Help for package protr

Version:

1.7-4

Title:

Generating Various Numerical Representation Schemes for Protein Sequences

Description:

Comprehensive toolkit for generating various numerical features of protein sequences described in Xiao et al. (2015) <doi:10.1093/bioinformatics/btv042>. For full functionality, the software 'ncbi-blast+' is needed, see https://blast.ncbi.nlm.nih.gov/doc/blast-help/downloadblastdata.html for more information.

License:

BSD_3_clause + file LICENSE

URL:

https://nanx.me/protr/, https://github.com/nanxstats/protr, http://protr.org

BugReports:

https://github.com/nanxstats/protr/issues

Encoding:

UTF-8

LazyData:

true

SystemRequirements:

ncbi-blast+ (see <https://blast.ncbi.nlm.nih.gov/doc/blast-help/downloadblastdata.html>)

VignetteBuilder:

knitr

Depends:

R (≥ 3.0.2)

Suggests:

knitr, rmarkdown, Biostrings, GOSemSim, foreach, doParallel, org.Hs.eg.db

RoxygenNote:

7.3.2

NeedsCompilation:

Packaged:

2024-09-10 23:07:28 UTC; nanx

Author:

Nan Xiao

[aut, cre], Qing-Song Xu [aut], Dong-Sheng Cao [aut], Sebastian Mueller [ctb] (company: Alva Genomics)

Maintainer:

Nan Xiao <me@nanx.me>

Repository:

CRAN

Date/Publication:

2024-09-11 23:00:05 UTC

protr: Generating Various Numerical Representation Schemes for Protein Sequences

Description

Comprehensive toolkit for generating various numerical features of protein sequences described in Xiao et al. (2015) doi:10.1093/bioinformatics/btv042. For full functionality, the software 'ncbi-blast+' is needed, see https://blast.ncbi.nlm.nih.gov/doc/blast-help/downloadblastdata.html for more information.

Author(s)

Maintainer: Nan Xiao me@nanx.me (ORCID)

Authors:

Qing-Song Xu qsxu@csu.edu.cn
Dong-Sheng Cao

Other contributors:

Sebastian Mueller (Alva Genomics) [contributor]

2D Autocorrelations Descriptors for 20 Amino Acids calculated by Dragon

Description

This dataset includes the 2D autocorrelations descriptors of the 20 amino acids calculated by Dragon (version 5.4) used for scales extraction in this package.

Examples

data(AA2DACOR)

3D-MoRSE Descriptors for 20 Amino Acids calculated by Dragon

Description

This dataset includes the 3D-MoRSE descriptors of the 20 amino acids calculated by Dragon (version 5.4) used for scales extraction in this package.

Examples

data(AA3DMoRSE)

Atom-Centred Fragments Descriptors for 20 Amino Acids calculated by Dragon

Description

This dataset includes the atom-centred fragments descriptors of the 20 amino acids calculated by Dragon (version 5.4) used for scales extraction in this package.

Examples

data(AAACF)

BLOSUM100 Matrix for 20 Amino Acids

Description

BLOSUM100 Matrix for the 20 amino acids. The matrix was extracted from the Biostrings package of Bioconductor.

Examples

data(AABLOSUM100)

BLOSUM45 Matrix for 20 Amino Acids

Description

BLOSUM45 Matrix for the 20 amino acids. The matrix was extracted from the Biostrings package of Bioconductor.

Examples

data(AABLOSUM45)

BLOSUM50 Matrix for 20 Amino Acids

Description

BLOSUM50 Matrix for the 20 amino acids. The matrix was extracted from the Biostrings package of Bioconductor.

Examples

data(AABLOSUM50)

BLOSUM62 Matrix for 20 Amino Acids

Description

BLOSUM62 Matrix for the 20 amino acids. The matrix was extracted from the Biostrings package of Bioconductor.

Examples

data(AABLOSUM62)

BLOSUM80 Matrix for 20 Amino Acids

Description

BLOSUM80 Matrix for the 20 amino acids. The matrix was extracted from the Biostrings package of Bioconductor.

Examples

data(AABLOSUM80)

Burden Eigenvalues Descriptors for 20 Amino Acids calculated by Dragon

Description

This dataset includes the Burden eigenvalues descriptors of the 20 amino acids calculated by Dragon (version 5.4) used for scales extraction in this package.

Examples

data(AABurden)

CPSA Descriptors for 20 Amino Acids calculated by Discovery Studio

Description

This dataset includes the CPSA descriptors of the 20 amino acids calculated by Discovery Studio (version 2.5) used for scales extraction in this package.

Details

All amino acid molecules had also been optimized with MOE 2011.10 (semiempirical AM1) before calculating these CPSA descriptors. The SDF file containing the information of the optimized amino acid molecules is included in this package. See OptAA3d for more information.

Examples

data(AACPSA)

Connectivity Indices Descriptors for 20 Amino Acids calculated by Dragon

Description

This dataset includes the connectivity indices descriptors of the 20 amino acids calculated by Dragon (version 5.4) used for scales extraction in this package.

Examples

data(AAConn)

Constitutional Descriptors for 20 Amino Acids calculated by Dragon

Description

This dataset includes the constitutional descriptors of the 20 amino acids calculated by Dragon (version 5.4) used for scales extraction in this package.

Examples

data(AAConst)

All 2D Descriptors for 20 Amino Acids calculated by Dragon

Description

This dataset includes all the 2D descriptors of the 20 amino acids calculated by Dragon (version 5.4) used for scales extraction in this package.

Examples

data(AADescAll)

Edge Adjacency Indices Descriptors for 20 Amino Acids calculated by Dragon

Description

This dataset includes the edge adjacency indices descriptors of the 20 amino acids calculated by Dragon (version 5.4) used for scales extraction in this package.

