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The importance of coordinated testing for fold recognition algorithms has already been stressed above. Fischer et al.fischer:bench have developed a number of benchmark sets of query and library folds. During the final stages of this work, SIVA was applied to the first benchmark in the series. This benchmark consists of 68 query folds and 301 library folds. These are whole chain structures taken directly from the PDB (unlike the CATH domains used in this work). Using secondary structure predictions, hydrophobicity and multiple sequences exactly as described above, SIVA gave 32 correct top hits according to an assessment performed by Daniel Fischer. The authors of the benchmark have reported 3D-1D methods which obtain 50 or more correct top hits (see their web pages for more details). Furthermore, their application of sequence-only alignments using established substitution matrices (PAM250, BLOSUM62, GONNET) on this benchmark results in around 40 correct top hits. How can this be possible, when it has been shown in this work that SIVA is far superior to standard sequence methods?
One possible explanation is to be found in the alignment algorithms used. Our comparison between Smith Waterman and SIVA may not be very fair because of the length-dependencies of our method (see Section 4.4.2). The Smith Waterman local alignment algorithm does not make effective use of sequence length information. The approach of Fischer and colleagues is to use the so-called global-local alignment and rank by raw scores[Fischer et al., 1996,Fischer & Eisenberg, 1996]. The global-local alignment ensures that the entire library sequence is aligned and penalised for any gaps (including at its termini), whilst the termini of the query sequence need not be aligned (or penalised) as in a standard local alignment. This approach is suited to scanning multi-domain queries against a library of single domains, and it should not be as length-dependent as SIVA. To some extent, however, the global-local algorithm may be particularly suited to the benchmark folds. In particular, the optimisation of gap penalties by Fischer et al.fischer:bench appears to be more severe than in this work.
The most compatible library fold identified by SIVA in the benchmark for query sequence 1tahA0[Noble et al., 1993, triacylglycerol hydrolase from Pseudomonas glumae, 318 residues] was 2liv00[Sack et al., 1989, leucine/isoleucine/valine periplasmic binding protein, 344 residues]. The `correct' answer 1tca00 (a yeast triacylglycerol hydrolase, 317 residues) ranked second. However, 1tahA0 and 2liv00 share 66 carbon- atoms which can be superposed with 2.5Å RMSd. The equivalenced residues encompass 5 strands and two helices. None of the other `incorrect' top hits in the benchmark trials had such clear structural similarity. The exclusion of 1mbc00 (sperm whale myoglobin) as a correct hit for 1cpcL0 (phycocyanin, a light harvesting protein from cyanobacteria) in the benchmark evaluation is debatable; the two proteins have completely different functions and share minimal sequence identity (10%). In our benchmark results for 1cpcL0, 1mbc00 ranks top, whilst the `correct' answer 1colA0 (colicin) ranks 23rd.
Of the 340 benchmark folds, 125 (37%) have less than 5 multiple sequences (gathered using our methods, see Appendix A), unlike the testing domains used in this chapter which have at least 5 or 10 multiple sequences. The detrimental effect of this lack of multiple sequence information has not yet been measured directly on the benchmark folds. When the trial of 78 vs. 197 folds (see Section 4.4.1) is repeated using single sequences (using hydrophobicity and DSC prediction information) only correct top hits are obtained. This is not a big improvement over the Smith Waterman results () and is substantially worse than the multiple sequence results (). The existence of large number of folds in the benchmark with poor evolutionary information is the most likely explanation for the poor performance of SIVA in this instance.