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 21named data frame containsncustomize 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. TheAccNocolumn contains the properties' names. Then users should explicitly specify these properties with these names in the argumentprops. See the examples below for a demonstration. The default value forcustompropsisNULL.
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)
#> CIDH920105.lag1 CIDH920105.lag2 CIDH920105.lag3 CIDH920105.lag4
#> 0.9361830 1.0442920 1.0452843 1.0563467
#> CIDH920105.lag5 CIDH920105.lag6 CIDH920105.lag7 CIDH920105.lag8
#> 0.9406031 1.0765517 1.0675786 0.9991363
#> CIDH920105.lag9 CIDH920105.lag10 CIDH920105.lag11 CIDH920105.lag12
#> 1.0316555 0.9684585 0.9353130 1.0201990
#> CIDH920105.lag13 CIDH920105.lag14 CIDH920105.lag15 CIDH920105.lag16
#> 0.9340933 1.0207373 1.0251486 1.0290464
#> CIDH920105.lag17 CIDH920105.lag18 CIDH920105.lag19 CIDH920105.lag20
#> 1.0414375 0.9494403 0.9905987 0.9987183
#> CIDH920105.lag21 CIDH920105.lag22 CIDH920105.lag23 CIDH920105.lag24
#> 0.9472542 0.9010009 0.9828848 1.0574098
#> CIDH920105.lag25 CIDH920105.lag26 CIDH920105.lag27 CIDH920105.lag28
#> 0.9897955 1.0290018 0.9400066 1.0584150
#> CIDH920105.lag29 CIDH920105.lag30 BHAR880101.lag1 BHAR880101.lag2
#> 0.9762904 1.0029734 0.9818711 1.0051730
#> BHAR880101.lag3 BHAR880101.lag4 BHAR880101.lag5 BHAR880101.lag6
#> 0.9967069 1.1012905 0.9595859 1.1337056
#> BHAR880101.lag7 BHAR880101.lag8 BHAR880101.lag9 BHAR880101.lag10
#> 1.0628740 0.9497400 1.0338201 0.9023549
#> BHAR880101.lag11 BHAR880101.lag12 BHAR880101.lag13 BHAR880101.lag14
#> 0.9802386 1.0151581 0.9292786 1.0066693
#> BHAR880101.lag15 BHAR880101.lag16 BHAR880101.lag17 BHAR880101.lag18
#> 0.9856150 0.9759971 1.0232794 1.0687209
#> BHAR880101.lag19 BHAR880101.lag20 BHAR880101.lag21 BHAR880101.lag22
#> 1.0491804 1.0351528 1.0232000 0.9016895
#> BHAR880101.lag23 BHAR880101.lag24 BHAR880101.lag25 BHAR880101.lag26
#> 1.0391900 1.0301878 1.0021211 1.0222207
#> BHAR880101.lag27 BHAR880101.lag28 BHAR880101.lag29 BHAR880101.lag30
#> 0.9260382 1.0403725 0.9530778 0.9884374
#> CHAM820101.lag1 CHAM820101.lag2 CHAM820101.lag3 CHAM820101.lag4
#> 1.0133839 1.0022438 1.0551890 1.0169637
#> CHAM820101.lag5 CHAM820101.lag6 CHAM820101.lag7 CHAM820101.lag8
#> 0.9413192 0.9730229 0.9865679 0.9428985
#> CHAM820101.lag9 CHAM820101.lag10 CHAM820101.lag11 CHAM820101.lag12
#> 0.9521705 1.0440834 0.9744226 0.9842079
#> CHAM820101.lag13 CHAM820101.lag14 CHAM820101.lag15 CHAM820101.lag16
#> 0.9375430 0.9847534 0.9762810 1.0337143
