extractProtGeary: Geary Autocorrelation Descriptor

In road2stat/Rcpi: Molecular Informatics Toolkit for Compound-Protein Interaction in Drug Discovery

Geary Autocorrelation Descriptor

Description

Geary Autocorrelation Descriptor

Usage

extractProtGeary(
  x,
  props = c("CIDH920105", "BHAR880101", "CHAM820101", "CHAM820102", "CHOC760101",
    "BIGC670101", "CHAM810101", "DAYM780201"),
  nlag = 30L,
  customprops = NULL
)

Arguments

x	A character vector, as the input protein sequence.
props	A character vector, specifying the Accession Number of the target properties. 8 properties are used by default, as listed below: AccNo. CIDH920105 Normalized average hydrophobicity scales (Cid et al., 1992) AccNo. BHAR880101 Average flexibility indices (Bhaskaran-Ponnuswamy, 1988) AccNo. CHAM820101 Polarizability parameter (Charton-Charton, 1982) AccNo. CHAM820102 Free energy of solution in water, kcal/mole (Charton-Charton, 1982) AccNo. CHOC760101 Residue accessible surface area in tripeptide (Chothia, 1976) AccNo. BIGC670101 Residue volume (Bigelow, 1967) AccNo. CHAM810101 Steric parameter (Charton, 1981) AccNo. DAYM780201 Relative mutability (Dayhoff et al., 1978b)
nlag	Maximum value of the lag parameter. Default is 30.
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 Geary autocorrelation descriptor (Dim: length(props) * nlag).

Value

A length nlag named vector

References

AAindex: Amino acid index database. https://www.genome.jp/dbget/aaindex.html

Feng, Z.P. and Zhang, C.T. (2000) Prediction of membrane protein types based on the hydrophobic index of amino acids. Journal of Protein Chemistry, 19, 269-275.

Horne, D.S. (1988) Prediction of protein helix content from an autocorrelation analysis of sequence hydrophobicities. Biopolymers, 27, 451-477.

Sokal, R.R. and Thomson, B.A. (2006) Population structure inferred by local spatial autocorrelation: an usage from an Amerindian tribal population. American Journal of Physical Anthropology, 129, 121-131.

See Also

See extractProtMoreauBroto and extractProtMoran for Moreau-Broto autocorrelation descriptors and Moran autocorrelation descriptors.

Examples

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

road2stat/Rcpi documentation built on Sept. 17, 2024, 9:44 p.m.