependent spectral intensity variations of poly(L-lysine) recorded within the temperature range 4–52 °C in 8 °C intervals at maximum contour levels (i) 4 contours, (ii) 8 contours, (iii) 16 contours and (iv) 32 contours. (b) Autocorrelation intensities of the synchronous spectra presented as a slice spectrum of the diagonal, where m1 = m2. Dashed horizontal lines indicate intensity levels as displayed using 4, 8, 16 and 32 contours.

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duced from data reported previously [24,26] and calculated using MATLAB software and freely available 2D correlation software [27]. We direct the reader to these papers for the specific conditions of each experiment, as the purpose of this paper is to discuss the development of our analysis of 2DCos Raman and 2DCos ROA experiments. The 2DCos moving window plot shown in Fig. 5 was generated from temperature-dependence Raman spectra collected for Mg2+ free-Ligand Binding Domain RNA (at 20 mg/mL concentration in 100 mM MOPS buffer) at temperatures ranging from 22–82 °C, in 4 °C steps. The Raman spectra for this RNA were measured using a ChiralRAMAN spectrometer (BioTools Inc., USA) with 532 nm excitation, 650 mW laser power at sample, 7 cm 1 spectral resolution, and 4 min 39 s data acquisition for each spectrum.

3. Discussion 3.1. The importance of data pretreatment

Fig. 4. Contour plot of the (a) Raman and (b) ROA autocorrelation as a function of spectral wavenumber and average translating-window pH generated from pretreated pH-dependent spectral intensity variations of poly(L-glutamic acid). The pH range of 4.7–5.4 is displayed only as no cross peaks are observed outside of this range. Adapted from [26].

Fig. 5. Contour plot of the 2DCos Raman autocorrelation as a function of spectral wavenumber and average translating-window temperature generated from pretreated temperature-dependent spectral intensity variations of ribosomal LBD RNA.

Careful data pretreatment, which is specific to the experimental procedures used, is essential for preparing reliable 2DCos Raman and 2DCos ROA correlation maps [25,28– 32] and is particularly important for studies of biological molecules for several reasons. Firstly, as proteins and RNAs are much larger than chemical species their vibrational spectra are consequently far more complex and greater care must be taken in resolving overlapping and relatively weak bands in the 2D contour maps. Secondly, many biological samples contain fluorescent impurities or aggregated particles that can cause large and nonsystematically varying background signals [23]. It is possible, and often advisable, to reduce such background signals experimentally, such as by dilution, addition of an absorbent or by changing laser wavelength, but it is often impossible to completely remove these background signals. Furthermore, it is preferable to study biological molecules solution, or at least under hydrated conditions, and the Raman signal from solvent water can be of significant and varying intensity relative to Raman bands from the biological molecule under investigation [14]. Thirdly, it is not always possible to design a 2D correlation experiment so that precise normalization factors for all spectra are defined. Spectral bands that are invariant with sample conditions, and so are directly proportional to the number of molecules generating them, are known as internal standards and are often used for normalization of spectra before the generation of 2D maps [21,31–34]. However, internal standards are less common for biological molecules than for smaller chemical molecules. Greater care must also be exercised in using external standards for intensity normalization of spectra for biological molecules as the simple chemical systems used, usually salts, can affect the conformational transition being investigated. Finally, the conformational transitions of proteins and RNAs are far more complex than most chemical reactions, and this can