#> CHAM820101.lag17 CHAM820101.lag18 CHAM820101.lag19 CHAM820101.lag20
#> 1.0637944 0.9600194 1.0264610 0.9730978
#> CHAM820101.lag21 CHAM820101.lag22 CHAM820101.lag23 CHAM820101.lag24
#> 0.9841629 0.8944897 1.0012565 1.0407044
#> CHAM820101.lag25 CHAM820101.lag26 CHAM820101.lag27 CHAM820101.lag28
#> 0.9774393 0.9556452 0.9771782 0.9855390
#> CHAM820101.lag29 CHAM820101.lag30 CHAM820102.lag1 CHAM820102.lag2
#> 1.0378770 0.9705165 0.9503346 1.0343446
#> CHAM820102.lag3 CHAM820102.lag4 CHAM820102.lag5 CHAM820102.lag6
#> 1.0930293 1.0805000 0.9861535 0.9921047
#> CHAM820102.lag7 CHAM820102.lag8 CHAM820102.lag9 CHAM820102.lag10
#> 1.0083331 0.9676778 1.0545499 0.9671392
#> CHAM820102.lag11 CHAM820102.lag12 CHAM820102.lag13 CHAM820102.lag14
#> 1.0000784 0.9749532 0.9242556 1.0615438
#> CHAM820102.lag15 CHAM820102.lag16 CHAM820102.lag17 CHAM820102.lag18
#> 1.0210459 1.0068944 0.9859525 0.9524320
#> CHAM820102.lag19 CHAM820102.lag20 CHAM820102.lag21 CHAM820102.lag22
#> 1.0254189 1.0110984 1.0153212 1.0091916
#> CHAM820102.lag23 CHAM820102.lag24 CHAM820102.lag25 CHAM820102.lag26
#> 1.0471259 1.0112268 0.9833002 1.0031331
#> CHAM820102.lag27 CHAM820102.lag28 CHAM820102.lag29 CHAM820102.lag30
#> 0.9958332 1.0151695 0.9763861 0.9332919
#> CHOC760101.lag1 CHOC760101.lag2 CHOC760101.lag3 CHOC760101.lag4
#> 1.0150406 0.9729159 1.0498232 0.9944486
#> CHOC760101.lag5 CHOC760101.lag6 CHOC760101.lag7 CHOC760101.lag8
#> 0.9503834 0.9408494 0.9576772 0.9247629
#> CHOC760101.lag9 CHOC760101.lag10 CHOC760101.lag11 CHOC760101.lag12
#> 0.9614165 1.0452973 1.0171362 0.9890922
#> CHOC760101.lag13 CHOC760101.lag14 CHOC760101.lag15 CHOC760101.lag16
#> 0.9510173 0.9532281 0.9770492 1.0067054
#> CHOC760101.lag17 CHOC760101.lag18 CHOC760101.lag19 CHOC760101.lag20
#> 1.0677338 0.9598050 1.0069556 1.0038437
#> CHOC760101.lag21 CHOC760101.lag22 CHOC760101.lag23 CHOC760101.lag24
#> 0.9524803 0.9014090 0.9852450 1.0379942
#> CHOC760101.lag25 CHOC760101.lag26 CHOC760101.lag27 CHOC760101.lag28
#> 1.0006621 0.9339269 0.9963756 1.0052963
#> CHOC760101.lag29 CHOC760101.lag30 BIGC670101.lag1 BIGC670101.lag2
#> 1.0413666 0.9467525 1.0059350 0.9929376
#> BIGC670101.lag3 BIGC670101.lag4 BIGC670101.lag5 BIGC670101.lag6
#> 1.0731913 1.0077837 0.9192554 0.9672734
#> BIGC670101.lag7 BIGC670101.lag8 BIGC670101.lag9 BIGC670101.lag10
#> 0.9461109 0.9529949 0.9730305 1.0427910
#> BIGC670101.lag11 BIGC670101.lag12 BIGC670101.lag13 BIGC670101.lag14
#> 1.0039312 0.9984156 0.9450803 0.9733642
#> BIGC670101.lag15 BIGC670101.lag16 BIGC670101.lag17 BIGC670101.lag18
#> 0.9915806 1.0137285 1.0702942 0.9415800
