source, ab initio protein structure prediction program developed by David Baker’s lab over the last decade (25). It uses the concept of fragment-based assembly similar to that employed by SFassembler, but at a much lower level of sequence identity and over much shorter fragment lengths. CS23D’s implementation of Rosetta uses a local fragment generation routine constrained by PREDITOR- generated torsion angles. It employs Rosetta’s default values for generating and evaluating protein structures, but these candidate structures are further evaluated using a combination of weighted chemical shift correlation coeﬃcients (a chemical shift ‘energy’). The weightings used in the chemical shift evaluation step are provided in the CS23D Documentation pages. To limit the time taken in this step, CS23D limits the number of Rosetta-generated candidate structures to 300. The structure exhibiting the lowest chemical shift energy is identiﬁed and reﬁned using GAfolder. If after the reﬁnement step, the structure has a positive energy value, CS23D generates the following warning message ‘Structure Did Not Converge’. However, the coordinates of the highest scoring structure are still presented in the output so that the user may attempt to use these as starting coordinates for a more conventional NOE-based structure determination. For its energy minimization and chemical shift reﬁne- ment step, CS23D employs a torsion-angle-based energy minimizer called GAfolder that uses a genetic algorithm to sample conformation space. The method is similar to that employed by GENFOLD (26), although GAfolder uses cyclic coordinate descent (23) to perform more eﬃcient writhe operations and it uses coordinates generated by the PREDITOR step (Figure 2) to perform segment swapping operations. The GAfolder potential energy function is a knowledge-based potential that includes information on predicted/known secondary structure, radius of gyration, hydrogen-bond energies, number of hydrogen bonds, allowed backbone and side-chain torsion angles, atom contact radii (bump checks), disulﬁde bonding informa- tion and a modiﬁed threading energy based on the Bryant and Lawrence potential (27). The chemical shift compo- nent of the GAfolder potential uses weighted correlation coeﬃcients calculated between the observed and SHIFTX (24) calculated shifts of the structure being reﬁned. The weighting coeﬃcients for all of the parameters are given in Figure 2. An illustration of how chemical shift minimization can improve the structure of a poorly modeled initial structure. This shows the improvement seen in a deliberately distorted model of ubiquitin. In this case, the protein regains much of its lost secondary structure and the RMSD converges from 2.5 to 1.0 A˚ from the native structure. Nucleic Acids Research, 2008, Vol. 36, Web Server issue W499 the CS23D Documentation web page. Evaluations using nearly 44 000 3D structure predictions from the CASP7 collection showed that the GAfolder potential (without the chemical shift component) was able to identify the native or near-native (<2.5 A˚ RMSD) structure in 80/90 (89%) of the CASP7 targets. RESULTS AND EVALUATION CS23D was evaluated in six diﬀerent ways: (i) assessing its ability to reﬁne input structures; (ii) assessing the quality of its structures relative to known X-ray and NMR struc- tures; (iii) assessing it capacity or robustness in generating (and reﬁning) randomly selected structures from the BMRB; (iv) assessing its capacity to generate and reﬁne very recently submitted BMRB assignments; (v) assessing its capability to generate and reﬁne de novo structures and (vi) comparing its performance to previously described programs (13,14). In the ﬁrst assessment, the quality of CS23D’s chemical shift/energy reﬁnement routine was investigated by looking at how distorted or nonnative structures could be reﬁned towards near-native or native structures. This was tested on a collection of eight proteins for which complete chemical shifts and high-quality X-ray structures were available. These structures were then distorted or damaged using random torsion angle dis- placement. After this distortion step the structures were run through GAFolder for 3000 iterations and the resulting structures were assessed in terms of the RMSD between the calculated structure and the native structures as well as the energy diﬀerence between the native and the calculated structure. Table 1 in the CS23D Documentation web page shows the results of these calculations. On an average, the RMSD was lowered by 1.0 A˚ , with the ﬁnal structure having an average RMSD of 1.5 A˚ relative to the native X-ray structure. Figure 2 shows an example of how GAFolder was able to correct a distorted (2.5 A˚ RMSD) structure of ubiquitin and bring it back to within 1.0 A˚ RMSD of the ‘true’ ubiquitin structure. Table 2 (Documentation web page) illustrates the results of comparing the X-ray structure, NMR structure and CS23D-derived structure for nine proteins for which both X-ray and NMR structures exist. As seen in this table, the CS23D-derived structures have about one-third of the number of Ramachandran violations and omega angle violations found in their conventionally determined NMR counterparts. Likewise, the proportion of hydrogen bonded residues, hydrogen-bond energies and average chemical shift correlation coeﬃcients are about 5–10% higher for CS23D-dervied structures than conventionally determined NMR structures. In fact, the CS23D structures are generally closer in structure quality characteristics to the corresponding high-resolution X-ray structures. To assess CS23D’s eﬀectiveness at generating high- quality structures from standard BMRB ﬁles, 62 BMRB ﬁles of monomeric proteins were randomly selected from the RefDB database (18). In order to avoid having the query match itself, the PDB structure of each of these query proteins was removed from CS23D’s sequence/ structure databases. After this database-editing step,