Fast evaluation of protein dynamics from deficient ^{15}N relaxation data
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Abstract
Simple and convenient method of protein dynamics evaluation from the insufficient experimental ^{15}N relaxation data is presented basing on the ratios, products, and differences of longitudinal and transverse ^{15}N relaxation rates obtained at a single magnetic field. Firstly, the proposed approach allows evaluating overall tumbling correlation time (nanosecond time scale). Next, local parameters of the model-free approach characterizing local mobility of backbone amide N–H vectors on two different time scales, S^{2} and R_{ ex }, can be elucidated. The generalized order parameter, S^{2}, describes motions on the time scale faster than the overall tumbling correlation time (pico- to nanoseconds), while the chemical exchange term, R_{ ex }, identifies processes slower than the overall tumbling correlation time (micro- to milliseconds). Advantages and disadvantages of different methods of data handling are thoroughly discussed.
Keywords
^{15}N magnetic relaxation Protein dynamics Model-free approach Ratio, product, and difference of relaxation rates Semi-quantitative analysis of ^{15}N relaxation dataIntroduction
Analysis of nuclear magnetic relaxation provides insight into the molecular motions of proteins (Palmer 2004; Mayo and Daragan 2005; Ejchart 2007; Charlier et al. 2016; Lisi and Loria 2016). One of the most successful and widely used approaches to the analysis of relaxation data is the model-free approach, MFA (Lipari and Szabo 1982). Two other model-independent descriptions of the internal molecular motions have been developed independently: the two-step model (Halle and Wennerström 1981) and the slowly relaxing local structure model (Tugarinov et al. 2001). In the frame of the MFA, local mobility is described by two parameters, the generalized order parameter, S^{2}, which corresponds to the spatial freedom of motion, and the internal correlation time, τ_{int}, corresponding to the rate of this motion in the pico- to nanosecond time scale, which is smaller than the single correlation time describing an isotropic overall molecular tumbling, τ_{ R }. The additional term, R_{ ex }, accounts for the conformational exchange contribution to R_{2} resulting from processes in the micro- to millisecond time scale, often referred to as chemical exchange effects (Stone et al. 1992; Korzhnev et al. 2001).
The most frequently measured protein relaxation parameters are the longitudinal, R_{1}, and transverse, R_{2}, relaxation rates of backbone amide ^{15}N nuclei complemented with ^{15}N-{^{1}H} NOEs (Palmer 2004; Reddy and Rainey 2010). Nuclear magnetic relaxation measurements are long lasting, their data reduction is demanding, and interpretation of the results complex (Zhukov and Ejchart 1999; Pawley et al. 2006; Jaremko et al. 2015). Especially NOE measurements are very time consuming owing to their inherently low sensitivity (Fushman 2003) and susceptibility to systematic errors resulting from not fully relaxed spectra and/or saturation transfer due to the exchange with water protons (Ferrage et al. 2008; Gong and Ishima 2007; Grzesiek and Bax 1993; Renner et al. 2002). So it often happens that limited relaxation data (e.g., R_{1} and R_{2} at a single magnetic field), precluding the determination of all MFA parameters, are solely available. Then, commonly used procedure is to examine the ratio between transverse and longitudinal rates, R_{2}/R_{1}, for the estimation of global isotropic correlation time of a protein (Kay et al. 1989). As a prerequisite this procedure requires an efficient rejection from calculation the residues exhibiting skewed R_{2}/R_{1} values owing to intense fast local motions and/or slow conformational exchange. Residues with an NOE (if available) < 0.6 are usually excluded, since in this case the internal motions are likely to skew significantly the R_{2}/R_{1} value. Usually such residues exhibit low experimental R_{2}/R_{1} values. On the other hand, residues undergoing conformational exchange are characterized by high R_{2}/R_{1} values. Routine method of appropriate residue selection can be done by excluding those residues with a R_{2}/R_{1} value outside of ± 1 standard deviation of the mean (Clore et al. 1990).