Examples

data(AAEdgeAdj)

Eigenvalue-Based Indices Descriptors for 20 Amino Acids calculated by Dragon

Description

This dataset includes the eigenvalue-based indices descriptors of the 20 amino acids calculated by Dragon (version 5.4) used for scales extraction in this package.

Examples

data(AAEigIdx)

Functional Group Counts Descriptors for 20 Amino Acids calculated by Dragon

Description

This dataset includes the functional group counts descriptors of the 20 amino acids calculated by Dragon (version 5.4) used for scales extraction in this package.

Examples

data(AAFGC)

GETAWAY Descriptors for 20 Amino Acids calculated by Dragon

Description

This dataset includes the GETAWAY descriptors of the 20 amino acids calculated by Dragon (version 5.4) used for scales extraction in this package.

Examples

data(AAGETAWAY)

Geometrical Descriptors for 20 Amino Acids calculated by Dragon

Description

This dataset includes the geometrical descriptors of the 20 amino acids calculated by Dragon (version 5.4) used for scales extraction in this package.

Examples

data(AAGeom)

Information Indices Descriptors for 20 Amino Acids calculated by Dragon

Description

This dataset includes the information indices descriptors of the 20 amino acids calculated by Dragon (version 5.4) used for scales extraction in this package.

Examples

data(AAInfo)

2D Descriptors for 20 Amino Acids calculated by MOE 2011.10

Description

This dataset includes the 2D descriptors of the 20 amino acids calculated by MOE 2011.10 used for scales extraction in this package.

Examples

data(AAMOE2D)

3D Descriptors for 20 Amino Acids calculated by MOE 2011.10

Description

This dataset includes the 3D descriptors of the 20 amino acids calculated by MOE 2011.10 used for scales extraction in this package. All amino acid molecules had also been optimized with MOE (semiempirical AM1) before calculating these 3D descriptors. The SDF file containing the information of the optimized amino acid molecules is included in this package. See OptAA3d for more information.

Examples

data(AAMOE3D)

Meta Information for the 20 Amino Acids

Description

This dataset includes the meta information of the 20 amino acids used for the 2D and 3D descriptor calculation in this package. Each column represents:

AAName: Amino acid name
Short: One-letter representation
Abbreviation: Three-letter representation
mol: SMILES representation
PUBCHEM_COMPOUND_CID: PubChem CID for the amino acid
PUBCHEM_LINK: PubChem link for the amino acid

Examples

data(AAMetaInfo)

Molecular Properties Descriptors for 20 Amino Acids calculated by Dragon

Description

This dataset includes the molecular properties descriptors of the 20 amino acids calculated by Dragon (version 5.4) used for scales extraction in this package.

Examples

data(AAMolProp)

PAM120 Matrix for 20 Amino Acids

Description

PAM120 Matrix for the 20 amino acids. The matrix was extracted from the Biostrings package of Bioconductor.

Examples

data(AAPAM120)

PAM250 Matrix for 20 Amino Acids

Description

PAM250 Matrix for the 20 amino acids. The matrix was extracted from the Biostrings package of Bioconductor.

Examples

data(AAPAM250)

PAM30 Matrix for 20 Amino Acids

Description

PAM30 Matrix for the 20 amino acids. The matrix was extracted from the Biostrings package of Bioconductor.

Examples

data(AAPAM30)

PAM40 Matrix for 20 Amino Acids

Description

PAM40 Matrix for the 20 amino acids. The matrix was extracted from the Biostrings package of Bioconductor.

Examples

data(AAPAM40)

PAM70 Matrix for 20 Amino Acids

Description

PAM70 Matrix for the 20 amino acids. The matrix was extracted from the Biostrings package of Bioconductor.

Examples

data(AAPAM70)

RDF Descriptors for 20 Amino Acids calculated by Dragon

Description

This dataset includes the RDF descriptors of the 20 amino acids calculated by Dragon (version 5.4) used for scales extraction in this package.

Examples

data(AARDF)

Randic Molecular Profiles Descriptors for 20 Amino Acids calculated by Dragon

Description

This dataset includes the Randic molecular profiles descriptors of the 20 amino acids calculated by Dragon (version 5.4) used for scales extraction in this package.

Examples

data(AARandic)

Topological Descriptors for 20 Amino Acids calculated by Dragon

Description

This dataset includes the topological descriptors of the 20 amino acids calculated by Dragon (version 5.4) used for scales extraction in this package.

Examples

data(AATopo)

Topological Charge Indices Descriptors for 20 Amino Acids calculated by Dragon

Description

This dataset includes the topological charge indices descriptors of the 20 amino acids calculated by Dragon (version 5.4) used for scales extraction in this package.

Examples

data(AATopoChg)

WHIM Descriptors for 20 Amino Acids calculated by Dragon

Description

This dataset includes the WHIM descriptors of the 20 amino acids calculated by Dragon (version 5.4) used for scales extraction in this package.

Examples

data(AAWHIM)

Walk and Path Counts Descriptors for 20 Amino Acids calculated by Dragon

Description

This dataset includes the walk and path counts descriptors of the 20 amino acids calculated by Dragon (version 5.4) used for scales extraction in this package.

Examples

data(AAWalk)

AAindex Data of 544 Physicochemical and Biological Properties for 20 Amino Acids

Description

The data was extracted from the AAindex1 database ver 9.1 (https://www.genome.jp/ftp/db/community/aaindex/old/ver9.1/aaindex1) as of November, 2012 (data last modified on 2006-08-14).

Details

With this dataset, users can investigate each property's accession number and other details. See https://www.genome.jp/aaindex/ for more information.

