An attempt to analyse progress in fold recognition from CASP1 to CASP3.

The CASP experiment has been conducted for the third time. An obvious question is whether there has been progress from CASP1 to CASP3. An analysis depends on many variables, including prediction category, number and difficulty of targets, methods used to evaluate prediction success, and the rules for submission. It also depends on whether progress is measured in terms of all predictions submitted or in terms of the best predictions for each target. The progress made by individual groups is another interesting issue. In view of this complexity and the limited amount of data an objective estimate of progress is difficult to obtain. In spite of such difficulties some estimate of progress is desirable. Here we present an attempt to quantify progress in the fold recognition category from CASP1 to CASP3. The numbers indicate clear progress from CASP1 to CASP2 but no improvement from CASP2 to CASP3. However, we argue that the targets in CASP3 are more difficult as compared to CASP2 which tran..

Characterization of Novel Proteins Based on Known Protein Structures.

this paper. The conservation of structure among distantly related proteins is the basis of structure-based functional annotations of uncharacterized proteins (Martin et al.

Automated Large Scale Evaluation of Protein Structure Predictions.

Evaluation and assessment are critical issues in CASP experiments. Automated procedures are necessary to compare a large number of predictions with the target folds. The evaluation has to reveal the maximum extent of similarity between predictions and targets, it should be applicable across prediction categories, and it should be transparent and accessible to a wide community. Here we present an automated evaluation scheme which is an attempt to meet these requirements. In the implementation and execution of this scheme we had to solve or circumvent problems of convergence, where algorithms fail to find optimum solutions, problems of ambiguity where no unique optimum solution exists, and problems in ranking and interpretation. Key features of this implementation are (1) the root mean square deviation of structure superimposition is kept close to a constant value throughout the evaluation and (2) all structural matches found between two folds are taken into account. We discuss these poi..