The results of the second Critical Assessment of Fully Automated Structure Prediction (CAFASP2) are presented. The goals of CAFASP are to (i) assess the performance of fully automatic web servers for structure prediction, by using the same blind prediction targets as those used at CASP4, (ii) inform the community of users about the capabilities of the servers, (iii) allow human groups participating in CASP to use and analyze the results of the servers while preparing their nonautomated predictions for CASP, and (iv) compare the performance of the automated servers to that of the human-expert groups of CASP. More than 30 servers from around the world participated in CAFASP2, covering all categories of structure prediction. The category with the largest participation was fold recognition, where 24 CAFASP servers filed predictions along with 103 other CASP human groups. The CAFASP evaluation indicated that it is difficult to establish an exact ranking of the servers because the number of prediction targets was relatively small and the differences among many servers were also small. However, roughly a group of five “best” fold recognition servers could be identified. The CASP evaluation identified the same group of top servers albeit with a slightly different relative order. Both evaluations ranked a semiautomated method named CAFASP-CONSENSUS, that filed predictions using the CAFASP results of the servers, above any of the individual servers. Although the predictions of the CAFASP servers were available to human CASP predictors before the CASP submission deadline, the CASP assessment identified only 11 human groups that performed better than the best server. Furthermore, about one fourth of the top 30 performing groups corresponded to automated servers. At least half of the top 11 groups corresponded to human groups that also had a server in CAFASP or to human groups that used the CAFASP results to prepare their predictions. In particular, the CAFASP-CONSENSUS group was ranked 7. This shows that the automated predictions of the servers can be very helpful to human predictors. We conclude that as servers continue to improve, they will become increasingly important in any prediction process, especially when dealing with genome-scale prediction tasks. We expect that in the near future, the performance difference between humans and machines will continue to narrow and that fully automated structure prediction will become an effective companion and complement to experimental structural genomics. Proteins 2001;Suppl 5:171–183. © 2002 Wiley-Liss, Inc.