W500 Nucleic Acids Research, 2008, Vol. 36, Web Server issue each of the 62 proteins was submitted to the CS23D server using its default settings. The structure of each of the lowest energy CS23D generated structures was evaluated by comparing its RMSD to that of the native structure. CS23D was able to generate a converged structure in 97% (60/62) of the cases. Further, the average Ca RMSD between the known structure and the CS23D generated structures was 1.1 A˚ RMSD (the range was 0.1–3.1 A˚ ). The average time to generate and reﬁne each structure was 10.2 min. This varied depending on the size, sequence similarity to known structure and structure generation methods (homology modeling/fragment assembly, chemi- cal shift threading, de novo structure generation) used by CS23D. Interestingly, the majority of cases (92%) were solved by CS23D using large fragment (i.e. homology) modeling supplemented with fragment concatenation. The high level of returned results probably exaggerates the real frequency with which CS23D would generate converged structures. This is because many of the proteins in RefDB (and by default the BMRB) are well-studied proteins for which many homologues have been character- ized. To better assess the eﬀectiveness of CS23D among newly deposited BMRB entries (for which novel folds or structural genomics entries may be more common), we downloaded all the 3 April 2008 ‘Recent Releases’ from the BMRB chemical shift repository and processed them through CS23D (total = 48 ﬁles). As before, the PDB structure of each query protein was removed from CS23D’s sequence/structure database prior to structure generation. Of the 39 ﬁles for which structures were available in the PDB (for comparative purposes), CS23D succeeded in generating converged structures for 36 of them (Web Table 4), with approximately the same proportion generated via homology modeling/fragment assembly (82%), chemical shift threading (14%) and de novo structure generation (4%) as seen in Web Table 3. Interestingly, several independent estimates of the reported frequency of completely novel folds being deposited into the PDB or being solved by NMR suggest that only about 5% of all structures have this characteristic (28). Based on these data, we would estimate that CS23D, under routine use by the NMR community, would be able to generate a converged structure at least 95% of the time. To assess CS23D’s capabilities in generating and reﬁning de novo structures, we conducted several tests (Tables 5 and 6 on the CS23D Documentation page). In the ﬁrst instance, 10 small proteins (<130 residues) were processed through CS23D with the subfragment assembly and chemical shift threading options turned oﬀ (Table 5). This forced the program to use only its ab initio folding components (torsion angle constraints + Rosetta). The average backbone RMSD for this set of ab initio CS23D- folded proteins was 2.1 A˚ . Table 6 illustrates the perfor- mance of CS23D relative to the level of sequence identity and subfragment or chemical shift threading matches. In preparing Table 6, we used ubiquitin as the query and progressively removed each of the matching homologues from CS23D’s structure databases. In total, ubiquitin had nine proteins that exhibited sequence identity of >40% and BLAST expect scores of <10 10 over some signiﬁcant portion of the ubiquitin sequence. The backbone RMSD between the CS23D structure and the ‘true’ ubiquitin structure ranged from 0.5 A˚ (at 90–100% identity) to 0.9 A˚ (at 40% sequence identity). After these were removed, we found an additional 14 proteins in the PDB that exhibited secondary structure or torsion-angle identity of >25% and BLAST scores of <10 10. The RMSD between the CS23D structure and the ‘true’ ubiquitin structure in these cases ranged from 0.8 A˚ (at 35–55% ‘torsion’ identity) to 4.2 A˚ (at 25–35% ‘torsion’ identity). After these were removed from the PDB, we found that CS23D was still able to generate moderately good models of ubiquitin (within 2.8 A˚ RMSD) in 3/10 attempts. Figure 3 summarizes the results from Web Tables 3–6 in terms of the performance of CS23D’s three diﬀerent structure generation schemas (subfragment assembly + homology modeling, chemical shift threading and ab initio structure generation) relative to the level of sequence identity of the matching templates or subfragments. Finally, to compare CS23D’s performance to previously described programs—CHESHIRE (13) and CS-Rosetta (14)—we ran CS23D on a number of the testing/training proteins used in these papers. This included 12 proteins used in their initial training and testing (Table 7a of the Web Documentation page), seven proteins that failed to converge for CS-Rosetta (Table 7b) and six proteins that were part of a blinded test for CS-Rosetta and for which chemical shifts were available (Table 7c). As seen in Table 7a, the average backbone RMSD for CHESHIRE, CS-Rosetta and CS23D predictions is 1.52, 1.48 and 1.64 A˚, respectively. In other words, there is little to dis- tinguish between the three methods. As shown in Table 7b, CS23D was able to generate good quality struc- tures for four of the seven structures (57%) that CS-Rosetta could not generate. Interestingly, both pro- grams failed for 1JW3, 1TVG and 2GDT. Table 7c shows Figure 3. A scatter plot showing the performance of CS23D’s three approaches to structure generation (subfragment assembly + homology modeling, chemical shift threading and ab initio structure generation) relative to the level of sequence identity of the matching templates or subfragments. Data from Tables 3–6 in the CS23D web Documentation pages were used to assemble this graph.