With the τ_{ R } already estimated, three methods of further analysis of limited relaxation data were independently developed. One of them is based on the observation that one of the MFA parameters, the generalized order parameter, S^{2}, can be extracted with a reasonable accuracy from a linear combination of relaxation rates, 2R_{2} − R_{1} (Habazettl and Wagner 1995). Another method investigates the product of relaxation rates R_{1}R_{2} also giving access to the S^{2} parameter (Kneller et al. 2002). The last method of the S^{2} parameter extraction from R_{1} and R_{2}, measured with the CEST technique to save experimental time and named lean MFA (LMFA), relies on the least squares minimization (Gu et al. 2016). Since all these methods base on the determination of the overall correlation time τ_{ R } from the R_{2}/R_{1} ratio an appropriate data selection seems to be crucial. Therefore, it was proposed to use for this purpose the product of relaxation rates R_{1}R_{2} and claimed that in contrary to the R_{2}/R_{1} ratio, the former allows to distinguish between the effects of motional anisotropy and chemical exchange (Kneller et al. 2002). For convenience, R_{2}/R_{1}, 2R_{2} − R_{1}, and R_{1}R_{2} will be further denoted as Q, D, and P, respectively.
This paper describes a comparative analysis of three mentioned above combinations of relaxation rates, which allows to select the optimal method of the MFA parameter elucidation from deficient relaxation data. The utility of this approach is presented by applying it to the relaxation data of four proteins of different size: immunoglobulin-binding domain of streptococcal protein G, GB1 (Idiyatullin et al. 2003), human ubiquitin (Lee and Wand 1999), human S100A1 calcium binding protein in apo state (Nowakowski et al. 2011), and β-lactamase PSE-4 (Morin and Gagné 2009). First limited use of this approach was applied to the ^{15}N relaxation rates of BacSp222 peptide (Nowakowski et al. 2018).
Method
The conformational exchange contribution to the transverse relaxation rate, R_{ ex }, is proportional to the square of the ^{15}N Larmor frequency, ω_{ N }. This term can be written as \({R_{ex}}=\Phi \omega _{N}^{2}\) (Peng and Wagner 1995). The proportionality factor Φ represents the effectiveness of conformational exchange processes and is independent on magnetic field strength facilitating direct comparison of chemical exchange terms determined at different magnetic field strengths for different proteins.
A joined analysis of Q = R_{2}/R_{1} and D = 2R_{2} − R_{1} or P = R_{1}R_{2} values allows obtaining semi-quantitative insight into the protein dynamics owing to the different relations of these quantities to the MFA parameters and, therefore, untangling these parameters from experimental data. The Q parameter is quasi-insensitive to both local MFA parameters, S^{2} and τ_{int}, in a reasonably broad range of their values (Fig. 1A, B). Therefore, it is well suited for the evaluation of the overall tumbling correlation times of proteins comprising residues with diverse local mobility. On the other hand, the P values are quasi-insensitive to τ_{int} but decrease considerably with the increased amplitude of local motions, as manifested at smaller values of the S^{2} order parameter (Fig. 1B). The D parameter is even less sensitive to τ_{int} changes than Q and P, but it displays a modest sensitivity to S^{2} changes. All three quantities, Q, D, and P, are sensitive to the chemical exchange term and increase with the R_{ ex } enlargement (Fig. 1C). Simultaneous effect of S^{2} and τ_{int}, changes on Q, P and D is shown in Figs. S1–S3 (Supporting Information). One has to be aware of the opposite effects of fast (ps–ns) and slow (µs–ms) motions on the P values. Both these effects can compensate one another leaving the P value unchanged and, thus, hiding chemical exchange effect. The D values are also sensitive to such compensation. They are, however, less sensitive to fast motions and more sensitive to slow ones than P values and, therefore, should retain at least partially the ability of detection of R_{ ex } terms.
