Calcium-Binding Protein Protocols Calcium-Binding Protein Protocols

Multiple Sequence Alignment 237
the direction and origin of evolution. Some background information on phylogenetic
analysis is given in Note 6.
4. Notes
4.1. Methodology Notes
1. In general, two basic classes of alignment programs have been developed: global
and local methods. Global alignment programs attempt to align the sequences
over their whole length, whereas local programs search only for the most conserved
regions and leave the other parts of the sequences unaligned. The most
effective alignment algorithm depends on the nature of the sequences to be
aligned. Global algorithms produce the most accurate and reliable alignments
when all the sequences in the data set are of similar length. However, when the
sequences differ greatly in length, local alignment programs are often more successful
at identifying the conserved regions.
The two most-explored computational techniques for multiple-sequence alignment
are Dynamic Programming (DP) (39,40) and, more recently, Hidden
Markov Modeling (HMM) (41,42). The DP technique guarantees the finding of
the highest scoring alignment determined from summing amino acid substitution
scores minus any insertion/deletion penalties. HMM is a statistical approach,
which is powerful if applied to sequence database searches. However, HMM
approaches for multiple sequence alignment generally perform poorly when compared
to other methods (43,44), mainly because of the inherently complex
parameterization of the technique. As a consequence, the state-of-the-art multiple
alignment methods are all based on the DP technique.
Some recent evaluations of available multiple alignment techniques have been
carried out (45) using a versatile database of benchmark alignments called
BAliBASE (45). These showed the method PRRP (46) to be marginally the most
accurate, closely followed by CLUSTALX, which is a much faster program. Virtually
the same accuracy as CLUSTALX was attained by the PRALINE method
when run on default parameters, not utilizing strategies such as profile-preprocessing
or predicted secondary structure induced alignment. Other methods
included in the assessment tests generally fell behind, such as the local alignment
method DIALIGN (47), the HMM-based method HMMT (48), or the Gibbs-sampling
method GIBBS (49). It must be stressed that DIALIGN was relatively successful
in aligning sequence with very large insertions or deletions. A further
discussion of alignment strategies and associated methods can be found in
Appendix I.
2. SRS (Sequence Retrieval System) (23) is a powerful front-end program to access
a large number of popular sequence databases. In addition to sequences, one can
search based on motifs such as those within the PROSITE or PFAM databases.
Care must be taken, however, as the algorithms used to create these databases
may include false-positive results, and exclude false-negative ones. The
documentation must be read carefully to establish which sequences are included/
excluded (see Subheading 4.2. for addition practical considerations when using