  Despite the recent advances in various Structural Genomics Projects, a large
gap remains between the number of sequenced and structurally characterized
proteins. Some reasons for this discrepancy include technical difficulties,
labor, and the cost related to determining a structure by experimental methods
such as NMR spectroscopy. Several computational methods have been developed to
expand the applicability of NMR spectroscopy by addressing temporal and
economical problems more efficiently. While these methods demonstrate
successful outcomes to solve more challenging and structurally novel proteins,
the cost has not been reduced significantly. Probability Density Profile
Analysis (PDPA) has been previously introduced by our lab to directly address
the economics of structure determination of routine proteins and the
identification of novel structures from a minimal set of unassigned NMR data.
2D-PDPA (in which 2D denotes incorporation of data from two alignment media)
has been successful in identifying the structural homolog of an unknown protein
within a library of ~1000 decoy structures. In order to further expand the
selectivity and sensitivity of PDPA, the incorporation of additional data was
necessary. However, the expansion of the original PDPA approach was limited by
its computational requirements where the inclusion of additional data would
render it computationally intractable. Here we present the most recent
developments of PDPA method (nD-PDPA: n Dimensional Probability Density Profile
Analysis) that eliminate 2D-PDPA's computational limitations, and allows
inclusion of RDC data from multiple vector types in multiple alignment media.