Examples

data(AAindex)

OptAA3d.sdf - 20 Amino Acids Optimized with MOE 2011.10 (Semiempirical AM1)

Description

OptAA3d.sdf - 20 Amino Acids Optimized with MOE 2011.10 (Semiempirical AM1)

Examples

# this operation requires the rcdk package
# require(rcdk)
# optaa3d = load.molecules(system.file("sysdata/OptAA3d.sdf", package = "protr"))
# view.molecule.2d(optaa3d[[1]])  # view the first AA

Auto Cross Covariance (ACC) for Generating Scales-Based Descriptors of the Same Length

Description

This function calculates the auto covariance and auto cross covariance for generating scale-based descriptors of the same length.

Usage

acc(mat, lag)

Arguments

mat

A p * n matrix. Each row represents one scale (total p scales), each column represents one amino acid position (total n amino acids).

lag

The lag parameter. Must be less than the amino acids.

Value

A length lag * p^2 named vector, the element names are constructed by: the scales index (crossed scales index) and lag index.

Note

Please see the references for details about auto cross covariance.

Author(s)

Nan Xiao <https://nanx.me>

References

Wold, S., Jonsson, J., Sjorstrom, M., Sandberg, M., & Rannar, S. (1993). DNA and peptide sequences and chemical processes multivariately modelled by principal component analysis and partial least-squares projections to latent structures. Analytica chimica acta, 277(2), 239–253.

Sjostrom, M., Rannar, S., & Wieslander, A. (1995). Polypeptide sequence property relationships in Escherichia coli based on auto cross covariances. Chemometrics and intelligent laboratory systems, 29(2), 295–305.

Examples

p <- 8 # p is the scales number
n <- 200 # n is the amino acid number
lag <- 7 # the lag paramter
mat <- matrix(rnorm(p * n), nrow = p, ncol = n)
acc(mat, lag)

Parallel Protein Sequence Similarity Calculation Between Two Sets Based on Sequence Alignment (In-Memory Version)

Description

Parallel calculation of protein sequence similarity based on sequence alignment between two sets of protein sequences.

Usage

crossSetSim(
  protlist1,
  protlist2,
  type = "local",
  cores = 2,
  batches = 1,
  verbose = FALSE,
  submat = "BLOSUM62",
  gap.opening = 10,
  gap.extension = 4
)

Arguments

protlist1

A length n list containing n protein sequences, each component of the list is a character string, storing one protein sequence. Unknown sequences should be represented as "".

protlist2

A length n list containing m protein sequences, each component of the list is a character string, storing one protein sequence. Unknown sequences should be represented as "".

type

Type of alignment, default is "local", can be "global" or "local", where "global" represents Needleman-Wunsch global alignment; "local" represents Smith-Waterman local alignment.

cores

Integer. The number of CPU cores to use for parallel execution, default is 2. Users can use the availableCores() function in the parallelly package to see how many cores they could use.

batches

Integer. How many batches should we split the similarity computations into. This is useful when you have a large number of protein sequences, enough number of CPU cores, but not enough RAM to compute and fit all the similarities into a single batch. Defaults to 1.

verbose

Print the computation progress? Useful when batches > 1.

submat

Substitution matrix, default is "BLOSUM62", can be one of "BLOSUM45", "BLOSUM50", "BLOSUM62", "BLOSUM80", "BLOSUM100", "PAM30", "PAM40", "PAM70", "PAM120", or "PAM250".

gap.opening

The cost required to open a gap of any length in the alignment. Defaults to 10.

gap.extension

The cost to extend the length of an existing gap by 1. Defaults to 4.

Value

A n x m similarity matrix.

Author(s)

Sebastian Mueller <https://alva-genomics.com>

Examples

## Not run: 

# Be careful when testing this since it involves parallelization
# and might produce unpredictable results in some environments

library("Biostrings")
library("foreach")
library("doParallel")

s1 <- readFASTA(system.file("protseq/P00750.fasta", package = "protr"))[[1]]
s2 <- readFASTA(system.file("protseq/P08218.fasta", package = "protr"))[[1]]
s3 <- readFASTA(system.file("protseq/P10323.fasta", package = "protr"))[[1]]
s4 <- readFASTA(system.file("protseq/P20160.fasta", package = "protr"))[[1]]
s5 <- readFASTA(system.file("protseq/Q9NZP8.fasta", package = "protr"))[[1]]

plist1 <- list(s1 = s1, s2 = s2, s4 = s4)
plist2 <- list(s3 = s3, s4_again = s4, s5 = s5, s1_again = s1)
psimmat <- crossSetSim(plist1, plist2)
colnames(psimmat) <- names(plist1)
rownames(psimmat) <- names(plist2)
print(psimmat)
#                 s1         s2         s4
# s3       0.10236985 0.18858241 0.05819984
# s4_again 0.04921696 0.12124217 1.00000000
# s5       0.03943488 0.06391103 0.05714638
# s1_again 1.00000000 0.11825938 0.04921696

## End(Not run)

Parallel Protein Sequence Similarity Calculation Between Two Sets Based on Sequence Alignment (Disk-Based Version)

Description

Parallel calculation of protein sequence similarity based on sequence alignment between two sets of protein sequences. This version offloads the partial results in each batch to the hard drive and merges the results together in the end, which reduces the memory usage.

