## 5 Discussion and perspectives  
A direct comparison of the performances of the algorithms described here is not possible since there are not currently available implementations of the older ones. Moreover, due to the limited computational capabilities of the mid nineties, the older methods have been tested only on a limited set of targets. Thushere we try to discuss the effectiveness of these approaches according to what is reported in literature  
In [21] the performances of the reconstructing algorithms have been described when the input is a native contact map of threshold \(8\) A (the review does not include the [PERSON] et al. algorithm, whose performance was reported in [18]). In the presence of no errors in the contact map, all such methods seem to behave quite well. The reconstructed structure, in almost all cases, matches well with the native one and its RMSD distance is quite small (the average RMSD reported is around \(4\)A for all the methods). A more extensive set of tests reported in [21] shows that native contact maps of threshold \(8\)A do not always contain enough information to assure a good reconstruction. For instance, there are native contact maps for which there are completely consistent realizations that are actually really distant from the native structures (in terms of RMSD). This happens when the contact map of threshold \(8\)A contains few or no long range contacts which actually impose the most relevant constraints to the protein structure. From this point of view, contact maps of higher thresholds (from \(12\)A to \(18\)A) seem to be the most informative in the sense that the reconstruction from these maps is always very accurate (the mean RMSD reported in [21] is below \(2\)A which is very close to the experimental error of NMR spectroscopy). Another interesting result is that the complete knowledge of the contact map is not necessary to obtain a good reconstruction. In [21], the authors show that with their algorithm just \(25\%\) of random entries of a contact map is sufficient to obtain good performances (assuming that the map has high threshold and that \(25\%\) of the entries are correct). The scenario changes dramatically when contact maps are allowed to contain errors. A simple way to add errors is to randomly flip the entries of the native maps. With this simple and _artificial_ error model the quality of the reconstructions rapidly decreases. The method of [PERSON] and [PERSON] scores poorly when the amount of random error is around the \(5\%\) of the entire map. The methods of [PERSON] et al. and [PERSON] et al. seem to be more robust since they can tolerate random errors up to \(5\%\) quite well (the mean RMSD obtained is below \(5\)A which usually implies high similarity with the native structure). These results are quite unsatisfactory since the amount of error contained in predicted contact maps is routinely much higher.  
This does not mean that the methods are completely useless for reconstructing the 3D structure from predicted contact maps. One notable example is the method of [PERSON] et al. which obtained encouraging reconstruction results on a large set of proteins [18]. The average RMSD obtained for the reconstructed structures is around \(13\)A. The method of [PERSON] et al. has been tested on a set of \(100\) predicted contact maps [22] (the maps have been predicted with CORNET predictor [10]). In this case, the average RMSD of the reconstructions is around \(16\)A. These results seem promising and suggest that new protein reconstruction algorithms that incorporate more information in the form of constraints and/or predictions will contribute towards the solution of the protein folding problem.