We have seen in the previous chapters that the bioinformatics tools are not yet mature enough to be used as routine instruments to design structural biology experiments. However, a very positive feature of these computational methods is that when they work [see equations (6) and (7)] they work very well. The following data are shown: the percentage of domains that are correctly predicted (see text for details) PC_C, the average deviation between the real and the predicted beginning of the domain Delta_b, and the average difference between the real and the predicted end of the domain Delta_e (standard deviations of the mean in parentheses). Table 5 shows the percentage of domains that are correctly predicted [according to equations (6) and (7) ] and the discrepancy between the real and the predicted boundary in the subset of domains that are correctly predicted. It appears that only a relatively modest fraction of the domains can be considered to be well predicted, according to the criteria defined by equations (6) and (7) . The percentage of good predictions is about 30-40%, with some prediction methods behaving considerably better than the others and able to well predict about 60% of the domains. The average values of Delta_b (see Methods) are close to and lower than 0 for all the prediction methods. Also the values of Delta_e are very small, though their absolute value tends to be slightly larger than that of Delta_b. Interestingly, the Delta_e values are positive, on average, for each prediction method.