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Automated protein resonance assignments of magic angle spinning solid-state NMR spectra of β1 immunoglobulin binding domain of protein G (GB1).

Moseley HN, Sperling LJ, Rienstra CM - J. Biomol. NMR (2010)

Bottom Line: However, few automated analysis tools are currently available for MAS SSNMR.This application to the 56 amino acid GB1 produced an overall 84.1% assignment of the N, CO, CA, and CB resonances with no errors using peak lists from NCACX 3D, CANcoCA 3D, and CANCOCX 4D experiments.This proof of concept demonstrates the tractability of this problem.

View Article: PubMed Central - PubMed

Affiliation: Department of Chemistry, University of Louisville, KY 40292, USA. hunter.moseley@louisville.edu

ABSTRACT
Magic-angle spinning solid-state NMR (MAS SSNMR) represents a fast developing experimental technique with great potential to provide structural and dynamics information for proteins not amenable to other methods. However, few automated analysis tools are currently available for MAS SSNMR. We present a methodology for automating protein resonance assignments of MAS SSNMR spectral data and its application to experimental peak lists of the β1 immunoglobulin binding domain of protein G (GB1) derived from a uniformly ¹³C- and ¹⁵N-labeled sample. This application to the 56 amino acid GB1 produced an overall 84.1% assignment of the N, CO, CA, and CB resonances with no errors using peak lists from NCACX 3D, CANcoCA 3D, and CANCOCX 4D experiments. This proof of concept demonstrates the tractability of this problem.

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Bipartite graph representing the protein resonance assignment problem. Amino acid typing limits the edges present. Red highlights represent spin system linking into a uniquely mapped segment
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Fig2: Bipartite graph representing the protein resonance assignment problem. Amino acid typing limits the edges present. Red highlights represent spin system linking into a uniquely mapped segment

Mentions: Figure 2 shows the protein resonance assignment problem represented as a bipartite graph. This assignment problem is essentially the same for both solution and solid-state NMR (Tycko 1996; Hong 1999) and involves seven basic steps to effectively solve it (Table 1). But one of the critical differences between solution and solid-state NMR is the root resonances used to group peaks into spin systems. These resonances are dictated by the set of NMR experiments (i.e., experimental strategy) used to solve this assignment problem. As shown in Fig. 1, common MAS SSNMR protein resonance assignment strategies use a partial triple resonance spin system root definition (Pauli et al. 2001; Igumenova et al. 2004; Franks et al. 2005; Balayssac et al. 2007; Hong 1999; Sperling et al. 2010), since not all three resonances may be present within each experiment in a given strategy. MAS SSNMR experimental strategies naturally group into three categories of assignment strategies (Table 2). In category I, two sets of experiments containing either Ni-C’i-1 or Ni-Cαi root resonances are combined into complete dipeptide spin systems using the single common amide nitrogen root resonance. In categories IIa and IIb, experiments containing either Ni-C’i-1 or Ni-Cαi root resonances are combined into complete dipeptide spin systems using two common root resonances. In category III, the listed 4D experiments contain all three root resonances, which represent a complete triple resonance spin system root definition. Labs have published assignment results using category I strategies, but only on small proteins (Hong 1999; Pauli et al. 2001; Igumenova et al. 2004; Franks et al. 2005; Balayssac et al. 2007). Labs are starting to use category II strategies for larger proteins (Frericks et al. 2006; Li et al. 2007; Li et al. 2008). It is expected that labs in the future will probably explore category III strategies using newer G-matrix Fourier transformation (GFT) experiments(Szyperski et al. 1993a; Szyperski et al. 1993b; Kim and Szyperski 2003; Kim and Szyperski 2004; Astrof et al. 2001; Luca and Baldus 2002). Moreover, category II and III strategies have strengths that could make them better for automation than even solution NMR strategies. First, the chemical shift dispersion in Euclidean space of Ni-Cαi, and especially C′i−1-Ni-Cαi root resonance tuples is significantly greater than for Ni-Hi root resonance tuples. Said another way, Ni-Cαi pairs of chemical shifts for a folded protein plotted on a 2D graph as small circles with radius representing the uncertainty in their chemical shift values will show less dense clumps (i.e. less overlapping of circles) than Ni-Hi pairs of chemical shifts plotted in a similar way. This helps prevent the non-unique grouping of peaks into spin systems, which severely complicates resonance assignments. Second, category IIa and IIb strategies can be combined into a single strategy represented as a merged double bipartite graph. This representation may lead to the development of superior grouping and linking algorithms.Fig. 2


Automated protein resonance assignments of magic angle spinning solid-state NMR spectra of β1 immunoglobulin binding domain of protein G (GB1).

