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Optimization of 1H decoupling eliminates sideband artifacts in 3D TROSY-based triple resonance experiments.

ABSTRACT: TROSY-based triple resonance experiments are essential for protein backbone assignment of large biomolecular systems by solution NMR spectroscopy. In a survey of the current Bruker pulse sequence library for TROSY-based experiments we found that several sequences were plagued by artifacts that affect spectral quality and hamper data analysis. Specifically, these experiments produce sidebands in the 13C(t 1) dimension with inverted phase corresponding to 1HN resonance frequencies, with approximately 5% intensity of the parent 13C crosspeaks. These artifacts originate from the modulation of the 1HN frequency onto the resonance frequency of 13C? and/or 13C? and are due to 180° pulses imperfections used for 1H decoupling during the 13C(t 1) evolution period. These sidebands can become severe for CAi, CAi-1 and/or CBi, CBi-1 correlation experiments such as TROSY-HNCACB. Here, we implement three alternative decoupling strategies that suppress these artifacts and, depending on the scheme employed, boost the sensitivity up to 14% on Bruker spectrometers. A class of comparable Agilent/Varian pulse sequences that use WALTZ16 1H decoupling can also be improved by this method resulting in up to 60-80% increase in sensitivity.

SUBMITTER: Xia Y

PROVIDER: S-EPMC5693675 | biostudies-other | 2017 Sep

REPOSITORIES: biostudies-other

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Optimization of 1H decoupling eliminates sideband artifacts in 3D TROSY-based triple resonance experiments.

Xia Youlin Y Rossi Paolo P Tonelli Marco M Huang Chengdong C Kalodimos Charalampos G CG Veglia Gianluigi G

Journal of biomolecular NMR 20170908 1

TROSY-based triple resonance experiments are essential for protein backbone assignment of large biomolecular systems by solution NMR spectroscopy. In a survey of the current Bruker pulse sequence library for TROSY-based experiments we found that several sequences were plagued by artifacts that affect spectral quality and hamper data analysis. Specifically, these experiments produce sidebands in the 13C(t 1) dimension with inverted phase corresponding to 1HN< ...[more]

PMID: 28887770

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Project description:With computational biology striving to provide more accurate theoretical accounts of biological systems, use of increasingly complex computational models seems inevitable. However, this trend engenders a challenge of optimal experimental design: due to the flexibility of complex models, it is difficult to intuitively design experiments that will efficiently expose differences between candidate models or allow accurate estimation of their parameters. This challenge is well exemplified in associative learning research. Associative learning theory has a rich tradition of computational modeling, resulting in a growing space of increasingly complex models, which in turn renders manual design of informative experiments difficult. Here we propose a novel method for computational optimization of associative learning experiments. We first formalize associative learning experiments using a low number of tunable design variables, to make optimization tractable. Next, we combine simulation-based Bayesian experimental design with Bayesian optimization to arrive at a flexible method of tuning design variables. Finally, we validate the proposed method through extensive simulations covering both the objectives of accurate parameter estimation and model selection. The validation results show that computationally optimized experimental designs have the potential to substantially improve upon manual designs drawn from the literature, even when prior information guiding the optimization is scarce. Computational optimization of experiments may help address recent concerns over reproducibility by increasing the expected utility of studies, and it may even incentivize practices such as study pre-registration, since optimization requires a pre-specified analysis plan. Moreover, design optimization has the potential not only to improve basic research in domains such as associative learning, but also to play an important role in translational research. For example, design of behavioral and physiological diagnostic tests in the nascent field of computational psychiatry could benefit from an optimization-based approach, similar to the one presented here.

Dataset Information

Optimization of 1H decoupling eliminates sideband artifacts in 3D TROSY-based triple resonance experiments.

Publications

Optimization of <sup>1</sup>H decoupling eliminates sideband artifacts in 3D TROSY-based triple resonance experiments.

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