Usage

crossSetSimDisk(
  protlist1,
  protlist2,
  cores = 2,
  batches = 1,
  path = tempdir(),
  verbose = FALSE,
  type = "local",
  submat = "BLOSUM62",
  gap.opening = 10,
  gap.extension = 4
)

Arguments

protlist1

A length n list containing n protein sequences, each component of the list is a character string, storing one protein sequence. Unknown sequences should be represented as "".

protlist2

A length n list containing m protein sequences, each component of the list is a character string, storing one protein sequence. Unknown sequences should be represented as "".

cores

Integer. The number of CPU cores to use for parallel execution, default is 2. Users can use the availableCores() function in the parallelly package to see how many cores they could use.

batches

Integer. How many batches should we split the pairwise similarity computations into. This is useful when you have a large number of protein sequences, enough number of CPU cores, but not enough RAM to compute and fit all the pairwise similarities into a single batch. Defaults to 1.

path

Directory for caching the results in each batch. Defaults to the temporary directory.

verbose

Print the computation progress? Useful when batches > 1.

type

Type of alignment, default is "local", can be "global" or "local", where "global" represents Needleman-Wunsch global alignment; "local" represents Smith-Waterman local alignment.

submat

Substitution matrix, default is "BLOSUM62", can be one of "BLOSUM45", "BLOSUM50", "BLOSUM62", "BLOSUM80", "BLOSUM100", "PAM30", "PAM40", "PAM70", "PAM120", or "PAM250".

gap.opening

The cost required to open a gap of any length in the alignment. Defaults to 10.

gap.extension

The cost to extend the length of an existing gap by 1. Defaults to 4.

Value

A n x m similarity matrix.

Author(s)

Nan Xiao <https://nanx.me>

Examples

## Not run: 

# Be careful when testing this since it involves parallelization
# and might produce unpredictable results in some environments

library("Biostrings")
library("foreach")
library("doParallel")

s1 <- readFASTA(system.file("protseq/P00750.fasta", package = "protr"))[[1]]
s2 <- readFASTA(system.file("protseq/P08218.fasta", package = "protr"))[[1]]
s3 <- readFASTA(system.file("protseq/P10323.fasta", package = "protr"))[[1]]
s4 <- readFASTA(system.file("protseq/P20160.fasta", package = "protr"))[[1]]
s5 <- readFASTA(system.file("protseq/Q9NZP8.fasta", package = "protr"))[[1]]

set.seed(1010)
plist1 <- as.list(c(s1, s2, s3, s4, s5)[sample(1:5, 100, replace = TRUE)])
plist2 <- as.list(c(s1, s2, s3, s4, s5)[sample(1:5, 100, replace = TRUE)])
psimmat <- crossSetSimDisk(
  plist1, plist2,
  cores = 2, batches = 10, verbose = TRUE,
  type = "local", submat = "BLOSUM62"
)

## End(Not run)

Amino Acid Composition Descriptor

Description

This function calculates the Amino Acid Composition descriptor (dim: 20).

Usage

extractAAC(x)

Arguments

x

A character vector, as the input protein sequence.

Value

A length 20 named vector

Author(s)

Nan Xiao <https://nanx.me>

References

M. Bhasin, G. P. S. Raghava. Classification of Nuclear Receptors Based on Amino Acid Composition and Dipeptide Composition. Journal of Biological Chemistry, 2004, 279, 23262.

Examples

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

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)

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"
  )
)

BLOSUM and PAM Matrix-Derived Descriptors

Description

This function calculates BLOSUM matrix-derived descriptors. For users' convenience, protr provides the BLOSUM45, BLOSUM50, BLOSUM62, BLOSUM80, BLOSUM100, PAM30, PAM40, PAM70, PAM120, and PAM250 matrices for the 20 amino acids to select from.

Usage

extractBLOSUM(x, submat = "AABLOSUM62", k, lag, scale = TRUE, silent = TRUE)

Arguments

x

A character vector, as the input protein sequence.

submat

Substitution matrix for the 20 amino acids. Should be one of AABLOSUM45, AABLOSUM50, AABLOSUM62, AABLOSUM80, AABLOSUM100, AAPAM30, AAPAM40, AAPAM70, AAPAM120, or AAPAM250. Default is "AABLOSUM62".

k

Integer. The number of selected scales (i.e. the first k scales) derived by the substitution matrix. This can be selected according to the printed relative importance values.

lag

The lag parameter. Must be less than the amino acids.

scale

Logical. Should we auto-scale the substitution matrix (submat) before doing eigen decomposition? Default is TRUE.

silent

Logical. Whether we print the relative importance of each scales (diagnal value of the eigen decomposition result matrix B) or not. Default is TRUE.

Value

A length lag * p^2 named vector, p is the number of scales selected.

Author(s)

Nan Xiao <https://nanx.me>

References

Georgiev, A. G. (2009). Interpretable numerical descriptors of amino acid space. Journal of Computational Biology, 16(5), 703–723.

Examples

x <- readFASTA(system.file("protseq/P00750.fasta", package = "protr"))[[1]]
blosum <- extractBLOSUM(x, submat = "AABLOSUM62", k = 5, lag = 7, scale = TRUE, silent = FALSE)

CTD Descriptors - Composition

Description

This function calculates the Composition descriptor of the CTD descriptors (dim: 21).

Usage

extractCTDC(x)

Arguments

x

A character vector, as the input protein sequence.

Value

A length 21 named vector

Note

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

Inna Dubchak, Ilya Muchink, Stephen R. Holbrook and Sung-Hou Kim. Prediction of protein folding class using global description of amino acid sequence. Proceedings of the National Academy of Sciences. USA, 1995, 92, 8700-8704.

Inna Dubchak, Ilya Muchink, Christopher Mayor, Igor Dralyuk and Sung-Hou Kim. Recognition of a Protein Fold in the Context of the SCOP classification. Proteins: Structure, Function and Genetics, 1999, 35, 401-407.

Examples

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

CTD Descriptors - Composition (with customized amino acid classification support)

Description

This function calculates the Composition descriptor of the CTD descriptors, with customized amino acid classification support.

