Motivation:
A well-known weakness of many topolgy prediction algorithms is that they often
confuse transmembrane regions with signal peptides, as both these types of regions
in general consist of a stretch of mainly hydrophobic residues.
When applied to whole genome data, it is important for a topology predictor not to falsely
predict actual signal peptides as transmembrane regions as this may lead for example to
inaccurate functional assignments.
Functionality:
SPOCTOPUS extends the original topology prediction algorithm of OCTOPUS by also predicting signal peptides.
The SPOCTOPUS algorithm is a pre-processor to OCTOPUS and uses a neural network to predict a signal peptide
preference score for each of the 70 most N-terminal residues. If these scores are high enough a signal peptide
is predicted.
Its exact location is determined by a hidden Markov model before the remainder of the topology
prediction is performed by OCTOPUS.
The final output contains information if a residues is predicted to be
N-terminal of a signal peptide (n), in a signal peptide (S), in a transmembrane (M), interface (I), close loop (L) or
globular loop (G) environment and the preference for each residue to
be on the inside (i) or outside (o) if the membrane.
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