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make the unambiguous interpretation of 2D contour maps more difficult than for studies of chemical reactions. For these reasons, 2D correlation maps generated from raw Raman or ROA spectra of biological molecules are typically of little or no use as the random variations in signal intensity are comparable to, or larger than, the systematically changing bands being studied. These variations lead to distortions in the 2D correlation maps, obscure small but real features and can also generate unreliable results [25]. Even simple data pretreatments can significantly improve the reliability of 2D contour maps and it is recognised that the methods of data pretreatment depend upon the specific spectroscopic techniques and experimental conditions used [28,31,32]. 3.2. Data pretreatment steps We have developed a protocol of pretreatment stages for generating reliable 2D contour maps. The effects of this protocol are shown in Figs. 1 and 2 for Raman and ROA spectra, respectively, collected for the transition of polylysine from a-helical to b-sheet conformations [24]. Firstly, baseline subtraction is performed on each individual spectrum across the full range of data collected, in order to remove the background signals originating from fluorescence, Rayleigh scattering from aggregates, or the spectrometer. Reference spectra can be used for this purpose but accurate mathematical models of fluorescence and aggregation effects are generally not available. We have found that the systematic anchoring of data points within all measured spectra to a user-defined baseline usually works well. Although this is a subjective approach and may introduce errors into measurements of the absolute intensity of bands, we emphasise that the point of using 2D correlation analysis is to identify systematic changes in intensity throughout a series of spectra so that the careful application of user-defined baseline corrections is effective. Spectra are then intensity normalized, which is a common approach employed in 2D correlation analysis to remove distortions not related to the experimental conditions being investigated. Several researchers have suggested that normalization methods should depend on the experimental conditions used, and several methods of normalization have been reported [21,23,28,35]. If possible, the experiment should be designed so that the amount of sample is constant, or at least accurately known for each spectrum. This then allows a simple mathematical normalization to a reference spectral intensity. More typically, an internal standard has to be used. In Raman studies on proteins and polypeptides the methylene deformation mode gives rise to a band at 1445 cm 1 [36,37] which has been reported to be invariant under different sample conditions [36–39]. Unlike Raman band intensities, ROA band intensities are sensitive to conformational mobility [40] and, therefore, experimental conditions. Therefore, there are no reliable internal standards for normalizing ROA

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spectra. However, the complementarity of Raman and ROA is an advantage here as the two sets of spectra are measured simultaneously so the same normalization factors, however determined, can be used for both sets of data. Spectra are also smoothed, typically using a Fast Fourier transform at 2 or 5 FFT points, although other smoothing techniques can be used [41,42]. It is not always possible to simply increase the number of spectra or data collection times, especially for ROA where intensities are typically 3–5 orders of magnitude weaker than for Raman scattering. The level of smoothing obviously depends on the noise level of the data. At this stage significant improvements should be observed in the 2D contour maps but small variations in the signal background may still exist. Therefore, the final stage in data pretreatment is to perform further local baseline subtractions across smaller spectral regions. It is advisable to prepare and examine 2D contour maps at each stage of the data pretreatment process. 3.3. Unevenly spaced data Data that is evenly spaced along the perturbation variable is generally required to perform 2D correlation analysis. However, this is not always possible in studies of conformational transitions of biological molecules, as in the case of pH-induced folding/unfolding studies or timedependent processes such as fibrillogenesis. In such cases where non-evenly spaced experimental data have been acquired two different approaches have been used in the literature for further data pretreatment. The first approach is to convert the unevenly spaced data into evenly spaced data through either interpolation or use of a simple curve fitting procedure, while the second approach is to modify the original computational procedure with a numerical integration method [43]. Use of the interpolation procedure assumes that the dynamic system being studied does not change significantly throughout the ‘missing’ sections of data. Although the interpolation procedure is effective, care must be exercised that the spectra vary in intensity in a consistent manner throughout the regions of missing data. As will be shown below, the complexity of conformational transitions in proteins and RNAs means that this may not be the case if relatively large gaps are left between consecutive spectra. 3.4. Selection of contour levels As proteins and RNAs have many more vibrational modes than the smaller chemical species that have been widely studied using 2D correlation analyses, the 2D correlation maps generated from Raman or ROA spectra of these biological molecules can be very complex and confusing. The number of features in a 2DCos Raman or ROA map can often be far greater than the number of reliable band assignments. Varying the maximum level of contours can greatly simplify interpretation of the contour maps, and establish a level at which weak and unreliable contours

