THE DISCON ALGORITHM, AN ACCURATE AND ROBUST ALTERNATIVE TO AN EIGENVALUE SOLUTION FOR EXTRACTING EXPERIMENTAL DISTANCES FROM NOESY DATA

The DISCON algorithm is a novel, computationally direct, iterative solution for cross-relaxation rates that can be applied to a single NOESY data set without prior knowledge of the applicable motional regime or a structural model. Tests with simulated NOESY data demonstrate that it is less subject to error propagation from incorrect autopeak values than an eigenvalue solution. The resulting cross rates are fully corrected for secondary NOE contributions. The algorithm has been incorporated into a software suite that provides distance estimates and their experimental precision from raw NOESY data. Missing values in the data matrix are approximated as class averages of existing data rather than calculated values based on a starting conformational model; thus, more accurate initial distance estimates for modeling can be derived without a starting model bias. The accuracy of the program is demonstrated using NOESY data for a rigid steroid, Its application for conformational analysis is illustrated with a flexible prostaglandin analog. © 1993 Academic Press, Inc.