CAFASP-1: Critical assessment of fully automated structure prediction methods

The results of the first Critical Assessment of Fully Automated Structure P rediction (CAFASP-1) are presented. The objective was to evaluate the succe ss rates of fully automatic web servers for fold recognition which are avai lable to the community This study was based on the targets used in the thir d meeting on the Critical Assessment of Techniques for Protein Structure Pr ediction (CASP-3). However, unlike CASP-3, the study was not a blind trial, as it was held after the structures of the targets were known. The aim was to assess the performance of methods without the user intervention that se veral groups used in their CASP-3 submissions. Although it is clear that "h uman plus machine" predictions are superior to automated ones, this CAFASP- 1 experiment is extremely valuable for users of our methods; it provides an indication of the performance of the methods alone, and not of the "human plus machine" performance assessed in CASP, This information may aid users in choosing which programs they wish to use and in evaluating the reliabili ty of the programs when applied to their specific prediction targets. In ad dition, evaluation of fully automated methods is particularly important to assess their applicability at genomic scales. For each target, groups submitted the top-ranking folds generated from thei r servers. In CAFASP-1 we concentrated on fold-recognition web servers only and evaluated only recognition of the correct fold, and not, as in CASP-3, alignment accuracy. Although some performance differences appeared within each of the four target categories used here, overall, no single server has proved markedly superior to the others. The results showed that current fu lly automated fold recognition servers can often identify remote similariti es when pairwise sequence search methods fail. Nevertheless, in only a few cases outside the family-level targets has the score of the top-ranking fol d been significant enough to allow for a confident fully automated predicti on. Because the goals, rules, and procedures of CAFASP-1 were different fro m those used at CASP-3, the results reported here are not comparable with t hose reported in CASP-3. Nevertheless, it is clear that current automated f old recognition methods can not yet compete with "human-expert plus machine " predictions. Finally, CAFASP-1 has been useful in identifying the requirements for a fut ure blind trial of automated served-based protein structure prediction. (C) 1999 Wiley-Liss, Inc.