Use of the Q values in the evaluation of an overall correlation time results in the τ_{R} underestimation provided the overall tumbling is isotropic (Korzhnev et al. 1997). Influence of the input τ_{R} and magnetic field strength values on the value of the apparent τ_{R} is demonstrated in Fig. 2. In the utmost situations (parts of plots below the dashed line in the Fig. 2 corresponding to intense internal motion: S^{2} = 0.7, τ_{int} = 100 ps, slow overall tumbling: τ_{R} = 32 ns and very high magnetic field: 23.5 T) the τ_{R} evaluation derived from the Q values breaks down; relative errors exceed 25%. Use of Q values retains the sense only if correlation time of internal motion, τ_{int}, is short and its amplitude small (Fig. S4).
In the case of anisotropic tumbling, the determined value of the orientation averaged overall correlation time τ_{R} = 0.5/(D_{1} + D_{2} + D_{3}), can be either larger or smaller than the τ_{R} value estimated from Q values, depending on the orientation of the N–H vector. Appropriate comparison is presented in Table 1 and Fig. 3.
Anisotropic tumbling visibly influences on the Q and D values, while its effect is strongly attenuated regarding P values, with variability ranges 25, 19, and 3%, respectively
α [deg] | R_{1} [s^{−1}] | R_{2} [s^{−1}] | Q | τ_{R}(Q) [ns] | D [s^{−1}] | P [s^{−2}] |
---|---|---|---|---|---|---|
0 | 1.983 | 9.200 | 4.639 | 9.098 | 19.901 | 26.814 |
30 | 2.060 | 8.791 | 4.267 | 8.601 | 18.819 | 26.623 |
60 | 2.200 | 8.100 | 3.682 | 7.754 | 16.977 | 26.203 |
90 | 2.263 | 7.818 | 3.456 | 7.401 | 16.208 | 25.980 |
One has to be aware of possible systematic deviations of so estimated \(S_{i}^{2}\) values. As it was shown earlier, the Q-derived τ_{R} values are underestimated in isotropically tumbling molecules. Accordingly, the P_{max} is underestimated as well, resulting in the overestimation of \(S_{i}^{2}\) values (Fig. 4). This effect is especially pronounced for slower internal motions (long τ_{int}) with large amplitudes (small S^{2}) at high magnetic fields. A similar effect was reported for the relaxation data in ATPase α-domain (Gu et al. 2016).
It is stated that the Q values do not distinguish between the effects of motional anisotropy and chemical exchange (Kneller et al. 2002), while the analysis of P data significantly attenuates the effects of motional anisotropy (c.f. Table 1) permitting rapid identification of residues undergoing chemical exchange, R_{ ex }. It will be shown in the next section, however, that the attenuation of motional anisotropy is not sufficient to identify unequivocally the R_{ ex } influenced residues. The elevated P values not always allow identifying residues affected by chemical exchange. In fact, only simultaneous outlying Q, D, and P values point out unequivocally to the chemical exchange.
Results and discussion
Combined analysis of Q, D, and P values is applied to four proteins for which the large relaxation data sets are available in the literature. Essential information on these proteins is collected in the Table 2.
Basic data concerning GB1, ubiquitin, S100A1, and PSE4 proteins
Protein | GB1 | Ubiquitin | S100A1 | PSE4 |
---|---|---|---|---|
Residues | 56 | 76 | 2 × 93 | 271 |
MW [kDa] | 6.3 | 8.7 | 21.0 | 29.3 |
τ_{R} (dτ_{R}) [ns] | 2.05 (0.02) | 4.36 (0.03) | 8.35 (0.04) | 12.30 (0.08) |
T [K] | 307.0 | 298.1 | 310.1 | 304.6 |
BMRB code | 5569 | 4245 | 16360 | 6838 |
PDB code | 1GB1 | 1D3Z | 2L0P | 1G68 |
Complete MFA results | Table S3 | Table S4 | Table S5 | Table S6 |
References | Idiyatullin et al. (2003) | Lee and Wand (1999) | Nowakowski et al. (2011) | Morin and Gagné (2009) |
Overall correlation time
The Q, D, and P site specific values for the analyzed proteins at all available magnetic fields are shown in Figs. 5 and S5–S14.The medians of experimentally observed Q = R_{1}/R_{2} values were used for evaluating overall correlation times. Vizualization of this procedure is shown in Figs. S15–S18. Some details are explained in the captions to these figures.