Usage

extractCTDCClass(x, aagroup1, aagroup2, aagroup3)

Arguments

x

A character vector, as the input protein sequence.

aagroup1

A named list which contains the first group of customized amino acid classification. See example below.

aagroup2

A named list which contains the second group of customized amino acid classification. See example below.

aagroup3

A named list which contains the third group of customized amino acid classification. See example below.

Value

A length k * 3 named vector, k is the number of amino acid properties used.

Note

Author(s)

Nan Xiao <https://nanx.me>

References

Examples

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

# using five customized amino acid property classification
group1 <- list(
  "hydrophobicity" = c("R", "K", "E", "D", "Q", "N"),
  "normwaalsvolume" = c("G", "A", "S", "T", "P", "D", "C"),
  "polarizability" = c("G", "A", "S", "D", "T"),
  "secondarystruct" = c("E", "A", "L", "M", "Q", "K", "R", "H"),
  "solventaccess" = c("A", "L", "F", "C", "G", "I", "V", "W")
)

group2 <- list(
  "hydrophobicity" = c("G", "A", "S", "T", "P", "H", "Y"),
  "normwaalsvolume" = c("N", "V", "E", "Q", "I", "L"),
  "polarizability" = c("C", "P", "N", "V", "E", "Q", "I", "L"),
  "secondarystruct" = c("V", "I", "Y", "C", "W", "F", "T"),
  "solventaccess" = c("R", "K", "Q", "E", "N", "D")
)

group3 <- list(
  "hydrophobicity" = c("C", "L", "V", "I", "M", "F", "W"),
  "normwaalsvolume" = c("M", "H", "K", "F", "R", "Y", "W"),
  "polarizability" = c("K", "M", "H", "F", "R", "Y", "W"),
  "secondarystruct" = c("G", "N", "P", "S", "D"),
  "solventaccess" = c("M", "S", "P", "T", "H", "Y")
)

extractCTDCClass(x, aagroup1 = group1, aagroup2 = group2, aagroup3 = group3)

CTD Descriptors - Distribution

Description

This function calculates the Distribution descriptor of the CTD descriptors (dim: 105).

Usage

extractCTDD(x)

Arguments

x

A character vector, as the input protein sequence.

Value

A length 105 named vector

Note

Author(s)

Nan Xiao <https://nanx.me>

References

Examples

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

CTD Descriptors - Distribution (with customized amino acid classification support)

Description

This function calculates the Distribution descriptor of the CTD descriptors, with customized amino acid classification support.

Usage

extractCTDDClass(x, aagroup1, aagroup2, aagroup3)

Arguments

x

A character vector, as the input protein sequence.

aagroup1

A named list which contains the first group of customized amino acid classification. See example below.

aagroup2

A named list which contains the second group of customized amino acid classification. See example below.

aagroup3

A named list which contains the third group of customized amino acid classification. See example below.

Value

A length k * 15 named vector, k is the number of amino acid properties used.

Note

Author(s)

Nan Xiao <https://nanx.me>

References

Examples

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

# using five customized amino acid property classification
group1 <- list(
  "hydrophobicity" = c("R", "K", "E", "D", "Q", "N"),
  "normwaalsvolume" = c("G", "A", "S", "T", "P", "D", "C"),
  "polarizability" = c("G", "A", "S", "D", "T"),
  "secondarystruct" = c("E", "A", "L", "M", "Q", "K", "R", "H"),
  "solventaccess" = c("A", "L", "F", "C", "G", "I", "V", "W")
)

group2 <- list(
  "hydrophobicity" = c("G", "A", "S", "T", "P", "H", "Y"),
  "normwaalsvolume" = c("N", "V", "E", "Q", "I", "L"),
  "polarizability" = c("C", "P", "N", "V", "E", "Q", "I", "L"),
  "secondarystruct" = c("V", "I", "Y", "C", "W", "F", "T"),
  "solventaccess" = c("R", "K", "Q", "E", "N", "D")
)

group3 <- list(
  "hydrophobicity" = c("C", "L", "V", "I", "M", "F", "W"),
  "normwaalsvolume" = c("M", "H", "K", "F", "R", "Y", "W"),
  "polarizability" = c("K", "M", "H", "F", "R", "Y", "W"),
  "secondarystruct" = c("G", "N", "P", "S", "D"),
  "solventaccess" = c("M", "S", "P", "T", "H", "Y")
)

extractCTDDClass(x, aagroup1 = group1, aagroup2 = group2, aagroup3 = group3)

CTD Descriptors - Transition

Description

This function calculates the Transition descriptor of the CTD descriptors (dim: 21).

Usage

extractCTDT(x)

Arguments

x

A character vector, as the input protein sequence.

Value

A length 21 named vector

Note

Author(s)

Nan Xiao <https://nanx.me>

References

Examples

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

CTD Descriptors - Transition (with customized amino acid classification support)

Description

This function calculates the Transition descriptor of the CTD descriptors, with customized amino acid classification support.

Usage

extractCTDTClass(x, aagroup1, aagroup2, aagroup3)

Arguments

x

A character vector, as the input protein sequence.

aagroup1

A named list which contains the first group of customized amino acid classification. See example below.

aagroup2

A named list which contains the second group of customized amino acid classification. See example below.

aagroup3

A named list which contains the third group of customized amino acid classification. See example below.

Value

A length k * 3 named vector, k is the number of amino acid properties used.

