Biology Reference
In-Depth Information
function-based method adopted in ref.
21
. These schemes are
respectively called PicXAA-PHMM, PicXAA-SPHMM, and
PicXAA-PF. Detailed description of each posterior probability
computation scheme can be found in ref.
20
.
2. PicXAA-PF and PicXAA-PHMM have comparable computa-
tional cost, which is considerably lower than that of PicXAA-
SPHMM. The increased computational cost of PicXAA-
SPHMM mainly arises from its computationally intensive proba-
bility estimation step that uses a complicated structural pair-
HMM.
3. PicXAA-PF and PicXAA-PHMM can be used for aligning both
protein sequences as well as nucleotide sequences, while
PicXAA-SPHMM can be only used for multiple protein
sequence alignment.
4. Although the main focus of PicXAA lies in effectively capturing
the local similarities across sequences while predicting the
global alignment of multiple sequences, it consistently yields
accurate alignment results for various reference sets with
diverse characteristics. In fact, PicXAA can accurately predict
the alignment of sequences that belong to closely related
sequence families (thus bearing strong global similarities) as
well as those that belong to distant families (thus sharing only
local similarities).
5. For distantly related sequences that share local similarities that
are limited to relatively short subsequences, PicXAA has a clear
advantage over other progressive alignment techniques in
terms of alignment accuracy. This is a direct effect of the
probabilistic greedy alignment approach adopted by PicXAA,
which first builds up the MSA from sequence regions that can
be aligned with high confidence.
6. Typically, PicXAA-PF outperforms PicXAA-PHMM on many
datasets, while PicXAA-PHMM yields better alignment results
for locally similar sequences.
7. Incorporating structural similarities can be advantageous for
aligning protein sequences that share many structural simila-
rities in addition to sequence similarities. PicXAA-SPHMM
uses the SPHMM implemented in [
15
] to estimate the pairwise
residue alignment probabilities by incorporating such struc-
tural information. As a result, PicXAA-SPHMM often yields
improved alignment results for structurally similar proteins, but
at the price of increased computational overhead.
8. Parameters used in PicXAA:
(a)
Number of iterations for the probabilistic consistency trans-
formation (PCT)
: In general, increasing this parameter will
improve the consistency of the predicted alignment while
