Nucleic Acids Research, 2008, Vol. 36, Web Server issue W501 that the structures generated by CS-Rosetta for ‘blinded’ structural genomics targets and generally better than those generated by CS23D (1 A˚ RMSD versus 3 A˚ RMSD). This highlights the fact that in trying to make CS23D a rapid structure generation tool, we have compromised some of its accuracy and capacity to generate de novo folds. Nevertheless, CS23D appears to perform as well as or better than CS-Rosetta and CHESHIRE in most other structure determination tasks. Furthermore, CS23D is approximately 10 000 times faster. CONCLUSIONS CS23D is designed to address a continuum of structure generation queries. At one extreme, if a submission has 99% sequence identity and exhibits >90% secondary structure conservation to a protein in the PDB, CS23D essentially functions as a homology modeling server with a (unique) chemical shift reﬁnement step. In this case, the structures generated by CS23D are somewhat better than those generated by conventional NMR methods (as measured by Ramachandran violations, chemical shift correlations, hydrogen bonding and other structure evaluation tools). At the other extreme, if a submission has <5% sequence identity and exhibits <30% secondary structure conservation to a protein in the PDB, then CS23D essentially functions as an ab initio 3D structure predictor that employs an extra chemical shift reﬁnement step. Between these extremes, CS23D is able to exploit a variety of other techniques to assemble, extend and reﬁne selected protein fragments (ranging from 20 to 200 residues) to routinely (95% of the time) create high- quality 3D structures that are often better than those generated by conventional methods. Obviously, CS23D is not without some limitations and there are certainly areas where improvements could be made. In particular, smarter use of chemical shift constraints for the Rosetta portion of the program could certainly increase CS23D’s frequency of convergence for queries having completely novel folds. Additionally, CS23D could be improved by allowing it to accept NOE, J-coupling or residual dipolar coupling constraints as part of its input data. This could be particularly useful for modeling protein complexes or proteins with ligands— two areas where chemical shift constraints are essentially useless. Despite these limitations, we believe the speed, accuracy, frequency and reliability with which CS23D can generate (and reﬁne) 3D protein structures—using only chemical shifts and sequence data—should make it a very useful addition to the current arsenal of structure generation and reﬁnement tools available to biomolecular NMR spectroscopists. ACKNOWLEDGEMENTS Funding for this project was provided by the Alberta Prion Research Institute, PrioNet, NSERC and Genome Alberta. 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