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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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Automated resonance assignments of β1 immunoglobulin binding domain of protein G. Resonances derived from intra experiments are indicated in red. Resonances derived from sequential experiments are indicated in blue
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Fig3: Automated resonance assignments of β1 immunoglobulin binding domain of protein G. Resonances derived from intra experiments are indicated in red. Resonances derived from sequential experiments are indicated in blue

Mentions: Currently, our implementation handles only a limited set of experimental peak lists which includes: (i) NCACX 3D (with 35ms DARR mixing) (ii) CANcoCA 3D, and (iii) CANCOCX 4D (Franks et al. 2005; Franks et al. 2007). These peak lists represent a category IIb assignment strategy (Table 2) which uses a Ni-Cαi root to create dipeptide spin systems. The implementation takes these peak lists, aligns them, groups peaks into dipeptide spin systems in a bottom-up strategy, and then types each ladder to probable amino acids using the carbon shift tuples. The implementation then simulates a set of Ni-Hi rooted peak lists for AutoAssign with an artificial HN shift equal to the observed CA shift divided by 6 (HN = CA/6). This creation of artificial HN shifts is necessary because AutoAssign requires Ni-Hi rooted peak lists. We then use AutoAssign to perform the linking and mapping steps. From this, we have an overall 84.1% assignment of the N, CO, CA, and CB resonances with no errors (Fig. 3), as compared to manually determined and verified assignments (BMRB entry 15156). These results demonstrate the feasibility of automating protein resonance assignments of MAS SSNMR spectral data. They are easily reproduced by the software and lack significant human subjectivity in the grouping and typing of spin systems. Also, the input peak lists are not perfect either, representing realistic peak lists that a spectroscopist used for manual assignment. There are only matching peaks to form 52 out of 56 dipeptide spin systems and some CB peaks are simply missing. Since the CANCOCX experiment is a 4D experiment, the resolution of the CA dimension is very low, causing a matching standard deviation of ~0.5 ppm when aligned to the other two peak lists. But our implementation handled the missing information and resolution issues and assigned 43 out of 52 dipeptide spin systems. There are three main reasons for these results: (i) better dispersion with a Ni-Cαi root; (ii) an improved bottom-up grouping algorithm that especially allows CANCOCX peaks to group around a common C’i-1-Ni-Cαi root before grouping with peaks from other peak lists; and (iii) improved amino acid typing algorithms that shrank the average “possible residue type list” to 5.7 residues with 0.9999 confidence (normally ~8 residues with Cα/Cβ typing). We expect even better results once improved linking and mapping algorithms are implemented, allowing the development of software that will improve the quality of analysis over manual assignment alone. This software is available at http://bioinformatics.chem.louisville.edu.


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)

Automated resonance assignments of β1 immunoglobulin binding domain of protein G. Resonances derived from intra experiments are indicated in red. Resonances derived from sequential experiments are indicated in blue
© Copyright Policy
Related In: Results  -  Collection

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

Fig3: Automated resonance assignments of β1 immunoglobulin binding domain of protein G. Resonances derived from intra experiments are indicated in red. Resonances derived from sequential experiments are indicated in blue
Mentions: Currently, our implementation handles only a limited set of experimental peak lists which includes: (i) NCACX 3D (with 35ms DARR mixing) (ii) CANcoCA 3D, and (iii) CANCOCX 4D (Franks et al. 2005; Franks et al. 2007). These peak lists represent a category IIb assignment strategy (Table 2) which uses a Ni-Cαi root to create dipeptide spin systems. The implementation takes these peak lists, aligns them, groups peaks into dipeptide spin systems in a bottom-up strategy, and then types each ladder to probable amino acids using the carbon shift tuples. The implementation then simulates a set of Ni-Hi rooted peak lists for AutoAssign with an artificial HN shift equal to the observed CA shift divided by 6 (HN = CA/6). This creation of artificial HN shifts is necessary because AutoAssign requires Ni-Hi rooted peak lists. We then use AutoAssign to perform the linking and mapping steps. From this, we have an overall 84.1% assignment of the N, CO, CA, and CB resonances with no errors (Fig. 3), as compared to manually determined and verified assignments (BMRB entry 15156). These results demonstrate the feasibility of automating protein resonance assignments of MAS SSNMR spectral data. They are easily reproduced by the software and lack significant human subjectivity in the grouping and typing of spin systems. Also, the input peak lists are not perfect either, representing realistic peak lists that a spectroscopist used for manual assignment. There are only matching peaks to form 52 out of 56 dipeptide spin systems and some CB peaks are simply missing. Since the CANCOCX experiment is a 4D experiment, the resolution of the CA dimension is very low, causing a matching standard deviation of ~0.5 ppm when aligned to the other two peak lists. But our implementation handled the missing information and resolution issues and assigned 43 out of 52 dipeptide spin systems. There are three main reasons for these results: (i) better dispersion with a Ni-Cαi root; (ii) an improved bottom-up grouping algorithm that especially allows CANCOCX peaks to group around a common C’i-1-Ni-Cαi root before grouping with peaks from other peak lists; and (iii) improved amino acid typing algorithms that shrank the average “possible residue type list” to 5.7 residues with 0.9999 confidence (normally ~8 residues with Cα/Cβ typing). We expect even better results once improved linking and mapping algorithms are implemented, allowing the development of software that will improve the quality of analysis over manual assignment alone. This software is available at http://bioinformatics.chem.louisville.edu.

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