For the proteins analyzed here, the determined τ_{R}(Q) values are larger than the corresponding values obtained from the MFA analysis as shown in the Fig. 6. Corresponding numerical values are reported in Table S2. The averaged deviations are equal to 18, 5, 2, and 5% for GB1, ubiquitin, S100A1, and PSE4, respectively. The opposite results would be expected from the simulation shown in Fig. 2. However, it is demonstrated in the “Method” section that the anisotropic tumbling can result in the overestimation of τ_{R} values and one should note that all four proteins reorient anisotropically (Table 3). It is worth noting that the largest discrepancy between Q-based and MFA derived τ_{R} values appears for GB1 characterized by the strongest tumbling anisotropy. Anisotropic tumbling and, therefore, the possible overestimation of τ_{R} values can be anticipated from the ratios of principal values of inertia tensor provided the protein structure is available (Mandel et al. 1995).
Anisotropic tumbling of the analyzed proteins
Protein | GB1 | Ubq | S100A1 | PSE4 |
---|---|---|---|---|
τ_{R} [ns]^{a} | 2.05 | 4.36 | 8.35 | 12.30 |
D_{1} [10^{7} s^{−1}] | 7.29 | 3.23 | 2.12 | 1.31 |
D_{2} [10^{7} s^{−1}] | 7.35 | 3.76 | 2.10 | 1.17 |
D_{3} [10^{7} s^{−1}] | 9.87 | 4.47 | 1.77 | 1.59 |
ΔD^{b} | 1.35 (0.02) | 1.28 (0.02) | 0.84 (0.01) | 1.28 (0.01) |
η | 0.006 | 0.119 | 0.035 | 0.084 |
I_{1}:I_{2}:I_{3}^{c} | 0.91:1.00:0.52^{d} | 0.90:1.00:0.62^{d} | 0.65:0.74:1.00^{e} | 1.00:0.89:0.58^{d} |
ΔD_{pred}^{f} | 1.36 | 1.24 | 0.83 | 1.28 |
Anisotropic tumbling as a reason of the divergence between τ_{R} values obtained from the MFA analysis and Q-based method can be demonstrated analyzing two residues of GB1, Thr17 and Asp40. Their N–H vectors are nearly perpendicular one another. The Q values have been obtained from the R_{1} and R_{2} relaxation rates and next used in the τ_{ R }(Q) determination. Results are presented in Table 4. The orientation of N–H vectors strongly influences the value of pseudo-isotropic overall correlation time determined using Q value. It has to be pointed out that the median, \(\tilde {Q}\), can be skewed due to the non uniform distribution of N–H vector orientations. Regardless of anisotropic tumbling the presented examples of τ_{R} estimation by means of the Q-based method give suitable results with the average deviation 7% and the largest one 21% for GB1 at 11.7 T as compared with the MFA results.
Overall correlation times τ_{R} determined for GB1 protein from the experimentally derived Q values
B_{0} [T] | \(\tilde {Q}\) | τ_{ R }(\(\tilde {Q}\)) | Q Thr17 | τ_{ R }(Q) Thr17 | Q Asp40 | τ_{ R }(Q) Asp40 |
---|---|---|---|---|---|---|
11.7 | 1.54 (0.06) | 2.48 (0.17) | 1.41 (0.11) | 2.04 (0.41) | 1.80 (0.14) | 3.16 (0.32) |
14.1 | 1.72 (0.05) | 2.47 (0.11) | 1.70 (0.11) | 2.42 (0.24) | 1.85 (0.09) | 2.72 (0.18) |
18.8 | 2.01 (0.08) | 2.26 (0.11) | 2.01 (0.07) | 2.12 (0.10) | 2.10 (0.08) | 2.36 (0.10) |
Chemical exchange
Residues exhibiting slow conformational mobility in the micro- to millisecond time scale have to be selected and next skipped in the further analysis of fast motions. Elevated Q, P, or D values owing to the chemical exchange mechanism result in non physical values of the generalized order parameter, S^{2} > 1.
