3D-Jury is coupled to the continuous online server evaluation program, LiveBench (http://BioInfo.PL/LiveBench/)

MaxSub is a new and independently developed method that further builds and extends some of the evaluation methods introduced at CASP3. MaxSub aims at identifying the largest subset of C(alpha) atoms of a model that superimpose 'well' over the experimental structure, and produces a single normalized score that represents the quality of the model. Because there exists no evaluation method for assessment measures of predicted models, it is not easy to evaluate how good our new measure is. Even though an exact comparison of MaxSub and the CASP3 assessment is not straightforward, here we use a test-bed extracted from the CASP3 fold-recognition models. A rough qualitative comparison of the performance of MaxSub vis-a-vis the human-expert assessment carried out at CASP3 shows that there is a good agreement for the more accurate models and for the better predicting groups. As expected, some differences were observed among the medium to poor models and groups. Overall, the top six predicting groups ranked using the fully automated MaxSub are also the top six groups ranked at CASP3. We conclude that MaxSub is a suitable method for the automatic evaluation of models.

In protein fold recognition, one assigns a probe amino acid sequence of unknown structure to one of a library of target 3D structures. Correct assignment depends on effective scoring of the probe sequence for its compatibility with each of the target structures. Here we show that, in addition to the amino acid sequence of the probe, sequence-derived properties of the probe sequence (such as the predicted secondary structure) are useful in fold assignment. The additional measure of compatibility between probe and target is the level of agreement between the predicted secondary structure of the probe and the known secondary structure of the target fold. That is, we recommend a sequence-structure compatibility function that combines previously developed compatibility functions (such as the 3D-1D scores of Bowie et al. [1991] or sequence-sequence replacement tables) with the predicted secondary structure of the probe sequence. The effect on fold assignment of adding predicted secondary structure is evaluated here by using a benchmark set of proteins (Fischer et al., 1996a). The 3D structures of the probe sequences of the benchmark are actually known, but are ignored by our method. The results show that the inclusion of the predicted secondary structure improves fold assignment by about 25%. The results also show that, if the true secondary structure of the probe were known, correct fold assignment would increase by an additional 8-32%. We conclude that incorporating sequence-derived predictions significantly improves assignment of sequences to known 3D folds. Finally, we apply the new method to assign folds to sequences in the SWISSPROT database; six fold assignments are given that are not detectable by standard sequence-sequence comparison methods; for two of these, the fold is known from X-ray crystallography and the fold assignment is correct.

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Recent assessments of structure prediction have demonstrated that i although fold recognition methods can often identify remote similarities when standard sequence search methods fail, the score of the top-ranking fold is not always signicant enough to allow a con dent prediction; ii the use of structural information such as secondary structure increases recognition accuracy; iii modern sequencebased methods incorporating evolutionary information from neighboring sequences can often identify very remote similarities; iv there is no one single method that is superior to other methods when evaluated over a wide range of targets, and v extensive h uman-expert intervention is usually required for the most di cult prediction targets. Here, I describe a new, hybrid fold recognition method that incorporates structural and evolutionary information into a single fully automated method. This work is a rst attempt towards the automation of some of the processes that are often applied by h uman predictors. The method is tested with two fold-recognition benchmarks demonstrating a superior performance. The higher sensitivity and selectivity enable the applicability of this method at genomic scales.

Results of several sessions of the CAFASP and LiveBench programs for assessment of performance of fold-recognition servers carried out via the meta server are available at http://BioInfo.PL/services.html.

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