Moseley HN, Sperling LJ, Rienstra CM - J. Biomol. NMR (2010)

Bipartite graph representing the protein resonance assignment problem. Amino acid typing limits the edges present. Red highlights represent spin system linking into a uniquely mapped segment
© Copyright Policy
Related In: Results  -  Collection

Show All Figures
getmorefigures.php?uid=PMC2962796&req=5

Fig2: Bipartite graph representing the protein resonance assignment problem. Amino acid typing limits the edges present. Red highlights represent spin system linking into a uniquely mapped segment
Mentions: Figure 2 shows the protein resonance assignment problem represented as a bipartite graph. This assignment problem is essentially the same for both solution and solid-state NMR (Tycko 1996; Hong 1999) and involves seven basic steps to effectively solve it (Table 1). But one of the critical differences between solution and solid-state NMR is the root resonances used to group peaks into spin systems. These resonances are dictated by the set of NMR experiments (i.e., experimental strategy) used to solve this assignment problem. As shown in Fig. 1, common MAS SSNMR protein resonance assignment strategies use a partial triple resonance spin system root definition (Pauli et al. 2001; Igumenova et al. 2004; Franks et al. 2005; Balayssac et al. 2007; Hong 1999; Sperling et al. 2010), since not all three resonances may be present within each experiment in a given strategy. MAS SSNMR experimental strategies naturally group into three categories of assignment strategies (Table 2). In category I, two sets of experiments containing either Ni-C’i-1 or Ni-Cαi root resonances are combined into complete dipeptide spin systems using the single common amide nitrogen root resonance. In categories IIa and IIb, experiments containing either Ni-C’i-1 or Ni-Cαi root resonances are combined into complete dipeptide spin systems using two common root resonances. In category III, the listed 4D experiments contain all three root resonances, which represent a complete triple resonance spin system root definition. Labs have published assignment results using category I strategies, but only on small proteins (Hong 1999; Pauli et al. 2001; Igumenova et al. 2004; Franks et al. 2005; Balayssac et al. 2007). Labs are starting to use category II strategies for larger proteins (Frericks et al. 2006; Li et al. 2007; Li et al. 2008). It is expected that labs in the future will probably explore category III strategies using newer G-matrix Fourier transformation (GFT) experiments(Szyperski et al. 1993a; Szyperski et al. 1993b; Kim and Szyperski 2003; Kim and Szyperski 2004; Astrof et al. 2001; Luca and Baldus 2002). Moreover, category II and III strategies have strengths that could make them better for automation than even solution NMR strategies. First, the chemical shift dispersion in Euclidean space of Ni-Cαi, and especially C′i−1-Ni-Cαi root resonance tuples is significantly greater than for Ni-Hi root resonance tuples. Said another way, Ni-Cαi pairs of chemical shifts for a folded protein plotted on a 2D graph as small circles with radius representing the uncertainty in their chemical shift values will show less dense clumps (i.e. less overlapping of circles) than Ni-Hi pairs of chemical shifts plotted in a similar way. This helps prevent the non-unique grouping of peaks into spin systems, which severely complicates resonance assignments. Second, category IIa and IIb strategies can be combined into a single strategy represented as a merged double bipartite graph. This representation may lead to the development of superior grouping and linking algorithms.Fig. 2

Bottom Line: However, few automated analysis tools are currently available for MAS SSNMR.This application to the 56 amino acid GB1 produced an overall 84.1% assignment of the N, CO, CA, and CB resonances with no errors using peak lists from NCACX 3D, CANcoCA 3D, and CANCOCX 4D experiments.This proof of concept demonstrates the tractability of this problem.

View Article: PubMed Central - PubMed

Affiliation: Department of Chemistry, University of Louisville, KY 40292, USA. hunter.moseley@louisville.edu

ABSTRACT
Magic-angle spinning solid-state NMR (MAS SSNMR) represents a fast developing experimental technique with great potential to provide structural and dynamics information for proteins not amenable to other methods. However, few automated analysis tools are currently available for MAS SSNMR. We present a methodology for automating protein resonance assignments of MAS SSNMR spectral data and its application to experimental peak lists of the β1 immunoglobulin binding domain of protein G (GB1) derived from a uniformly ¹³C- and ¹⁵N-labeled sample. This application to the 56 amino acid GB1 produced an overall 84.1% assignment of the N, CO, CA, and CB resonances with no errors using peak lists from NCACX 3D, CANcoCA 3D, and CANCOCX 4D experiments. This proof of concept demonstrates the tractability of this problem.

Show MeSH