The Grail problem

Genome sequencing projects have created a heavy demand for protein structure prediction. Structure prediction at present relies on modeling based on data collected from the many proteins for which both sequence and structure are known (reviewed by Baker, Nature 2000, 405:39-42). When the sequence identity between a protein of known structure and the putative homolog is high (about 50% or greater), most existing modeling methods work well. The difficulty arises in the most interesting cases, when sequence identity to proteins of known structure is low or absent. No completely reliable methods for structure prediction exist for these cases at present. New methods and claimed improvements to existing ones are always evaluated on test systems in the same way: the predicted structure is superimposed onto the true one so as to minimize the root-mean-square deviation in atomic coordinates - a measure of the difference in position - between all pairs of equivalent atoms (which may be alpha carbons or all backbone atoms; side-chains are usually excluded). This single number, the root-mean-square or rms deviation, is then reported as the measure of how well the predicted and actual structures agree.The use of the rms deviation as a measure of the quality of a structure prediction has its origins in the early days of protein crystallography, when there was considerable interest in the precision of experimentally determined protein structures. Two different structures of the same protein solved, for example, in two different laboratories, or by the same laboratory in two different crystal forms, would be superimposed and the rms deviation would be calculated. Well-determined structures at high resolution often yield rms deviations of less than 0.5 Angstroms in such a comparison.But predicted structures are not experimental ones, and the rms deviations between models of homologous protein structures and real ones are typically between 2 and 4 Angstroms, even in the best cases. A