Note

Author(s)

Nan Xiao <https://nanx.me>

References

Examples

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

# using five customized amino acid property classification
group1 <- list(
  "hydrophobicity" = c("R", "K", "E", "D", "Q", "N"),
  "normwaalsvolume" = c("G", "A", "S", "T", "P", "D", "C"),
  "polarizability" = c("G", "A", "S", "D", "T"),
  "secondarystruct" = c("E", "A", "L", "M", "Q", "K", "R", "H"),
  "solventaccess" = c("A", "L", "F", "C", "G", "I", "V", "W")
)

group2 <- list(
  "hydrophobicity" = c("G", "A", "S", "T", "P", "H", "Y"),
  "normwaalsvolume" = c("N", "V", "E", "Q", "I", "L"),
  "polarizability" = c("C", "P", "N", "V", "E", "Q", "I", "L"),
  "secondarystruct" = c("V", "I", "Y", "C", "W", "F", "T"),
  "solventaccess" = c("R", "K", "Q", "E", "N", "D")
)

group3 <- list(
  "hydrophobicity" = c("C", "L", "V", "I", "M", "F", "W"),
  "normwaalsvolume" = c("M", "H", "K", "F", "R", "Y", "W"),
  "polarizability" = c("K", "M", "H", "F", "R", "Y", "W"),
  "secondarystruct" = c("G", "N", "P", "S", "D"),
  "solventaccess" = c("M", "S", "P", "T", "H", "Y")
)

extractCTDTClass(x, aagroup1 = group1, aagroup2 = group2, aagroup3 = group3)

Conjoint Triad Descriptor

Description

This function calculates the Conjoint Triad descriptor (dim: 343).

Usage

extractCTriad(x)

Arguments

x

A character vector, as the input protein sequence.

Value

A length 343 named vector

Note

Author(s)

Nan Xiao <https://nanx.me>

References

J.W. Shen, J. Zhang, X.M. Luo, W.L. Zhu, K.Q. Yu, K.X. Chen, Y.X. Li, H.L. Jiang. Predicting Protein-protein Interactions Based Only on Sequences Information. Proceedings of the National Academy of Sciences. 007, 104, 4337–4341.

Examples

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

Conjoint Triad Descriptor (with customized amino acid classification support)

Description

This function calculates the Conjoint Triad descriptor, with customized amino acid classification support.

Usage

extractCTriadClass(x, aaclass)

Arguments

x

A character vector, as the input protein sequence.

aaclass

A list containing the customized amino acid classification. See example below.

Value

A length k^3 named vector, where k is the number of customized classes of the amino acids.

Note

Author(s)

Nan Xiao <https://nanx.me>

References

Examples

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

# use customized amino acid classification (normalized van der Waals volume)
newclass <- list(
  c("G", "A", "S", "T", "P", "D", "C"),
  c("N", "V", "E", "Q", "I", "L"),
  c("M", "H", "K", "F", "R", "Y", "W")
)

extractCTriadClass(x, aaclass = newclass)

Dipeptide Composition Descriptor

Description

This function calculates the Dipeptide Composition descriptor (dim: 400).

Usage

extractDC(x)

Arguments

x

A character vector, as the input protein sequence.

Value

A length 400 named vector

Author(s)

Nan Xiao <https://nanx.me>

References

M. Bhasin, G. P. S. Raghava. Classification of Nuclear Receptors Based on Amino Acid Composition and Dipeptide Composition. Journal of Biological Chemistry, 2004, 279, 23262.

Examples

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

Scales-Based Descriptors with 20+ classes of Molecular Descriptors

Description

This function calculates the scales-based descriptors with molecular descriptors sets calculated by Dragon, Discovery Studio and MOE. Users can specify which molecular descriptors to select from one of these deseriptor sets by specify the numerical or character index of the molecular descriptors in the descriptor set.

Usage

extractDescScales(
  x,
  propmat,
  index = NULL,
  pc,
  lag,
  scale = TRUE,
  silent = TRUE
)

Arguments

x

A character vector, as the input protein sequence.

propmat

The matrix containing the descriptor set for the amino acids, which can be chosen from AAMOE2D, AAMOE3D, AACPSA, AADescAll, AA2DACOR, AA3DMoRSE, AAACF, AABurden, AAConn, AAConst, AAEdgeAdj, AAEigIdx, AAFGC, AAGeom, AAGETAWAY, AAInfo, AAMolProp, AARandic, AARDF, AATopo, AATopoChg, AAWalk, and AAWHIM.

index

Integer vector or character vector. Specify which molecular descriptors to select from one of these deseriptor sets by specify the numerical or character index of the molecular descriptors in the descriptor set. Default is NULL, which means selecting all the molecular descriptors in this descriptor set.

pc

Integer. The maximum dimension of the space which the data are to be represented in. Must be no greater than the number of amino acid properties provided.

lag

The lag parameter. Must be less than the amino acids.

scale

Logical. Should we auto-scale the property matrix (propmat) before doing MDS? Default is TRUE.

silent

Logical. Whether we print the standard deviation, proportion of variance and the cumulative proportion of the selected principal components or not. Default is TRUE.

Value

A length lag * p^2 named vector, p is the number of scales selected.

Author(s)

Nan Xiao <https://nanx.me>

Examples

x <- readFASTA(system.file("protseq/P00750.fasta", package = "protr"))[[1]]
descscales <- extractDescScales(
  x,
  propmat = "AATopo", index = c(37:41, 43:47),
  pc = 5, lag = 7, silent = FALSE
)

Scales-Based Descriptors derived by Factor Analysis

Description

This function calculates scales-based descriptors derived by Factor Analysis (FA). Users can provide customized amino acid property matrices.

