REDCRAFT

REDCRAFT is an open-source software tool for determining a protein's structure using residual dipolar couplings (RDCs). It allows simultaneous determination of a protein's structure and dynamics. Its effectiveness has been demonstrated on both synthetic and experimental data. REDCRAFT contains stages that allow the incorporation of user-specified dihedral angle constraints, such as those produced by TALOS or a restriction to specific regions of Ramachandran space. It is robust with respect to noise and missing data. The program is highly efficient and can produce a structure for an 80-residue protein within two hours.

Initial Publication:
Bryson, Michael, Fang Tian, James H. Prestegard, and Homayoun Valafar. "REDCRAFT: A tool for simultaneous characterization of protein backbone structure and motion from RDC data." Journal of Magnetic Resonance 191.2 (2008): 322-34.

REDCAT

REDCAT is a software package for the analysis of residual dipolar couplings (RDCs) for structure validation and elucidation. In addition to basic utilities to solve for order tensors and back-calculate couplings from a given order tensor and proposed structure, a number of improved algorithms have been incorporated. These include the proper sampling of the Null-space (when the system of linear equations is under-determined), more sophisticated filters for invalid order-tensor identification, error analysis for the identification of the problematic measurements, and simulation of the effects of dynamic averaging processes. REDCAT is a user-friendly program with excellent performance. The modular implementation of REDCAT's algorithms, with separation of the computational engine from the graphical engine, allows for flexible and easy command line interaction.

Initial Publication:
Valafar, Homayoun, and James H. Prestegard. "REDCAT: a residual dipolar coupling analysis tool." Journal of Magnetic Resonance 167.2 (2004): 228-41.

PDPA

Probability Density Profile Analysis (PDPA) is a tool that uses multiple correlated unassigned RDC data sets and an estimation of experimental error to produce a probabilityu density function for the true distribution of RDC's. This is used as a structural fingerprint which is then compared to simulated RDC data freom a library of structures to determine the best match. Applications include novel protein fold target selection for structural genomics initatives, structural homologue detection for structure determintation by threading, and confirmation of computationally modeled protein structures from a small amount of experimental data.

Initial Publication:
Bansal, Sonal, Xijiang Miao, Michael W. Adms, James H. Prestegard, and Homayoun Valafar. "Rapid Classification of protein structure models using unassigned backbone RDCs and probability density profile analysis (PDPA)." Journal of Magnetic Resonance 192.1 (2008): 60-68.

TALI

While many distance metrics, for instance backbone RMSD, exist to quantify the difference between protein structures, none exist that are based on the protein's torsion angles.  TALI defines each protein as a sequence of triples consisting of 2 torsion angles and a residue type.  Drawing on idea behind local and global sequence alignment tools such as the Needleman-Wunsch algorithm, TALI computes a distance metric that is sensitive to similarity in local structure, and is insensitive to small localized changes in structure that can have a huge impact on backbone RMSD.  Applications include domain  detection, structural homology detection, and phylogeny reconstruction. For more information, see Tali: Protein Structure Alignment Using Backbone Torsion Angles, and Tali: local alignment of protein structures using backbone torsion angles.

Initial Publication: Reference Here

Protein Structure Phylogeny from TALI

Protein structure phylogeny...

Initial Publication: Reference Here

2D-RDC

2D-RDC analysis (soon to come)
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 l-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 l-map.

Initial Publication: Reference Here

nD-RDC

Advances in NMR instrumentation and pulse sequence design have resulted in easier acquisition of Residual Dipolar Coupling (RDC) data. However, computational and theoretical analysis of this type of data has continued to challenge the international community of investigators because of their complexity and rich information content. Contemporary use of RDC data has required a-priori assignment, which significantly increases the overall cost of structural analysis. This article introduces a novel algorithm that utilizes unassigned RDC data acquired from multiple alignment media (nD-RDC, n greater-or-equal, slanted 3) for simultaneous extraction of the relative order tensor matrices and reconstruction of the interacting vectors in space.

Estimation of the relative order tensors and reconstruction of the interacting vectors can be invaluable in a number of endeavors. An example application has been presented where the reconstructed vectors have been used to quantify the fitness of a template protein structure to the unknown protein structure. This work has other important direct applications such as verification of the novelty of an unknown protein and validation of the accuracy of an available protein structure model in drug design. More importantly, the presented work has the potential to bridge the gap between experimental and computational methods of structure determination.

Initial Publication:
Xijiang Miao, Rishi Mukhopadhyay, Homayoun Valafar, "Estimation of relative order tensors, and reconstruction of vectors in space using unassigned RDC data and its application." Journal of Magnetic Resonance, 194.2 (2008): 202-211