extractProtPAAC: Pseudo Amino Acid Composition Descriptor

In nanxstats/Rcpi: Molecular Informatics Toolkit for Compound-Protein Interaction in Drug Discovery

Pseudo Amino Acid Composition Descriptor

Description

Pseudo Amino Acid Composition Descriptor

Usage

extractProtPAAC(
  x,
  props = c("Hydrophobicity", "Hydrophilicity", "SideChainMass"),
  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. 3 properties are used by default, as listed below: 'Hydrophobicity' Hydrophobicity value of the 20 amino acids 'Hydrophilicity' Hydrophilicity value of the 20 amino acids 'SideChainMass' Side-chain mass of the 20 amino acids
lambda	The lambda parameter for the PAAC 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.

Details

This function calculates the Pseudo Amino Acid Composition (PAAC) descriptor (Dim: 20 + lambda, default is 50).

Value

A length 20 + lambda named vector

Note

Note the default 20 * 3 prop values have been already independently given in the function. Users could 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.

References

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

Type 1 pseudo amino acid composition. http://www.csbio.sjtu.edu.cn/bioinf/PseAAC/type1.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)

CRC Handbook of Chemistry and Physics, 66th ed., CRC Press, Boca Raton, Florida (1985). (The side-chain mass data)

R.M.C. Dawson, D.C. Elliott, W.H. Elliott, K.M. Jones, Data for Biochemical Research 3rd ed., Clarendon Press Oxford (1986). (The side-chain mass data)

See Also

See extractProtAPAAC for amphiphilic pseudo amino acid composition descriptor.

Examples

x = readFASTA(system.file('protseq/P00750.fasta', package = 'Rcpi'))[[1]]
extractProtPAAC(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 3 default properties, 4 properties in the AAindex database,
# and 3 cutomized properties
extractProtPAAC(x, customprops = myprops,
                props = c('Hydrophobicity', 'Hydrophilicity', 'SideChainMass',
                          'CIDH920105', 'BHAR880101',
                          'CHAM820101', 'CHAM820102',
                          'MyProp1', 'MyProp2', 'MyProp3'))

nanxstats/Rcpi documentation built on July 6, 2023, 9:57 a.m.