#> BIGC670101.lag19 BIGC670101.lag20 BIGC670101.lag21 BIGC670101.lag22
#> 1.0139971 0.9625814 0.9565350 0.8898701
#> BIGC670101.lag23 BIGC670101.lag24 BIGC670101.lag25 BIGC670101.lag26
#> 0.9905643 1.0442658 0.9891278 0.9570413
#> BIGC670101.lag27 BIGC670101.lag28 BIGC670101.lag29 BIGC670101.lag30
#> 0.9987817 1.0008018 1.0376577 0.9536494
#> CHAM810101.lag1 CHAM810101.lag2 CHAM810101.lag3 CHAM810101.lag4
#> 0.9927308 0.9615010 1.0668733 1.0353024
#> CHAM810101.lag5 CHAM810101.lag6 CHAM810101.lag7 CHAM810101.lag8
#> 0.9916586 0.9749523 0.9871645 0.9444802
#> CHAM810101.lag9 CHAM810101.lag10 CHAM810101.lag11 CHAM810101.lag12
#> 1.0291907 1.0194290 1.0607408 0.9999449
#> CHAM810101.lag13 CHAM810101.lag14 CHAM810101.lag15 CHAM810101.lag16
#> 0.9931695 0.9297656 0.9611833 0.9814565
#> CHAM810101.lag17 CHAM810101.lag18 CHAM810101.lag19 CHAM810101.lag20
#> 1.0321136 0.9892586 1.0563039 1.0206550
#> CHAM810101.lag21 CHAM810101.lag22 CHAM810101.lag23 CHAM810101.lag24
#> 0.9041110 0.9103649 0.9775755 1.0068787
#> CHAM810101.lag25 CHAM810101.lag26 CHAM810101.lag27 CHAM810101.lag28
#> 1.0079916 0.9596887 1.0191264 1.0140882
#> CHAM810101.lag29 CHAM810101.lag30 DAYM780201.lag1 DAYM780201.lag2
#> 0.9151374 0.9253788 0.9646010 1.0522299
#> DAYM780201.lag3 DAYM780201.lag4 DAYM780201.lag5 DAYM780201.lag6
#> 1.0335338 1.1041881 0.9693172 1.0428607
#> DAYM780201.lag7 DAYM780201.lag8 DAYM780201.lag9 DAYM780201.lag10
#> 1.1097225 0.9169442 0.9054205 0.9649199
#> DAYM780201.lag11 DAYM780201.lag12 DAYM780201.lag13 DAYM780201.lag14
#> 1.0263117 1.0189425 0.9578808 1.0286703
#> DAYM780201.lag15 DAYM780201.lag16 DAYM780201.lag17 DAYM780201.lag18
#> 1.0104818 1.0764483 0.9954050 0.9180787
#> DAYM780201.lag19 DAYM780201.lag20 DAYM780201.lag21 DAYM780201.lag22
#> 0.9481601 1.0128404 0.9719377 0.9898303
#> DAYM780201.lag23 DAYM780201.lag24 DAYM780201.lag25 DAYM780201.lag26
#> 0.9977120 0.9509454 1.0878960 1.0429411
#> DAYM780201.lag27 DAYM780201.lag28 DAYM780201.lag29 DAYM780201.lag30
#> 0.9938437 0.9506562 0.9532393 1.0463685
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'))
#> CIDH920105.lag1 CIDH920105.lag2 CIDH920105.lag3 CIDH920105.lag4
#> 0.9361830 1.0442920 1.0452843 1.0563467
#> CIDH920105.lag5 CIDH920105.lag6 CIDH920105.lag7 CIDH920105.lag8
#> 0.9406031 1.0765517 1.0675786 0.9991363
#> CIDH920105.lag9 CIDH920105.lag10 CIDH920105.lag11 CIDH920105.lag12
#> 1.0316555 0.9684585 0.9353130 1.0201990
#> CIDH920105.lag13 CIDH920105.lag14 CIDH920105.lag15 CIDH920105.lag16
#> 0.9340933 1.0207373 1.0251486 1.0290464
#> CIDH920105.lag17 CIDH920105.lag18 CIDH920105.lag19 CIDH920105.lag20