Usage

extractFAScales(
  x,
  propmat,
  factors,
  scores = "regression",
  lag,
  scale = TRUE,
  silent = TRUE
)

Arguments

x

A character vector, as the input protein sequence.

propmat

A matrix containing the properties for the amino acids. Each row represent one amino acid type, each column represents one property. Note that the one-letter row names must be provided for we need them to seek the properties for each AA type.

factors

Integer. The number of factors to be fitted. Must be no greater than the number of AA properties provided.

scores

Type of scores to produce. The default is "regression", which gives Thompson's scores, "Bartlett" given Bartlett's weighted least-squares scores.

lag

The lag parameter. Must be less than the amino acids number in the protein sequence.

scale

Logical. Should we auto-scale the property matrix (propmat) before doing Factor Analysis? Default is TRUE.

silent

Logical. Whether we print the SS loadings, proportion of variance and the cumulative proportion of the selected factors or not. Default is TRUE.

Value

A length lag * p^2 named vector, p is the number of scales (factors) selected.

Author(s)

Nan Xiao <https://nanx.me>

References

Atchley, W. R., Zhao, J., Fernandes, A. D., & Druke, T. (2005). Solving the protein sequence metric problem. Proceedings of the National Academy of Sciences of the United States of America, 102(18), 6395-6400.

Examples

x <- readFASTA(system.file("protseq/P00750.fasta", package = "protr"))[[1]]
data(AATopo)
tprops <- AATopo[, c(37:41, 43:47)] # select a set of topological descriptors
fa <- extractFAScales(x, propmat = tprops, factors = 5, lag = 7, silent = FALSE)

Geary Autocorrelation Descriptor

Description

This function calculates the Geary autocorrelation descriptor (dim: length(props) * nlag).

Usage

extractGeary(
  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

Value

A length length(props) * nlag named vector.

Note

Author(s)

Nan Xiao <https://nanx.me>

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.

Examples

x <- readFASTA(system.file("protseq/P00750.fasta", package = "protr"))[[1]]
extractGeary(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
extractGeary(
  x,
  customprops = myprops,
  props = c(
    "CIDH920105", "BHAR880101",
    "CHAM820101", "CHAM820102",
    "MyProp1", "MyProp2", "MyProp3"
  )
)

Scales-Based Descriptors derived by Multidimensional Scaling

Description

This function calculates scales-based descriptors derived by Multidimensional Scaling (MDS). Users can provide customized amino acid property matrices.

Usage

extractMDSScales(x, propmat, k, lag, scale = TRUE, silent = TRUE)

Arguments

x

A character vector, as the input protein sequence.

propmat

k

Integer. The maximum dimension of the space which the data are to be represented in. Must be no greater than the number of AA properties provided.

lag

The lag parameter. Must be less than the amino acids.

scale

Logical. Should we auto-scale the property matrix (propmat) before doing MDS? Default is TRUE.

silent

Logical. Whether to print the k eigenvalues computed during the scaling process or not. Default is TRUE.

Value

A length lag * p^2 named vector, p is the number of scales (dimensionality) selected.

Author(s)

Nan Xiao <https://nanx.me>

References

Venkatarajan, M. S., & Braun, W. (2001). New quantitative descriptors of amino acids based on multidimensional scaling of a large number of physical-chemical properties. Molecular modeling annual, 7(12), 445–453.

Examples

x <- readFASTA(system.file("protseq/P00750.fasta", package = "protr"))[[1]]
data(AATopo)
tprops <- AATopo[, c(37:41, 43:47)] # select a set of topological descriptors
mds <- extractMDSScales(x, propmat = tprops, k = 5, lag = 7, silent = FALSE)

Moran Autocorrelation Descriptor

Description

This function calculates the Moran autocorrelation descriptor (dim: length(props) * nlag).

Usage

extractMoran(
  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

Value

A length length(props) * nlag named vector.

Note

Author(s)

Nan Xiao <https://nanx.me>

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.

Examples

x <- readFASTA(system.file("protseq/P00750.fasta", package = "protr"))[[1]]
extractMoran(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
extractMoran(
  x,
  customprops = myprops,
  props = c(
    "CIDH920105", "BHAR880101",
    "CHAM820101", "CHAM820102",
    "MyProp1", "MyProp2", "MyProp3"
  )
)

Normalized Moreau-Broto Autocorrelation Descriptor

Description

This function calculates the normalized Moreau-Broto autocorrelation descriptor (dim: length(props) * nlag).

Usage

extractMoreauBroto(
  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

Value

A length length(props) * nlag named vector.

Note

Author(s)

Nan Xiao <https://nanx.me>

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.

Examples

x <- readFASTA(system.file("protseq/P00750.fasta", package = "protr"))[[1]]
extractMoreauBroto(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
extractMoreauBroto(
  x,
  customprops = myprops,
  props = c(
    "CIDH920105", "BHAR880101",
    "CHAM820101", "CHAM820102",
    "MyProp1", "MyProp2", "MyProp3"
  )
)

Pseudo Amino Acid Composition (PseAAC) Descriptor

Description

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

Usage

extractPAAC(
  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 PseAAC descriptors, default is 30.

w

The weighting factor, default is 0.05.

customprops

Value

A length 20 + lambda named vector

Note

Note the default 20 * 3 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)

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)

Examples

x <- readFASTA(system.file("protseq/P00750.fasta", package = "protr"))[[1]]
extractPAAC(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 from the
# AAindex database, and 3 cutomized properties
extractPAAC(
  x,
  customprops = myprops,
  props = c(
    "Hydrophobicity", "Hydrophilicity", "SideChainMass",
    "CIDH920105", "BHAR880101",
    "CHAM820101", "CHAM820102",
    "MyProp1", "MyProp2", "MyProp3"
  )
)

Compute PSSM (Position-Specific Scoring Matrix) for given protein sequence

Description

This function calculates the PSSM (Position-Specific Scoring Matrix) derived by PSI-Blast for given protein sequence or peptides.

