It is not able to support the generation of protein com- plexes that consist of multiple, independent chains. The resulting SFAssembler model represents a ‘molecular replacement’ template which is subject to reﬁnement using experimental chemical shift data. In particular, the protein is reﬁned with a torsion-based genetic algorithm (GAfolder) using the chemical shift and knowledge-based potentials described earlier. The exact methodology and overall performance of this kind of structure generation protocol has been described in detail in the CS23D paper (10). Since that publication, a number of improvements were made to this algorithm and incorporated into GeNMR, including the use of parallelism, increasing the size of the reference structure database, enhancing the sampling protocol and improving the energy functions. An indication of GeNMR’s performance under this sce- nario (using ﬁve proteins with sequence identities ranging from 35% to 98%) is given at the top of Table 1. More complete results are shown in Table V of GeNMR’s Documentation web page which show the results of a test conducted on 50 proteins randomly chosen from the Nucleic Acids Research, 2009, Vol. 37, Web Server issue W673 BioMagResBank (16) with 35–99% sequence identity to known structures. Typical structure generation times for this scenario are about 15–20 min, depending of the server load and the size of the structure. Scenario (b): Shift only with no homologue As with ‘Scenario (a)’ this kind of query falls into the category of shift-based structure determination is handled by the CS23D (8) component of GeNMR. In this situation homology modeling has failed and, as a result, several alternative routes to structure modeling are attempted including chemical shift threading, sub-fragment assembly and de novo structure generation by Rosetta. As before, the exact methodology and overall performance of this kind of structure generation protocol has been described in detail in the CS23D paper (10). An indication of GeNMR’s performance under this scenario is given in Table 1 using a set of four proteins where all homologues have been removed from the database prior to testing. More complete results, based on tests conducted on 15 proteins randomly chosen from the BioMagResBank Table 1. Performance assessment of GeNMR under diﬀerent data input scenarios using a default of 10 structure models (see text for an explanation of each scenario) RMSD (A˚ ) to reference PDB Calculation Time (min) No. of distance restraints Protein Name (PDB ID) Sequence ID (%) Scernario (a) – Shift data only—query has homologue in database Ubiquitin (1UBQA) 62 SeR13 (2K1HA) 58 Ig Domain of Palladin (2DM2A) 35 Abl Kinase (2HYYA) 98 RGD-Hirudin (2JOOA) 88 Scernario (b) – Shift data only—query has NO homologue in database Ubiquitin (1UBQA) – 4-helix Bundle (2I7UA) – Discoidin Domain DDR2 (2Z4FA) – CheW (2HO9A) – Scernario (c) – NOE data only—query has homologue in database Cyclophilin (1CWCA) 75 Regulatory Protein E2 (1A7G) 70 Serine Protein Inhibitor (3CI2) 82 DnaB (1JWE) 47 Superoxide Dismutase (2AF2) 97 PyJ Protein (1FAF) 95 Neurotoxin II (1NOR) 87 Scernario (d) – NOE data only—query has NO homologue in database Ubiquitin (1UBQ) – Forkhead FOXO4 (1E17) – Proﬁlin (1AWI) – Mu DNA Binding Protein (2EZI) – SV40 ORI Binding Protein (1TBD) – Scernario (e) – NOE+Shift data—query has homologue in database Response Regulator Spo0F (1FSP) 96 Proﬁlin (1AWI) 99 Interleukin 4 (1BBN) 87 Metalloproteinase 12 (1YCM) 99 Ubiquitin (1UBQ) 96 Scernario (f) – NOE+Shift data—query has NO homologue in database Interleukin 4 (1BNN) – SV40 ORI Binding Protein (1TBD) – Superoxide Dismutase (2AF2) – Ribosomal Protein S4 (1C05) – Neurotoxin II (1NOR) – 1.55 2.31 1.29 1.76 1.32 2.55 1.48 1.63 2.66 0.98 1.67 1.49 0.99 1.27 0.05 0.92 1.38 2.20 1.96 2.43 2.61 1.17 0.37 1.68 1.44 0.42 1.69 1.21 1.25 2.86 0.98 10 13 18 19 15 18 22 36 25 23 16 19 19 14 10 16 21 48 46 25 46 16 28 20 19 25 25 23 16 21 11 – – – – – – – – – 4096 1197 961 1194 2672 870 540 1318 1294 1794 1009 1709 1835 1794 917 3544 1318 917 1709 2672 2256 482