#> 1.0414375 0.9494403 0.9905987 0.9987183
#> CIDH920105.lag21 CIDH920105.lag22 CIDH920105.lag23 CIDH920105.lag24
#> 0.9472542 0.9010009 0.9828848 1.0574098
#> CIDH920105.lag25 CIDH920105.lag26 CIDH920105.lag27 CIDH920105.lag28
#> 0.9897955 1.0290018 0.9400066 1.0584150
#> CIDH920105.lag29 CIDH920105.lag30 BHAR880101.lag1 BHAR880101.lag2
#> 0.9762904 1.0029734 0.9818711 1.0051730
#> BHAR880101.lag3 BHAR880101.lag4 BHAR880101.lag5 BHAR880101.lag6
#> 0.9967069 1.1012905 0.9595859 1.1337056
#> BHAR880101.lag7 BHAR880101.lag8 BHAR880101.lag9 BHAR880101.lag10
#> 1.0628740 0.9497400 1.0338201 0.9023549
#> BHAR880101.lag11 BHAR880101.lag12 BHAR880101.lag13 BHAR880101.lag14
#> 0.9802386 1.0151581 0.9292786 1.0066693
#> BHAR880101.lag15 BHAR880101.lag16 BHAR880101.lag17 BHAR880101.lag18
#> 0.9856150 0.9759971 1.0232794 1.0687209
#> BHAR880101.lag19 BHAR880101.lag20 BHAR880101.lag21 BHAR880101.lag22
#> 1.0491804 1.0351528 1.0232000 0.9016895
#> BHAR880101.lag23 BHAR880101.lag24 BHAR880101.lag25 BHAR880101.lag26
#> 1.0391900 1.0301878 1.0021211 1.0222207
#> BHAR880101.lag27 BHAR880101.lag28 BHAR880101.lag29 BHAR880101.lag30
#> 0.9260382 1.0403725 0.9530778 0.9884374
#> CHAM820101.lag1 CHAM820101.lag2 CHAM820101.lag3 CHAM820101.lag4
#> 1.0133839 1.0022438 1.0551890 1.0169637
#> CHAM820101.lag5 CHAM820101.lag6 CHAM820101.lag7 CHAM820101.lag8
#> 0.9413192 0.9730229 0.9865679 0.9428985
#> CHAM820101.lag9 CHAM820101.lag10 CHAM820101.lag11 CHAM820101.lag12
#> 0.9521705 1.0440834 0.9744226 0.9842079
#> CHAM820101.lag13 CHAM820101.lag14 CHAM820101.lag15 CHAM820101.lag16
#> 0.9375430 0.9847534 0.9762810 1.0337143
#> CHAM820101.lag17 CHAM820101.lag18 CHAM820101.lag19 CHAM820101.lag20
#> 1.0637944 0.9600194 1.0264610 0.9730978
#> CHAM820101.lag21 CHAM820101.lag22 CHAM820101.lag23 CHAM820101.lag24
#> 0.9841629 0.8944897 1.0012565 1.0407044
#> CHAM820101.lag25 CHAM820101.lag26 CHAM820101.lag27 CHAM820101.lag28
#> 0.9774393 0.9556452 0.9771782 0.9855390
#> CHAM820101.lag29 CHAM820101.lag30 CHAM820102.lag1 CHAM820102.lag2
#> 1.0378770 0.9705165 0.9503346 1.0343446
#> CHAM820102.lag3 CHAM820102.lag4 CHAM820102.lag5 CHAM820102.lag6
#> 1.0930293 1.0805000 0.9861535 0.9921047
#> CHAM820102.lag7 CHAM820102.lag8 CHAM820102.lag9 CHAM820102.lag10
#> 1.0083331 0.9676778 1.0545499 0.9671392
#> CHAM820102.lag11 CHAM820102.lag12 CHAM820102.lag13 CHAM820102.lag14
#> 1.0000784 0.9749532 0.9242556 1.0615438
#> CHAM820102.lag15 CHAM820102.lag16 CHAM820102.lag17 CHAM820102.lag18
#> 1.0210459 1.0068944 0.9859525 0.9524320
