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College of Engineering and Computing

Biomedical Engineering

Identification and Refinement of Structures From Unassigned RDC Data using PDPA

Residual Dipolar Couplings (RDCs) have been shown to provide an effective means for structure calculation of proteins even in the more challenging conditions of internal dynamics or proteins that form complexes. Traditional approaches that utilize RDCs require the data to be "assigned" meaning that it needs to be known which particular residue in the protein produces each RDC value. This step is usually extremely time consuming for experimentalists. Probability Density Profile Analysis (PDPA) is a tool that bypasses assignment by using multiple correlated unassigned RDC data sets and an estimation of experimental error to produce a probability density function (PDF) for the true distribution of RDC's. This is used as a structural fingerprint which is then compared to simulated RDC data from a library of structures to determine the best match. Applications include novel protein fold target selection for structural genomics initiatives, structural homologue detection for structure determination by threading, and confirmation of computationally modeled protein structures from a small amount of experimental data. In recent and ongoing work a derivative of PDPA (nD-PDPA) has shown great promise in refinement of protein structures.

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  • A. Fahim, S. Irausquin, H. Valafar. (2016). nD-PDPA: n-Dimensional Probability Density Profile Analysis. In: H. R. Arabnia, Q. N. Tran (Eds.), Emerging Trends in Applications and Infrastructures for Computational Biology, Bioinformatics, and Systems Biology. Morgan Kaufmann, Imprint of Elsevier, Cambridge, MA, pp. 179–194.
  • Fahim, A., Mukhopadhyay, R., Yandle, R., Prestegard, J. H., Valafar, H. (2013). Protein Structure Validation and Identification from Unassigned Residual Dipolar Coupling Data Using 2D-PDPA. Molecules (Basel, Switzerland), 18(9), 10162–88. doi:10.3390/molecules180910162, PMID: 23973992. (MCB-0644195, P20 RR-016461)