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can be removed. The effect of varying contour levels is shown in Fig. 3, in which increasing the number of contours obviously increases the information content of the 2D map. At fewer contours important information is lost, while at higher contour levels the 2D maps can become overly complex due to baseline variations and noise. This choice of contour levels is especially important when the original spectra display large variations in intensity across the full spectral range, which is often the case for Raman and ROA spectra of proteins. For each 2DCos Raman or 2DCos ROA study the effect of the number of maximum contour levels should be examined in order to determine the optimum. 3.5. Bisignate spectra As all bands in Raman spectra are of the same positive sign, interpretation of the 2DCos Raman synchronous and asynchronous plots to determine sequence order of band changes is, relatively, straightforward following Noda’s rules. Interpretation of these plots is more complicated, however, for bisignate spectra such as those measured in ROA. Using Noda’s rules of interpretation [17], positive synchronous cross peaks indicate that both spectral bands change intensity in the same direction, either both increasing or decreasing, whereas negative cross peaks (which are shaded grey in these figures) indicate that both bands change in opposite direction, one band increasing in intensity and the other decreasing. Relating these contours to their structural origins is a relatively simple process for intensity variations in the positive intensity range of spectra. A band that is increasing in intensity becomes more positive in absolute magnitude while a band that is decreasing in intensity becomes less positive in absolute terms. However, for bands in the negative intensity range of the spectra, a band that is increasing in intensity becomes more negative in absolute magnitude and a band that is decreasing in intensity becomes less negative in absolute magnitude. Consequently, a negative cross peak (shaded) developed between a negative ROA band and a positive ROA band indicates that intensities of both bands, in absolute magnitude, are either increasing or decreasing, while a positive cross peak (unshaded) implies that the magnitude of one band is increasing as the magnitude of the second band is decreasing. It is therefore essential when interpreting 2D correlation contour maps to know whether ROA intensity changes occur in the positive or negative intensity range of the original spectra and this additional information has been indicated by the inclusion of plus or minus signs as superscripts on all ROA bands. As all Raman bands are positive in sign, no superscripts are required for 2DCos Raman plots. This labelling procedure also provides a simple means of discriminating between bands from Raman spectra (no superscript) and those from ROA spectra (with + or superscript). Noda’s rules for interpretation of asynchronous plots remain appropriate irrespective of the signs of the ROA bands.

3.6. Making sense of the complexity of conformational transitions Although Noda’s rules can be used to identify for each spectral band a sequence order of change in intensity relative to other bands, they do not unequivocally relate these band intensity changes to the perturbation. One method that has been suggested as being able to address this problem is called a moving window [43,44]. In this moving window analysis, the full set of spectra is partitioned into smaller subsets, with the size of these subsets depending on the spectral data being examined. For example, the first subset could contain the first three spectra, the second subset would then consist of the second to fourth spectra, with the third subset consisting of the third to fifth spectra, and so on until all spectra are included. In this way, the subsets of spectra form a moving window of data across the full perturbation range examined. An example is shown in Fig. 4, where Raman spectra collected for the unfolding of a-helix in polyglutamic acid have been divided into subsets of three spectra each [26]. A 2D correlation analysis was performed on each subset independently and the peak intensities of the autocorrelations for each were then compared. Comparisons of the largest autocorrelation intensities for these subsets then allowed identification of the different regions of the full perturbation range where dynamic changes occurred. In the contour plot of the moving window autocorrelation spectra shown in Fig. 4, we can see that there are two distinct regions of spectral variations. These two stages of peak intensity changes in the a-helixto-disordered transition in poly(L-glutamic acid), one occurring from pH 4.75–5.05 and the other from pH 5.05–5.35 reflect differences in helical stability between the central region of helix and the relatively destabilized residues at the N- and C-termini [3,26,45]. Fraying of the ends of a-helices is thought to play an important role in helix melting [46] and it is evident that the combination of Raman and ROA spectroscopies with 2D correlation analysis is able to clearly distinguish between these two helical stabilities. Vibrational spectroscopies in combination with 2D correlation analysis can also be used to study RNA folding, or unfolding. There is currently a large gap between the two types of structural characterization methods used in RNA biology. X-ray crystallography and NMR can provide structural details to atomic resolution but are difficult to apply to most RNA samples, whereas UV melting profiles are widely used to identify when a change in RNA structure occurs but are unable to identify which structural motifs or bases are involved. There is great opportunity for 2DCos Raman spectroscopy to fill this gap and provide detailed information on RNA folding or unfolding transitions. As for proteins, analysis of 2DCos Raman synchronous and asynchronous contour maps for RNA melting experiments can lead to confusing or inaccurate results due to the complexity of these transitions. Moving window analyses are able to resolve the different phases of RNA

