Skip to Main Content
A nearly complete sequential resonance assignment is a key factor leading to successful protein structure determination via NMR spectroscopy. Assuming the availability of a set of NMR spectral peak lists, most of the existing assignment algorithms first use the differences between chemical shift values for common nuclei across multiple spectra to provide the evidence that some pairs of peaks should be assigned to sequentially adjacent amino acid residues in the target protein. They then use these connectivities as constraints to produce a sequential assignment. At various levels of success, these algorithms typically generate a large number of potential connectivity constraints and it grows exponentially as the quality of spectral data decreases. A key observation used in our sequential assignment program, CISA, is that chemical shift residual signature information can be used to improve the connectivity determination and, thus, dramatically decrease the number of predicted connectivity constraints. Fewer connectivity constraints lead to fewer ambiguities in the sequential assignment. Extensive simulation studies on several large test data sets demonstrated that CISA is efficient and effective compared to the three most recently proposed sequential resonance assignment programs, RANDOM, PACES, and MARS.