None of GB1 residues shows outlying Q, P, or D values being a hallmark of the chemical exchange (c.f. Figs. S5–S7). This observation is consistent with the MFA analysis performed for R_{1}, R_{2}, and NOE data at three magnetic fields (Table S3). Both, standard deviations of Φ factors multiplied by an appropriate Student’s t-value and F-test applied to the partial target functions characterizing the fit quality for a given residue with and without assumption of chemical exchange (three or two local parameters) were applied to verify the significance of the MFA-derived Φ factors.
Among the Q, P, and D values calculated for ubiquitin one residue, Asn25, displays the largest values pointing out to the effective chemical exchange mechanism in the relaxation of this residue (c.f. Figs. S8 and S9). This result is consistent with the results of the MFA analysis. Asn25 is the unique residue exhibiting meaningful Φ value (Table S4). Three other residues show large but erratic Q, P, D values. None of them, however, possesses a meaningful Φ value in the MFA analysis.
Glu22 is the unique residue in the S100A1 protein displaying simultaneous large Q, P, and D values, thus clearly exhibiting the chemical exchange mechanism (Figs. 5, S10, and S11). This fact is also consistent with the MFA analysis (Table S5). However, three other residues, Glu3, Gly23 (only at 16.4 T), and Lys25, similarly as Glu22, display distinct P values. So, they can be suspected to undergo a chemical exchange. On the other hand, their Q values and majority of D values are very close to the corresponding medians. Φ factors of Gly23 and Lys25 residues obtained in the MFA analysis reveal the chemical exchange mechanism for these residues but definitely exclude it for Glu3 residue. Summing up, Q values fail to recognize chemical exchange for Gly23 and Lys25 but P values can lead to false recognition of non existing slow motions. D values perform the best at 16.4 T but fail at 9.4 T. None of Q, P, and D parameters is fully immune to the erroneous identification of chemical exchange.
In PSE4 protein (Figs. S12–S14) Q, P, D values for two residues, Thr128 and Ser235, at 11.7 T exceed corresponding outliers’ limits indicating chemical exchange. They also display the largest Φ values in the MFA analysis (Table S6). Nevertheless, not all of Q, P, D values at two higher magnetic fields confirm the efficient chemical exchange. Several other residues show non systematic, outlying Q, P, D values. Three of them, Thr57, Leu221, and Gly236, exhibit meaningful Φ values indicating chemical exchange while for the remaining residues, protruding Q, P, or D values seem to be misleading.
All the above discussed results point out to the possibility of ambiguous or false recognition of chemical exchange involved residues. None of Q, P, and D parameters is fully immune to the erroneous identification of chemical exchange but their values inspected simultaneously usually identify residues influenced by chemical exchange with a high probability.
Generalized order parameters
There are three methods allowing to estimate site specific generalized order parameters S^{2} which describe local fast mobility of a protein backbone. Since such information is important in study of protein function and interactions with other molecules, it is necessary to compare results of these methods. Determination of the isotropic, overall tumbling correlation time, τ_{ R }, from the properly selected Q value makes a starting point of all these methods. Two methods utilizing Eqs. (8a) or (8b) are based on D or P values, respectively. The third method, LMFA, requires fit of back calculated relaxation rates R_{1} and R_{2} with fixed τ_{ R }(Q) and adjustable S^{2} parameters to the experimental data.
