Homology-extended sequence alignment

Abstract

We present a profile-profile multiple alignment strategy that uses database searching to collect homologues for each sequence in a given set, in order to enrich their available evolutionary information for the alignment. For each of the alignment sequences, the putative homologous sequences that score above a pre-defined threshold are incorporated into a position-specific pre-alignment profile. The enriched position-specific profile is used for standard progressive alignment, thereby more accurately describing the characteristic features of the given sequence set. We show that owing to the incorporation of the pre-alignment information into a standard progressive multiple alignment routine, the alignment quality between distant sequences increases significantly and outperforms state-of-the-art methods, such as T-COFFEE and MUSCLE. We also show that although entirely sequence-based, our novel strategy is better at aligning distant sequences when compared with a recent contact-based alignment method. Therefore, our pre-alignment profile strategy should be advantageous for applications that rely on high alignment accuracy such as local structure prediction, comparative modelling and threading.

Figure 1

The schematic representation of the PRALINE_PSI strategy. Each sequence is submitted as a PSI-BLAST query to a database of choice. The resulting local alignments are filtered for redundancy and if no hits are found or all hits are redundant, the search is re-run using a new E-value threshold 10 times less stringent. The final local alignments for each sequence are converted to a pre-profile and given to the PRALINE alignment algorithm.

Figure 2

Comparison of alignment methods on the 624 HOMSTRAD pairwise alignments (Q score). The difference (Δ) between the average scores of each tested alignment method and that of the PRALINE_BASIC method is taken at 5% intervals. The PRALINE_PREPRO values for the pairwise alignments are identical to those of PRALINE_BASIC and, therefore, they are not included. The PRALINE_PSI scores are for the incremental strategy starting with an E-value of 10⁻⁶.

Figure 3

Sequence alignments of the protein methyltransferase (HOMSTRAD family ‘SopU_methylase_N’). The numbers in parentheses represent the Q scores of each alignment. The bottom alignment (HOMSTRAD) is the reference alignment derived from structure super-positioning and shows the secondary structures (DSSP-derived). Both the contact-based and the single sequence-based methods show a shift in the matched secondary structure elements, which is entirely prevented by the use of the extended evolutionary information. Correctly aligned residue pairs are denoted by a ‘∧’ sign.

Figure 4

The effects of using E-value thresholds of increasing stringency in PRALINE_PSI on the 624 HOMSTRAD pairwise alignments. (A) The difference (Δ) between the average Q scores of PRALINE_PSI and the basic PRALINE method, for all cases (0–100% sequence identity) and separately, cases between 0 and 30%, 30 and 60% and 60 and 100% sequence identity. (B) The distributions of improved, equal and worsened cases compared with the basic PRALINE method for each E-value threshold. The ‘inc’ column is the PRALINE_PSI incremental strategy starting from a threshold of 10⁻⁶, and the ‘max’ column is PRALINE_PSI's theoretical upper limit for the tested threshold range.

Figure 5

Comparison of alignment methods on the 399 HOMSTRAD multiple alignments (CS score). The difference (Δ) between the average scores of each tested alignment method and that of the PRALINE_BASIC method is taken at 5% intervals. The PRALINE_PSI scores are for the incremental strategy starting with an E-value of 10⁻⁶.