title: Protein structure elucidation from minimal NMR data: the CLOUDS approach.
abstract: In this chapter we review automated methods of protein NMR data analysis and expand on the assignment-independent CLOUDS approach. As presented, given a set of reliable NOEs it is feasible to derive a spatial H-atom distribution that provides a low-resolution image of the protein structure. In order to generate such a list of unambiguous NOEs, a probabilistic assessment of the NOE identities (in terms of frequency-labeled H-atom sources) was developed on the basis of Bayesian inference. The methodology, encompassing programs SPI and BACUS, provides a list of "clean" NOEs that does not hinge on prior knowledge of sequence-specific resonance assignments or a preliminary structural model. As such, the combined SPI/BACUS approach, intrinsically adaptable to include 13C- and/or 15N-edited experiments, affords a useful tool for the analysis of NMR data irrespective of whether the adopted structure calculation protocol is assignment-dependent.
authors.affiliation: National Institutes of Diabetes and Digestive and Kidney Diseases, National Institutes of Health, Bethesda, Maryland 20892, USA.
authors.forename: Alexander
authors.initials: A
authors.lastname: Grishaev
authors.forename: Miguel
authors.initials: M
authors.lastname: Llinás
mesh_terms: D001499:Bayes Theorem; D003198:Computer Simulation; D003627:Data Interpretation, Statistical; D009682:Magnetic Resonance Spectroscopy / Q000379:methods*; D009010:Monte Carlo Method; D017434:Protein Structure, Tertiary; D011506:Proteins / Q000737:chemistry*; D012984:Software
chemical_list: D011506:Proteins