Comparison of the results of these methods with those obtained for the MFA analysis of all available relaxation data as a reference, was performed. Similar conclusions can be reached from the results obtained for each of four investigated proteins. General tendencies can be discussed and presented on the examples of several selected GB1 residues. They are shown in Fig. 7.
An important conclusion drawn from data shown in Fig. 7 is that confidence ranges of S^{2}(D) are roughly twice larger than corresponding ranges for S^{2}(P) and S^{2}(LMFA). This is a general feature obtained for all residues in all analyzed proteins (total 399 residues). Confidence ranges for S^{2}(P) and S^{2}(LMFA) are comparable, but always larger than those characterizing MFA results for obvious reason of a larger number of data and parameters used in the latter method. Another important factor concerns dispersion of S^{2} values resulting from different methods for the same residue. In the majority of cases S^{2}(D), S^{2}(P), and S^{2}(LMFA) are close each other at a given magnetic field but differ from the S^{2}(MFA) and among the data determined at different magnetic fields (Fig. 7, Thr11 and Glu19). In a number of cases all the data are close each other (Fig. 7, Ala23). Such situation takes place when all individual Q values are close to the corresponding medians \(\tilde {Q}\). In the opposite case, when individual Q values differ markedly from medians, all S^{2} values are broadly dispersed (Fig. 7, Asp40).
Whole sets of S^{2} values estimated as the sums of squared differences between S^{2}(MFA) and S^{2}(X) (X = D, P, LMFA) allow to estimate accuracy of each method. Once more P-based and LMFA approaches are comparable and slightly more accurate than the D-based one.
Concluding, P-based and LMFA methods of S^{2} determination perform similarly but the former one is less demanding from the computational standpoint.
Conclusions
The R_{1} and R_{2} relaxation rates of ^{15}N nuclei measured at a single magnetic field strength in proteins are not sufficient to perform a formal MFA analysis, but can be utilized for the semi-quantitative evaluation of the overall tumbling correlation time from the median of the set of transverse to longitudinal relaxation rates Q = R_{2}/R_{1}. Generalized order parameters S^{2} characterizing amplitude of internal local motions, faster than the overall tumbling, can be site selectively evaluated using either linear combination D = 2R_{2} − R_{1} or product P = R_{2}·R_{1} or LMFA method. Efficiency of these methods was carefully compared and the P and LMFA methods are comparably accurate while the former is less demanding from the computational standpoint. As the final result one obtains estimation of the overall correlation time, τ_{R}, and parameters characterizing internal motions, S^{2}, on the time scales faster than τ_{R}. Additionally, residues undergoing conformational motions in the micro- to millisecond time scale can be selected basing on the Q, D, and P outliers.
Notes
Acknowledgements
The research by LJ and MJ reported in this publication was supported by funding from King Abdullah University of Science and Technology (KAUST).
