W674 Nucleic Acids Research, 2009, Vol. 37, Web Server issue (16) with <35% sequence identity to known structures, are shown in TableVI of GeNMR’s Documentation web page. As has been noted before, the performance for shift-based structure determination of novel protein folds is often not as good as it is for known folds (10). Typical structure generation times for this scenario are about 20–35 min, depending of the server load and the size of the structure. Scenario (c): NOE only with homologue In this situation, the user provides only the protein sequence and the NOE assignment table (in CYANA or XPLOR format) to the GeNMR server. Minimally, the user must provide at least one NOE per residue. This scenario matches what is most commonly performed in NMR-based structure calculations with programs such as DYANA, CYANA or XPLOR-NIH. As with ‘Scenario (a)’, the sequence is searched against a non- redundant database of PDB sequences and secondary structures from PPT-DB (19) using BLAST (25) with a sliding length-dependent Expect cutoﬀ, ranging from 10 1 (for <11 residues) to 10 5 (for >50 residues). If the sequence exceeds the cutoﬀ, a structural model is gener- ated using Proteus2 (18). If the protein consists of several discontinuous domains, a program called SFAssembler will link the models together to produce a model of the holo-protein. The resulting model serves as a ‘molecular replacement’ template which is subject to reﬁnement using the experimental NOE data. In particular, the protein is reﬁned with GAfolder using both a NOE-based and a knowledge-based potential. The NOE potential measures the percentage of NOE violations with a diﬀerent weight assigned to short-range NOEs versus long-range NOEs. The exact form of the NOE potential function is given in GeNMR’s Documentation page. Table 1 lists the results of tests conducted on seven proteins with experi- mental NOE data as chosen from the BioMagResBank with 45–97% sequence identity to known structures (the exact match was removed in this test). Additional exam- ples, showing how well GeNMR’s blended NOE+shift reﬁnement process is able to work on remote homologues ( 30% sequence ID) or poor starting structures (up to 4.3 A˚ RMSD) are given in Tables III and IV of GeNMR’s Documentation page. Overall, it is clear that GeNMR produces structures that are very similar to the known structures (<1.5 A˚ RMSD). Typical structure gen- eration times for this scenario are about 15–20 min, depending of the server load and the size of the structure. Scenario (d): NOE only with no homologue Given the ever-diminishing likelihood of ﬁnding a truly novel fold these days, this is actually a relatively rare situation. Indeed, we surveyed all non-redundant (95% sequence identity, length >20 residues) proteins that were solved by NMR and deposited in the PDB from 1 January 2009 to 20 March 2009. Of the 386 polypeptide chains in this particular set, 71% had signiﬁcant (E-value <1 10 5) sequence homology to a known structure, 11% had ‘threadable’ structures (folds similar or identical to known folds using secondary structure matching) that could be detected by GeNMR and 18% exhibited truly novel folds with no sequence or structure similarity to anything else in the PDB. Nevertheless, generating a structure without any prior knowledge is also considered to be the ‘acid test’ of any NMR structure determination protocol as it demonstrates the ability of the program to use only experimental data to generate and reﬁne struc- ture. Because no chemical shift data is provided, no chem- ical shift threading or torsion angle constraint generation can be performed. Instead, a separate algorithm (called PatterNOE) scans for NOE patterns to identify secondary structure elements (helices, beta turns, beta strands) and assigns approximate torsion angle constraints. In particu- lar, PatterNOE analyzes short and medium-range inter- residue NOEs from the NOE constraint list and compares these to well-known inter-residue NOE patterns and dis- tance constraints derived from idealized helices, beta strands (parallel and antiparallel) and beta turns (type I and type II) to identify secondary structure elements. Standard torsion angles derived from these ‘ideal’ helices, beta strands and beta turns are then used to generate the corresponding torsion angle restraints and, subsequently, a starting protein structure. After this step, GeNMR par- titions the NOE constraint ﬁle into short range (1–5 resi- dues), medium range (6–12 residues) and long range (>12 residues) NOEs. Using a genetic algorithm (GAfolder), the initial structures are then reﬁned in a progressive three-step fashion with the short range NOEs being satis- ﬁed ﬁrst, then the medium range NOEs and ﬁnally the long range NOEs. Typically, this process generates an ensemble of structures that are within 3–4 A˚ RMSD of the actual structure. In the ﬁnal step, this ‘rough’ structure is reﬁned using simulated annealing with XPLOR-NIH. Figure 2 shows the initial, unfolded structure for ubiquitin along with the ‘rough’ structure (post GAfolder) and the ﬁnal structural ensemble (post XPLOR). Table 1 lists the results of tests conducted on ﬁve proteins with experimen- tal NOE data where the homology modeling step has been removed from the GeNMR pipeline. These data show that GeNMR produces structures that are very similar to the known structures (<2.5 A˚ RMSD). Typical structure gen- eration times for this scenario are about 40–45 min, depending of the server load and the size of the structure. Scenario (e): NOE+Shift with homologue In this situation, the user provides the sequence, the chem- ical shifts (in BMRB or SHIFT format) and the NOE assignment table (in CYANA or XPLOR format), to the GeNMR server. Given that it is impossible to obtain NOE assignments without chemical shift assignments, this should be the most common scenario for GeNMR. Because all three ‘vital’ data sets are provided as input this also allows GeNMR to make full use of its sequence/structure databases, its chemical shift reﬁnement routines and its NOE reﬁnement routines. As with scenar- ios (a) and (c), a molecular model is generated using Proteus2 or SFAssembler. As before, the resulting model represents a ‘molecular replacement’ template that is sub- ject to reﬁnement using the experimental NOE and chem- ical shift data. In particular, the protein is reﬁned with a