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RDC derived protein backbone resonance assignment using fragment assembly

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Abstract

Experimental residual dipolar couplings (RDCs) in combination with structural models have the potential for accelerating the protein backbone resonance assignment process because RDCs can be measured accurately and interpreted quantitatively. However, this application has been limited due to the need for very high-resolution structural templates. Here, we introduce a new approach to resonance assignment based on optimal agreement between the experimental and calculated RDCs from a structural template that contains all assignable residues. To overcome the inherent computational complexity of such a global search, we have adopted an efficient two-stage search algorithm and included connectivity data from conventional assignment experiments. In the first stage, a list of strings of resonances (CA-links) is generated via exhaustive searches for short segments of sequentially connected residues in a protein (local templates), and then ranked by the agreement of the experimental 13Cα chemical shifts and 15N-1H RDCs to the predicted values for each local template. In the second stage, the top CA-links for different local templates in stage I are combinatorially connected to produce CA-links for all assignable residues. The resulting CA-links are ranked for resonance assignment according to their measured RDCs and predicted values from a tertiary structure. Since the final RDC ranking of CA-links includes all assignable residues and the assignment is derived from a “global minimum”, our approach is far less reliant on the quality of experimental data and structural templates. The present approach is validated with the assignments of several proteins, including a 42 kDa maltose binding protein (MBP) using RDCs and structural templates of varying quality. Since backbone resonance assignment is an essential first step for most of biomolecular NMR applications and is often a bottleneck for large systems, we expect that this new approach will improve the efficiency of the assignment process for small and medium size proteins and will extend the size limits assignable by current methods for proteins with structural models.

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Acknowledgments

We are grateful for financial supports from the National Institutes of Health (5R01GM081793-03), the American Diabetes Association (7-07-RA-34) and the Penn State University College of Medicine. We would like to thank Dr. James H. Prestegard at the University of Georgia, Dr. Homay Valafar at the University of South Carolina and Dr. Maria C. Bewley at Penn State University for helpful discussions. The work was performed on a 600 MHz NMR spectrometer at the NMR Core Facility at the Penn State College of Medicine, which was purchased with funds from NIH 1S10RRO21172 and the Tobacco Settlement Funds awarded by the Pennsylvania Department of Health. Several figures were prepared with the program VMD. VMD was developed with NIH support by the Theoretical and Computational Biophysics group at the Beckman Institute, University of Illinois at Urbana-Champaign.

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  1. Department of Biochemistry and Molecular Biology, College of Medicine, Pennsylvania State University, Hershey, PA, 17033, USA

    Xingsheng Wang, Brian Tash, John M. Flanagan & Fang Tian

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  1. Xingsheng Wang
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Correspondence to Fang Tian.

Electronic supplementary material

Detailed sample preparation and plots of order matrix analysis of the input data for the bovine crystallin and PDZ3 structures by REDCAT. We are integrating the assignment scripts into a single program, and the program will be available upon request from the authors.

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Wang, X., Tash, B., Flanagan, J.M. et al. RDC derived protein backbone resonance assignment using fragment assembly. J Biomol NMR 49, 85–98 (2011). https://doi.org/10.1007/s10858-010-9467-z

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