The application of three-dimensional (3D) heteronuclear NMR spectroscopy to the sequential assignment of the NMR spectra of larger proteins is presented, using uniformly labeled (~ 95%)[15N] interleukin ß, a protein of 153 residuesand molecular mass of 17.4 kDa, as an example. The two-dimensional (2D) 600-MHz spectra of interleukin\ß are too complex for complete analysis, owing to extensive cross-peak overlap and chemical shift degeneracy. We show that the combined use of 3D'H-15N Hartmann-Hahn-multiple quantum coherence (HOHAHA-HMQC) and nuclear Overhauser-multiple quantum coherence (NOESY-HMQC) spectroscopy, designed to provide the necessary through-bond and through-space correlations for sequential assignment, provides a practical general-purpose method for resolving ambiguities which severely limit the analysis of conventional 2D NMR spectra. The absence of overlapping cross-peaks in these 3D spectra allows the unambiguous identification of CH (z')-NH (z+ l) and NH (z')-NH (z'+ l) through-space nuclear Overhauser connectivities necessary for connecting a particular C “H-(z)-NH (z) through-bond correlation with itsassociated through-space sequential cross-peak. The problem of amide NH chemical shift degeneracy in the* H NMR spectrum is therefore effectively removed, and the assignment procedure simply involves inspecting a series of 2D- slices edited by the chemical shift of the directly bonded 15N atom. Connections between residues can be identified almost without any knowledge of the spin system types involved, though this type of information is clearly required for the eventual placement of the connected residues within the primary sequence. Strategies for obtaining identification of spin system types include traditional analysis of the spectrum of nonlabile protons, site-directed mutagenesis, and specific labeling of selected amino acids. It is envisaged that the intrinsic simplicity of the 3D heteronuclear spectra, even for proteins of 150-200 residues, will permit the development of efficient com-puter-assisted or automated sequential assignment methods.