#> CHAM820102.lag19 CHAM820102.lag20 CHAM820102.lag21 CHAM820102.lag22
#> 1.0254189 1.0110984 1.0153212 1.0091916
#> CHAM820102.lag23 CHAM820102.lag24 CHAM820102.lag25 CHAM820102.lag26
#> 1.0471259 1.0112268 0.9833002 1.0031331
#> CHAM820102.lag27 CHAM820102.lag28 CHAM820102.lag29 CHAM820102.lag30
#> 0.9958332 1.0151695 0.9763861 0.9332919
#> MyProp1.lag1 MyProp1.lag2 MyProp1.lag3 MyProp1.lag4
#> 0.9987014 1.0042430 0.9768573 1.0215035
#> MyProp1.lag5 MyProp1.lag6 MyProp1.lag7 MyProp1.lag8
#> 0.9654430 1.0173820 0.9899070 1.0259654
#> MyProp1.lag9 MyProp1.lag10 MyProp1.lag11 MyProp1.lag12
#> 1.0470257 0.9354884 0.9901335 1.0102046
#> MyProp1.lag13 MyProp1.lag14 MyProp1.lag15 MyProp1.lag16
#> 0.9604603 0.9777933 0.9986361 0.9935197
#> MyProp1.lag17 MyProp1.lag18 MyProp1.lag19 MyProp1.lag20
#> 0.9863639 1.0013734 0.9944359 1.0021568
#> MyProp1.lag21 MyProp1.lag22 MyProp1.lag23 MyProp1.lag24
#> 1.0101742 0.9369555 0.9493721 1.0463017
#> MyProp1.lag25 MyProp1.lag26 MyProp1.lag27 MyProp1.lag28
#> 1.0762954 0.9830056 0.9012154 1.0225609
#> MyProp1.lag29 MyProp1.lag30 MyProp2.lag1 MyProp2.lag2
#> 0.9707321 0.9912091 0.9900593 1.0132171
#> MyProp2.lag3 MyProp2.lag4 MyProp2.lag5 MyProp2.lag6
#> 0.9939543 1.0220681 0.9749927 1.1142163
#> MyProp2.lag7 MyProp2.lag8 MyProp2.lag9 MyProp2.lag10
#> 0.9886743 1.0465832 1.0369295 0.9221438
#> MyProp2.lag11 MyProp2.lag12 MyProp2.lag13 MyProp2.lag14
#> 0.9835647 1.0490332 0.9073427 1.0270654
#> MyProp2.lag15 MyProp2.lag16 MyProp2.lag17 MyProp2.lag18
#> 1.0853336 0.9506871 0.9516677 0.9625250
#> MyProp2.lag19 MyProp2.lag20 MyProp2.lag21 MyProp2.lag22
#> 1.0281791 0.9887198 0.9739604 0.9363962
#> MyProp2.lag23 MyProp2.lag24 MyProp2.lag25 MyProp2.lag26
#> 0.9529674 1.0334644 1.0674287 1.0375601
#> MyProp2.lag27 MyProp2.lag28 MyProp2.lag29 MyProp2.lag30
#> 0.9084307 1.0695042 0.9657679 0.9768113
#> MyProp3.lag1 MyProp3.lag2 MyProp3.lag3 MyProp3.lag4
#> 1.0146280 0.9722862 1.0497983 1.0080631
#> MyProp3.lag5 MyProp3.lag6 MyProp3.lag7 MyProp3.lag8
#> 0.9823002 0.9406788 0.9923895 0.9274328
#> MyProp3.lag9 MyProp3.lag10 MyProp3.lag11 MyProp3.lag12
#> 0.9692848 1.0514912 1.0026759 0.9732718
#> MyProp3.lag13 MyProp3.lag14 MyProp3.lag15 MyProp3.lag16
#> 0.9387388 0.9636088 0.9700880 1.0131516
#> MyProp3.lag17 MyProp3.lag18 MyProp3.lag19 MyProp3.lag20
#> 1.0611696 0.9700136 1.0189479 0.9968361
#> MyProp3.lag21 MyProp3.lag22 MyProp3.lag23 MyProp3.lag24
#> 0.9737338 0.9262255 1.0027198 1.0388112
#> MyProp3.lag25 MyProp3.lag26 MyProp3.lag27 MyProp3.lag28
#> 0.9966404 0.9222564 0.9889491 1.0064019
#> MyProp3.lag29 MyProp3.lag30
#> 1.0376997 0.9617269