Supplementary material
References
- Abragam A (1989) Principles of nuclear magnetism. Clarendon Press, OxfordGoogle Scholar
- Baber JL, Szabo A, Tjandra N (2001) Analysis of slow interdomain motion of macromolecules using NMR relaxation data. J Am Chem Soc 123:3953–3959CrossRefGoogle Scholar
- Charlier C, Cousin SF, Ferrage F (2016) Protein dynamics from nuclear magnetic relaxation. Chem Soc Rev 45:2410 – 2422CrossRefGoogle Scholar
- Clore GM, Driscoll PC, Wingfield PT, Gronenborn AM. (1990) Analysis of the backbone dynamics of interleukin-16 using two-dimensional inverse detected heteronuclear ^{15}N-^{1}H NMR spectroscopy. Biochemistry 29:7387–7401CrossRefGoogle Scholar
- Ejchart A (2007) Insight into protein dynamics from nuclear magnetic relaxation studies. Polimery 52:745–751Google Scholar
- Ferrage F, Piserchio A, Cowburn D, Ghose R (2008) On the measurement of ^{15}N–{^{1}H} nuclear Overhauser effects. J Magn Reson 192:302 – 313ADSCrossRefGoogle Scholar
- Fushman D (2003) In BioNMR in drug research. In: Zerbe O (ed) Methods and principles in medicinal chemistry, vol 16. Wiley, Weinheim, pp 283–308Google Scholar
- Gong Q, Ishima R (2007) ^{15}N–{^{1}H} NOE experiment at high magnetic field strengths. J Biomol NMR 37:147–157CrossRefGoogle Scholar
- Grzesiek S, Bax A (1993) The importance of not saturating H_{2}O in protein NMR. Application to sensitivity enhancement and NOE measurements. J Am Chem Soc 115:12593 – 12594CrossRefGoogle Scholar
- Gu Y, Hansen AL, Peng Y, Brüschweiler R (2016) Rapid determination of fast protein dynamics from NMR chemical exchange saturation transfer data. Angew Chem Int Ed 55:3117–3119CrossRefGoogle Scholar
- Habazettl J, Wagner G (1995) A simplified method for analyzing nitrogen-15 nuclear magnetic resonance data of proteins. J Magn Reson B 109:100–104CrossRefGoogle Scholar
- Halle B, Wennerström H (1981) Interpretation of magnetic-resonance data from water nuclei in heterogeneous systems. J Chem Phys 75:1928–1943ADSCrossRefGoogle Scholar
- Idiyatullin D, Nesmelova I, Daragan VA, Mayo KH (2003) Heat capacities and a snapshot of the energy landscape in protein GB1 from the pre-denaturation temperature dependence of backbone NH nanosecond fluctuations. J Mol Biol 325:149–162CrossRefGoogle Scholar
- Jaremko M, Jaremko L, Nowakowski M, Wojciechowski M, Szczepanowski RH, Panecka R, Zhukov I, Bochtler M, Ejchart A (2014) NMR structural studies of the first catalytic half-domain of ubiquitin activating enzyme. J Struct Biol 185:69–78CrossRefGoogle Scholar
- Jaremko L, Jaremko M, Nowakowski M, Ejchart A (2015) The quest for simplicity: remarks on the free-approach models. J Phys Chem B 119:11978 – 11987CrossRefGoogle Scholar
- Kay LE, Torchia DA, Bax A (1989) Backbone dynamics of proteins as studied by 15N inverse detected heteronuclear NMR spectroscopy: application to staphylococcal nuclease. Biochemistry 28:8972–8979CrossRefGoogle Scholar
- Kneller MK, Lu M, Bracken C (2002) An effective method for the discrimination of motional anisotropy and chemical exchange. J Am Chem Soc 124:1852–1853CrossRefGoogle Scholar
- Korzhnev DM, Orekhov VY, Arseniev AS (1997) Model-free approach beyond the borders of its applicability. J Magn Reson 127:184–191ADSCrossRefGoogle Scholar
- Korzhnev DM, Billeter M, Arseniev AS, Orekhov VY (2001) NMR studies of Brownian tumbling and internal motions in proteins. Prog Nucl Magn Reson Spectrosc 38:197–266CrossRefGoogle Scholar
- Lee AL, Wand AJ (1999) Assessing potential bias in the determination of rotational correlation times of proteins by NMR relaxation. J Biomol NMR 13:101–112CrossRefGoogle Scholar