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melting as shown in Fig. 5, which displays the moving window autocorrelation spectra as a function of the average window temperature for a sample of Mg2+-free LBD RNA. This 58 nucleotide ribosomal RNA fragment binds the C-terminal domain of the L11 ribosomal protein [47] and is a convenient model system to test our 2D correlation methods as it has been characterized using both high resolution, X-ray crystallography [48], and low resolution, UV melting, methods. The Raman spectra were pretreated as already described and intensity normalized to the band at 1100 cm 1 which is related to the number of phosphodiester linkages irrespective of their local environments [49]. From Fig. 5 it appears that there are three distinct phases in the thermal unfolding of the LBD RNA, from 26–40 °C, 40–58 °C, and 58–77 °C. As this RNA sample was Mg2+-free it does not support extended tertiary structure under these conditions and the moving window plot is monitoring the sequential unfolding of secondary structure elements. We are currently in the process of identifying and characterizing these changes in secondary structure using 2 D synchronous and asynchronous maps constructed separately for each of these three distinct phases.

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principles are the same as for other types of 2DCos studies, using other techniques and on structurally simpler chemical systems, even greater care must be exercised in studies on biomolecules. There has been extensive discussion in the literature about the difficulties of interpreting 2D contour maps [30,52–59] which has developed the applicability of these methods to chemical reactions in particular. Although 2D generalized correlation analysis is now being frequently applied to biological reactions and structural changes the level of analysis is usually not detailed and much of the information contained in the rich synchronous and asynchronous contour maps is left unused. Protein folding is a complex cooperative process involving both sequential and coupled changes in secondary structure and tertiary interactions, with these being characterized by changes in both the polypeptide backbone and in side chain interactions [60]. 2DCos Raman and 2DCos ROA spectroscopies can provide uniquely detailed information on protein and RNA (un)folding and through careful application of these techniques we may begin to obtain a clearer understanding of their mechanisms. Acknowledgements

3.7. 2DCos raman/ROA heterocorrelations The complementarity of Raman and ROA spectroscopies for studying conformational transitions in biomolecules has been discussed in the Introduction. Raman and ROA spectra can be measured simultaneously on the same sample, and 2D heterocorrelation of Raman and ROA spectra provides an exciting opportunity for their joint analysis. Heterocorrelation analysis been applied to many different combinations of spectroscopic techniques including 2DCos IR and 2DCos SAXS [18], IR and NIR [50,51], and IR and Raman spectroscopies [23,21] but we have only just begun to conduct 2DCos Raman/ROA studies [24,26]. 2DCos Raman/ROA plots have been used so far to verify the details of 2DCos Raman and 2DCos ROA plots and to confirm Raman/ROA band relationships. Heterocorrelation 2DCos Raman/ROA plots are more complex in appearance than either 2DCos Raman or 2DCos ROA plots, partly because they do not exhibit autopeaks, only cross peaks, and heterocorrelation plots are therefore not symmetric with respect to the diagonal. As further 2DCos Raman/ROA studies of protein conformational changes are performed, none have yet been performed on RNA, our ability to interpret the 2D heterocorrelation contour maps should further improve as they have for 2DCos Raman and 2DCos ROA homocorrelation contour maps. 4. Conclusions In this paper we have presented a series of data pretreatment steps designed to maximize the reliability and information content of 2DCos Raman and 2DCos ROA contour maps from proteins and RNAs. Although the

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