Function for predicting the oligomerization of one or multiple coiled coil segments
The model to be considered; can either be one of the
models included in the package (
One or several amino acid sequences; valid
characters are all uppercase letters except ‘B’,
‘J’, ‘O’, ‘U’, ‘X’, and
‘Z’; invalid characters are tolerated, but ignored
by the prediction. This argument can be a character vector,
a character vector containing the heptad register(s); valid characters are the lowercase letters ‘a’-‘g’ and dashes ‘-’. Can also be omitted, see details below.
predict is the most important one in the
procoil package. It is used to apply a coiled coil
prediction model to coiled coil sequences/segments. It uses the
discriminant function described in
By default the final classification is computed on the basis of
the discriminant function value f(x). If f(x)>=0,
the sequence x is predicted as trimer, otherwise as dimer.
reg argument is missing,
looks whether the object passed as argument
includes heptad register information, either as an attribute
seq is a character vector), as
seq is an
object), or via annotation metadata (if
In any case, the
reg argument has priority over all other
ways of specifying the heptad annotation. In other words,
reg is specified and
seq contains heptad
annotations in one of the ways described above, the
reg argument has priority and the heptad annotation in
seq is ignored.
reg argument must have exactly as many elements
seq has sequences, and the registers must be
aligned to the sequences, i.e. the first register must be
exactly as long as the first sequence, and so on.
If heptad registers contain dashes, the
function extracts all contiguous coiled coil segments and computes
predictions for all of them. The returned
CCProfile object then contains
profiles/predictions of all coiled coil segments that were
seq (see example below).
Ulrich Bodenhofer [email protected]
Mahrenholz, C.C., Abfalter, I.G., Bodenhofer, U., Volkmer, R., and Hochreiter, S. (2011) Complex networks govern coiled coil oligomerization - predicting and profiling by means of a machine learning approach. Mol. Cell. Proteomics 10(5):M110.004994. DOI: 10.1074/mcp.M110.004994
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## predict oligomerization of GCN4 wildtype GCN4wt <- predict(PrOCoilModel, "MKQLEDKVEELLSKNYHLENEVARLKKLV", "abcdefgabcdefgabcdefgabcdefga") ## show result GCN4wt ## example with four GCN4 mutations GCN4mSeq <- c("GCN4wt" ="MKQLEDKVEELLSKNYHLENEVARLKKLV", "GCN4_N16Y_L19T"="MKQLEDKVEELLSKYYHTENEVARLKKLV", "GCN4_E22R_K27E"="MKQLEDKVEELLSKNYHLENRVARLEKLV", "GCN4_V23K_K27E"="MKQLEDKVEELLSKNYHLENEKARLEKLV") ## to illustrate the alternative interface, we convert this ## character vector to an 'AAStringSet' object and add ## heptad registers as annotation metadata GCN4mAA <- AAStringSet(GCN4mSeq) annotationMetadata(GCN4mAA, annCharset="abcdefg") <- rep("abcdefgabcdefgabcdefgabcdefga", 4) ## predict oligomerization (note: no 'reg' argument!) GCN4mut <- predict(PrOCoilModel, GCN4mAA) ## display summary of result GCN4mut ## predict oligomerization of unknown sequence (Marcoil example) MarcoilEx <- predict(PrOCoilModel, "MGECDQLLVFMITSRVLVLSTLIIMDSRQVYLENLRQFAENLRQNIENVHSFLENLRADLENLRQKFPGKWYSAMPGRHG", "-------------------------------abcdefgabcdefgabcdefgabcdefgabcdefg--------------") ## show results MarcoilEx
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