extractAPAAC: Amphiphilic Pseudo Amino Acid Composition (APseAAC)...

In protr: Generating Various Numerical Representation Schemes for Protein Sequences

Amphiphilic Pseudo Amino Acid Composition (APseAAC) Descriptor

Description

This function calculates the Amphiphilic Pseudo Amino Acid Composition (APseAAC, or APAAC) descriptor (dim: 20 + (n * lambda), n is the number of properties selected, default is 80).

Usage

extractAPAAC(
  x,
  props = c("Hydrophobicity", "Hydrophilicity"),
  lambda = 30,
  w = 0.05,
  customprops = NULL
)

Arguments

x	A character vector, as the input protein sequence.
props	A character vector, specifying the properties used. 2 properties are used by default, as listed below: 'Hydrophobicity' Hydrophobicity value of the 20 amino acids 'Hydrophilicity' Hydrophilicity value of the 20 amino acids
lambda	The lambda parameter for the APAAC descriptors, default is 30.
w	The weighting factor, default is 0.05.
customprops	A n x 21 named data frame contains n customized property. Each row contains one property. The column order for different amino acid types is 'AccNo', 'A', 'R', 'N', 'D', 'C', 'E', 'Q', 'G', 'H', 'I', 'L', 'K', 'M', 'F', 'P', 'S', 'T', 'W', 'Y', 'V', and the columns should also be exactly named like this. The AccNo column contains the properties' names. Then users should explicitly specify these properties with these names in the argument props. See the examples below for a demonstration. The default value for customprops is NULL.

Value

A length 20 + n * lambda named vector, n is the number of properties selected.

Note

Note the default 20 * 2 prop values have already been independently given in the function. Users can also specify other (up to 544) properties with the Accession Number in the AAindex data, with or without the default three properties, which means users should explicitly specify the properties to use. For this descriptor type, users need to intelligently evaluate the underlying details of the descriptors provided, instead of using this function with their data blindly. It would be wise to use some negative and positive control comparisons where relevant to help guide interpretation of the results.

Author(s)

Nan Xiao <https://nanx.me>

References

Kuo-Chen Chou. Prediction of Protein Cellular Attributes Using Pseudo-Amino Acid Composition. PROTEINS: Structure, Function, and Genetics, 2001, 43: 246-255.

Kuo-Chen Chou. Using Amphiphilic Pseudo Amino Acid Composition to Predict Enzyme Subfamily Classes. Bioinformatics, 2005, 21, 10-19.

JACS, 1962, 84: 4240-4246. (C. Tanford). (The hydrophobicity data)

PNAS, 1981, 78:3824-3828 (T.P.Hopp & K.R.Woods). (The hydrophilicity data)

See Also

See extractPAAC for the pseudo amino acid composition (PseAAC) descriptor.

Examples

x <- readFASTA(system.file("protseq/P00750.fasta", package = "protr"))[[1]]
extractAPAAC(x)

myprops <- data.frame(
  AccNo = c("MyProp1", "MyProp2", "MyProp3"),
  A = c(0.62, -0.5, 15), R = c(-2.53, 3, 101),
  N = c(-0.78, 0.2, 58), D = c(-0.9, 3, 59),
  C = c(0.29, -1, 47), E = c(-0.74, 3, 73),
  Q = c(-0.85, 0.2, 72), G = c(0.48, 0, 1),
  H = c(-0.4, -0.5, 82), I = c(1.38, -1.8, 57),
  L = c(1.06, -1.8, 57), K = c(-1.5, 3, 73),
  M = c(0.64, -1.3, 75), F = c(1.19, -2.5, 91),
  P = c(0.12, 0, 42), S = c(-0.18, 0.3, 31),
  T = c(-0.05, -0.4, 45), W = c(0.81, -3.4, 130),
  Y = c(0.26, -2.3, 107), V = c(1.08, -1.5, 43)
)

# use 2 default properties, 4 properties from the
# AAindex database, and 3 cutomized properties
extractAPAAC(
  x,
  customprops = myprops,
  props = c(
    "Hydrophobicity", "Hydrophilicity",
    "CIDH920105", "BHAR880101",
    "CHAM820101", "CHAM820102",
    "MyProp1", "MyProp2", "MyProp3"
  )
)

protr documentation built on Sept. 12, 2024, 6:44 a.m.