Rapid classification of protein structure models using unassigned backbone RDCs and probability density profile analysis (PDPA)

Bansal, Sonal; Miao, Xijiang; Adams, Michael W. W.; Prestegard, James H.; Valafar, Homayoun

doi:10.1016/j.jmr.2008.01.014

Cited by 16 publications

(27 citation statements)

References 40 publications

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“…EPAR then computes the average value for the leftmost matrix in Eq. (3) (Bansal et al 2008) for each set of corresponding vectors across the domains. Because the symmetric axis of the dimer must be collinear with one of the 3 axes of PAF, this step is not required for a perfectly symmetric homo-dimer, as RDCs for both subunits are identical.…”

Section: Methodsmentioning

confidence: 99%

Backbone resonance assignment and order tensor estimation using residual dipolar couplings

et al. 2011

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An NMR investigation of proteins with known X-ray structures is of interest in a number of endeavors. Performing these studies through nuclear magnetic resonance (NMR) requires the costly step of resonance assignment. The prevalent assignment strategy does not make use of existing structural information and requires uniform isotope labeling. Here we present a rapid and cost-effective method of assigning NMR data to an existing structure—either an X-ray or computationally modeled structure. The presented method, Exhaustively Permuted Assignment of RDCs (EPAR), utilizes unassigned residual dipolar coupling (RDC) data that can easily be obtained by NMR spectroscopy. The algorithm uses only the backbone N–H RDCs from multiple alignment media along with the amino acid type of the RDCs. It is inspired by previous work from Zweckstetter and provides several extensions. We present results on 13 synthetic and experimental datasets from 8 different structures, including two homodimers. Using just two alignment media, EPAR achieves an average assignment accuracy greater than 80%. With three media, the average accuracy is higher than 94%. The algorithm also outputs a prediction of the assignment accuracy, which has a correlation of 0.77 to the true accuracy. This prediction score can be used to establish the needed confidence in assignment accuracy.

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Section: Methodsmentioning

confidence: 99%

Backbone resonance assignment and order tensor estimation using residual dipolar couplings

et al. 2011

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“…When residual dipolar couplings are collected in multiple alignment media, the RDC data in each medium that corresponds to the same interacting vector can be paired using chemical shift data [18]. As previously observed [14,29,31,38], the information gained by correlating data from two media is much richer than the data that can be gleaned from a separate analysis of each single channel of data.…”

Section: Background and Theorymentioning

confidence: 99%

“…Applications of the RDC data include the study of challenging biomolecules such as membrane proteins [1–4], carbohydrates [5–7], and DNA/RNA [8–11] to name a few. Recent advances include structure determination from multiple alignment media [12–15], reconstruction of the relative order tensors from unassigned RDC data [15,16], and identification of homologous structures [17,18] from unassigned RDC data. These elaborate methods utilize RDC data collected from multiple alignment media, sometimes requiring data from as many as 5 or more alignment media [13,19–21].…”

Section: Introductionmentioning

confidence: 99%

“…These contemporary methods of order tensor estimation require resonance assignment, which is costly in both time and money. Under the significantly cheaper assumption of unassigned resonances and no structure, a number of recent approaches have described various methods of estimating order tensors (or relative order tensors) [13,14,17,18,29,30]. These serve either as the final point of analysis or as an intermediate step toward a final point such as structure determination or study of internal dynamics.…”

Section: Introductionmentioning

confidence: 99%

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Efficient and accurate estimation of relative order tensors from λ-maps

Mukhopadhyay¹,

Miao²,

Shealy³

et al. 2009

Journal of Magnetic Resonance

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The rapid increase in the availability of RDC data from multiple alignment media in recent years has necessitated the development of more sophisticated analyses that extract the RDC data’s full information content. This article presents an analysis of the distribution of RDCs from two media (2D-RDC data), using the information obtained from a λ-map. This article also introduces an efficient algorithm, which leverages these findings to extract the order tensors for each alignment medium using unassigned RDC data in the absence of any structural information. The results of applying this 2D-RDC analysis method to synthetic and experimental data are reported in this article. The relative order tensor estimates obtained from the 2D-RDC analysis are compared to order tensors obtained from the program REDCAT after using assignment and structural information. The final comparisons indicate that the relative order tensors estimated from the unassigned 2D-RDC method very closely match the results from methods that require assignment and structural information. The presented method is successful even in cases with small datasets. The results of analyzing experimental RDC data for the protein 1P7E are presented to demonstrate the potential of the presented work in accurately estimating the principal order parameters from RDC data that incompletely sample the RDC space. In addition to the new algorithm, a discussion of the uniqueness of the solutions is presented; no more than two clusters of distinct solutions have been shown to satisfy each λ-map.

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“…These methods generally utilize a very small portion of RDCs (N-H from one or two alignment media) in order to guide the computational modeling of structures, which have produced very exciting results. A number of other research efforts 10,11,41,48–50 , have demonstrated the possibility of De Novo structure determination based on slightly more experimental data than what is required by hybrid approaches. However these methods employ stochastic search approaches that do not provide any upper bound in computation time or quality of the final structure.…”

Section: Introductionmentioning

confidence: 99%

Dynafold: A Dynamic Programming Approach to Protein Backbone Structure Determination From Minimal Sets of Residual Dipolar Couplings

Mukhopadhyay

Irausquin

Schmidt

et al. 2014

J. Bioinform. Comput. Biol.

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Residual Dipolar Couplings (RDCs) are a source of NMR data that can provide a powerful set of constraints on the orientation of inter-nuclear vectors, and are quickly becoming a larger part of the experimental toolset for molecular biologists. However, few reliable protocols exist for the determination of protein backbone structures from small sets of RDCs. DynaFold is a new dynamic programming algorithm designed specifically for this task, using minimal sets of RDCs collected in multiple alignment media. DynaFold was first tested utilizing synthetic data generated for the N-H, Cα-Hα, and C-N vectors of 1BRF, 1F53, 110M and 3LAY proteins, with up to ±1 Hz error in 3 alignment media, and was able to produce structures with less than 1.9Å of the original structures. DynaFold was then tested using experimental data, obtained from the Biological Magnetic Resonance Bank, for proteins PDBID:1P7E and PDBID:1D3Z using RDC data from two alignment media. This exercise yielded structures within 1.0Å of their respective published structures in segments with high data density, and less than 1.9Å over the entire protein. The same sets of RDC data were also used in comparisons with traditional methods for analysis of RDCs, which failed to match the accuracy of DynaFold's approach to structure determination.

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Rapid classification of protein structure models using unassigned backbone RDCs and probability density profile analysis (PDPA)

Cited by 16 publications

References 40 publications

Backbone resonance assignment and order tensor estimation using residual dipolar couplings

Backbone resonance assignment and order tensor estimation using residual dipolar couplings

Efficient and accurate estimation of relative order tensors from λ-maps

Dynafold: A Dynamic Programming Approach to Protein Backbone Structure Determination From Minimal Sets of Residual Dipolar Couplings

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