Biology Reference
In-Depth Information
Fig. 8 Tree representation of alignment shown in Fig.
9
1.
Aligning distantly related protein sequences
. Although state-of-
the-art alignment methods are able to make very accurate
MSAs, inaccurate MSA can arise due to divergent evolution.
It has been shown that the accuracy of alignment methods
decreases dramatically when the sequence identity between
the aligned sequences is lower than 30 % [
16
]. Given this
limitation, it is advisable to compile a number of MSAs using
different amino acid substitution matrices (e.g., PAM and
BLOSUM matrices). It is helpful to know that higher PAM
numbers and low BLOSUM numbers (e.g., PAM250 or BLO-
SUM45) correspond to exchange matrices that are suited for
the alignment of more divergent sequences, respectively,
whereas matrices with lower PAM and higher BLOSUM num-
bers are more suitable for more closely related protein
sequences. It is also important to try different gap penalties
when aligning distant protein sequences. Gap penalties play an
important role in the dynamic programming algorithm; there-
fore they can have considerable influence on the alignment
quality. The higher the gap penalties, the stricter the insertion
of gaps into the alignment and consequently the fewer gaps
inserted. Gap regions in an MSA often correspond to loop
regions in the associated tertiary structure, which are more
likely to be altered by divergent evolution. Therefore, it can
be useful to lower the gap penalty values when aligning diver-
gent proteins, although care should be taken not to deviate too
much from the recommended settings. Excessive gap penalty
values will enforce a gap-less alignment, whereas low gap penal-
ties will lead to alignments with very many gaps, allowing
(near) identical amino acids to be matched. In both cases
the resulting alignment will be biologically inaccurate.
2.7
Practical Issues
