Assessing sequence comparison methods with reliable structurally identified distant evolutionary relationships

Pairwise sequence comparison methods have been assessed using proteins whose relationships are known reliably from their structures and functions, as described in the scop database [Murzin, A. G., Brenner, S. E., Hubbard, T. & Chothia C. (1995) J. Mol. Biol. 247, 536–540]. The evaluation tested the programs blast [Altschul, S. F., Gish, W., Miller, W., Myers, E. W. & Lipman, D. J. (1990). J. Mol. Biol. 215, 403–410], wu-blast2 [Altschul, S. F. & Gish, W. (1996) Methods Enzymol. 266, 460–480], fasta [Pearson, W. R. & Lipman, D. J. (1988) Proc. Natl. Acad. Sci. USA 85, 2444–2448], and ssearch [Smith, T. F. & Waterman, M. S. (1981) J. Mol. Biol. 147, 195–197] and their scoring schemes. The error rate of all algorithms is greatly reduced by using statistical scores to evaluate matches rather than percentage identity or raw scores. The E-value statistical scores of ssearch and fasta are reliable: the number of false positives found in our tests agrees well with the scores reported. However, the P-values reported by blast and wu-blast2 exaggerate significance by orders of magnitude. ssearch, fasta ktup = 1, and wu-blast2 perform best, and they are capable of detecting almost all relationships between proteins whose sequence identities are >30%. For more distantly related proteins, they do much less well; only one-half of the relationships between proteins with 20–30% identity are found. Because many homologs have low sequence similarity, most distant relationships cannot be detected by any pairwise comparison method; however, those which are identified may be used with confidence.

A unified statistical framework for sequence comparison and structure comparison

We present an approach for assessing the significance of sequence and structure comparisons by using nearly identical statistical formalisms for both sequence and structure. Doing so involves an all-vs.-all comparison of protein domains [taken here from the Structural Classification of Proteins (scop) database] and then fitting a simple distribution function to the observed scores. By using this distribution, we can attach a statistical significance to each comparison score in the form of a P value, the probability that a better score would occur by chance. As expected, we find that the scores for sequence matching follow an extreme-value distribution. The agreement, moreover, between the P values that we derive from this distribution and those reported by standard programs (e.g., blast and fasta validates our approach. Structure comparison scores also follow an extreme-value distribution when the statistics are expressed in terms of a structural alignment score (essentially the sum of reciprocated distances between aligned atoms minus gap penalties). We find that the traditional metric of structural similarity, the rms deviation in atom positions after fitting aligned atoms, follows a different distribution of scores and does not perform as well as the structural alignment score. Comparison of the sequence and structure statistics for pairs of proteins known to be related distantly shows that structural comparison is able to detect approximately twice as many distant relationships as sequence comparison at the same error rate. The comparison also indicates that there are very few pairs with significant similarity in terms of sequence but not structure whereas many pairs have significant similarity in terms of structure but not sequence.