- Lipari G, Szabo A (1982) Model-free approach to the interpretation of nuclear magnetic resonance relaxation in macromolecules. 1. Theory and range of validity. J Am Chem Soc 104:4546–4559CrossRefGoogle Scholar
- Lisi GP, Loria JP (2016) Using NMR spectroscopy to elucidate the role of molecular motions in enzyme function. Prog NMR Spectrosc 92–93:1–17CrossRefGoogle Scholar
- Mandel AM, Akke M, Palmer AG III (1995) Backbone dynamics of Escherichia coli ribonuclease HI: correlation with structure and function in an active enzyme. J Mol Biol 246:144–163CrossRefGoogle Scholar
- Maronna RA, Martin RD, Yohai VJ (2006) Robust statistics: theory and methods. Wiley, HobokenCrossRefzbMATHGoogle Scholar
- Mayo KH, Daragan VA (2005) Protein dynamics NMR relaxation. World Scientific Publishing, LondonGoogle Scholar
- Nowakowski M, Jaremko Ł, Jaremko M, Zhukov I, Belczyk A, Bierzyński A, Ejchart A (2011) Solution NMR structure and dynamics of human apo-S100A1 protein. J Struct Biol 174:391–399CrossRefGoogle Scholar
- Nowakowski M, Jaremko Ł, Władyka B, Dubin G, Ejchart A, Mak P (2018) Spatial attributes of the four-helix bundle group of bacteriocins—the high-resolution structure of BacSp222 in solution. Int J Biol Macromol 107:2715–2724CrossRefGoogle Scholar
- Ottiger M, Bax A (1998) Determination of relative NHN, N-C′, Cα-C′, and Cα-Hα effective bond lengths in a protein by NMR in a dilute liquid crystalline phase. J Am Chem Soc 120:12334–12341CrossRefGoogle Scholar
- Palmer AG III (2004) NMR characterization of the dynamics of biomolecules. Chem Rev 104:3623–3640CrossRefGoogle Scholar
- Pawley NH, Clark MD. Michalczyk R (2006) Rectifying system-specific errors in NMR relaxation measurements. J Magn Reson 178:77–87ADSCrossRefGoogle Scholar
- Peng JW, Wagner G (1992) Mapping of the spectral densities of N-H bond motions in Eglin c using heteronuclear relaxation experiments. Biochemistry 31:8571–8583CrossRefGoogle Scholar
- Peng JW, Wagner G (1995) Frequency spectrum of NH bonds in eglin c from spectral density mapping at multiple fields.Biochemistry 34:16733–16752CrossRefGoogle Scholar
- Reddy T, Rainey JK (2010) Interpretation of biomolecular NMR spin relaxation parameters. Biochem Cell Biol 88:131–142CrossRefGoogle Scholar
- Renner C, Schleicher M, Moroder L, Holak TA (2002) Practical aspects of the 2D ^{15}N-{^{1}H}-NOE experiment. J Biomol NMR 23:23–33CrossRefGoogle Scholar
- Stone MJ, Fairbrother WJ, Palmer AG III, Reizer J, Saier MH Jr, Wright PE (1992) Backbone dynamics of the Bacillus subtilis glucose permease IIA domain determined from ^{15}N NMR relaxation measurements. Biochemistry 31:4394–4406CrossRefGoogle Scholar
- Tjandra N, Feller SE, Pastor RW, Bax A (1995) Rotational diffusion anisotropy of human ubiquitin from ^{15}N NMR relaxation. J Am Chem Soc 117:12562–12566CrossRefGoogle Scholar
- Tugarinov V, Liang Z, Shapiro YE, Freed JH, Meirovitch E (2001) A structural mode-coupling approach to ^{15}N NMR relaxation in proteins. J Am Chem Soc 123:3055–3063CrossRefGoogle Scholar
- Woessner DE (1962) Nuclear spin relaxation in ellipsoids undergoing rotational Brownian motion. J Chem Phys 37:647–654ADSCrossRefGoogle Scholar
- Yao L, Grishaev A, Cornilescu G, Bax A (2010) Site-specific backbone amide ^{15}N chemical shift anisotropy tensors in a small protein from liquid crystal and cross-correlated relaxation measurements. J Am Chem Soc 132:4295 – 4309CrossRefGoogle Scholar
- Zhukov I, Ejchart A (1999) Factors improving the accuracy of determination of ^{15}N relaxation parameters in proteins. Acta Biochim Pol 46:665–671Google Scholar
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