Rcpi (version 1.8.0)

extractProtAPAAC: Amphiphilic Pseudo Amino Acid Composition Descriptor

Description

Amphiphilic Pseudo Amino Acid Composition Descriptor

Usage

extractProtAPAAC(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 customize 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.

Details

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

References

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

Type 2 pseudo amino acid composition. http://www.csbio.sjtu.edu.cn/bioinf/PseAAC/type2.htm

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 extractProtPAAC for pseudo amino acid composition descriptor.

Examples

x = readFASTA(system.file('protseq/P00750.fasta', package = 'Rcpi'))[[1]]
extractProtAPAAC(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 in the AAindex database,
# and 3 cutomized properties
extractProtAPAAC(x, customprops = myprops,
                 props = c('Hydrophobicity', 'Hydrophilicity',
                           'CIDH920105', 'BHAR880101',
                           'CHAM820101', 'CHAM820102',
                           'MyProp1', 'MyProp2', 'MyProp3'))