Usage

extractPSSM(
  seq,
  start.pos = 1L,
  end.pos = nchar(seq),
  psiblast.path = NULL,
  makeblastdb.path = NULL,
  database.path = NULL,
  iter = 5,
  silent = TRUE,
  evalue = 10L,
  word.size = NULL,
  gapopen = NULL,
  gapextend = NULL,
  matrix = "BLOSUM62",
  threshold = NULL,
  seg = "no",
  soft.masking = FALSE,
  culling.limit = NULL,
  best.hit.overhang = NULL,
  best.hit.score.edge = NULL,
  xdrop.ungap = NULL,
  xdrop.gap = NULL,
  xdrop.gap.final = NULL,
  window.size = NULL,
  gap.trigger = 22L,
  num.threads = 1L,
  pseudocount = 0L,
  inclusion.ethresh = 0.002
)

Arguments

seq

Character vector, as the input protein sequence.

start.pos

Optional integer denoting the start position of the fragment window. Default is 1, i.e. the first amino acid of the given sequence.

end.pos

Optional integer denoting the end position of the fragment window. Default is nchar(seq), i.e. the last amino acid of the given sequence.

psiblast.path

Character string indicating the path of the psiblast program. If NCBI Blast+ was previously installed in the operation system, the path will be automatically detected.

makeblastdb.path

Character string indicating the path of the makeblastdb program. If NCBI Blast+ was previously installed in the system, the path will be automatically detected.

database.path

Character string indicating the path of a reference database (a FASTA file).

iter

Number of iterations to perform for PSI-Blast.

silent

Logical. Whether the PSI-Blast running output should be shown or not (May not work on some Windows versions and PSI-Blast versions), default is TRUE.

evalue

Expectation value (E) threshold for saving hits. Default is 10.

word.size

Word size for wordfinder algorithm. An integer >= 2.

gapopen

Integer. Cost to open a gap.

gapextend

Integer. Cost to extend a gap.

matrix

Character string. The scoring matrix name (default is "BLOSUM62").

threshold

Minimum word score such that the word is added to the BLAST lookup table. A real value >= 0.

seg

Character string. Filter query sequence with SEG ("yes", "window locut hicut", or "no" to disable). Default is "no".

soft.masking

Logical. Apply filtering locations as soft masks? Default is FALSE.

culling.limit

An integer >= 0. If the query range of a hit is enveloped by that of at least this many higher-scoring hits, delete the hit. Incompatible with best.hit.overhang and best_hit_score_edge.

best.hit.overhang

Best Hit algorithm overhang value (A real value >= 0 and =< 0.5, recommended value: 0.1). Incompatible with culling_limit.

best.hit.score.edge

Best Hit algorithm score edge value (A real value >=0 and =< 0.5, recommended value: 0.1). Incompatible with culling_limit.

xdrop.ungap

X-dropoff value (in bits) for ungapped extensions.

xdrop.gap

X-dropoff value (in bits) for preliminary gapped extensions.

xdrop.gap.final

X-dropoff value (in bits) for final gapped alignment.

window.size

An integer >= 0. Multiple hits window size, To specify 1-hit algorithm, use 0.

gap.trigger

Number of bits to trigger gapping. Default is 22.

num.threads

Integer. Number of threads (CPUs) to use in the BLAST search. Default is 1.

pseudocount

Integer. Pseudo-count value used when constructing PSSM. Default is 0.

inclusion.ethresh

E-value inclusion threshold for pairwise alignments. Default is 0.002.

Details

For given protein sequences or peptides, PSSM represents the log-likelihood of the substitution of the 20 types of amino acids at that position in the sequence. Note that the output value is not normalized.

Value

The original PSSM, a numeric matrix which has end.pos - start.pos + 1 columns and 20 named rows.

Note

The function requires the makeblastdb and psiblast programs to be properly installed in the operation system or their paths provided.

The two command-line programs are included in the NCBI-BLAST+ software package. To install NCBI Blast+ for your operating system, see https://blast.ncbi.nlm.nih.gov/doc/blast-help/downloadblastdata.html for detailed instructions.

Ubuntu/Debian users can directly use the command sudo apt-get install ncbi-blast+ to install NCBI Blast+. For OS X users, download ncbi-blast- ... .dmg then install. For Windows users, download ncbi-blast- ... .exe then install.

Author(s)

Nan Xiao <https://nanx.me>

References

Altschul, Stephen F., et al. "Gapped BLAST and PSI-BLAST: a new generation of protein database search programs." Nucleic acids research 25.17 (1997): 3389–3402.

Ye, Xugang, Guoli Wang, and Stephen F. Altschul. "An assessment of substitution scores for protein profile-profile comparison." Bioinformatics 27.24 (2011): 3356–3363.

Rangwala, Huzefa, and George Karypis. "Profile-based direct kernels for remote homology detection and fold recognition." Bioinformatics 21.23 (2005): 4239–4247.

Examples

if (Sys.which("makeblastdb") == "" | Sys.which("psiblast") == "") {
  cat("Cannot find makeblastdb or psiblast. Please install NCBI Blast+ first")
} else {
  x <- readFASTA(system.file(
    "protseq/P00750.fasta",
    package = "protr"
  ))[[1]]
  dbpath <- tempfile("tempdb", fileext = ".fasta")
  invisible(file.copy(from = system.file(
    "protseq/Plasminogen.fasta",
    package = "protr"
  ), to = dbpath))

  pssmmat <- extractPSSM(seq = x, database.path = dbpath)

  dim(pssmmat) # 20 x 562 (P00750: length 562, 20 Amino Acids)
}

Profile-based protein representation derived by PSSM (Position-Specific Scoring Matrix) and auto cross covariance

Description

This function calculates the feature vector based on the PSSM by running PSI-Blast and auto cross covariance tranformation.

Usage

extractPSSMAcc(pssmmat, lag)

Arguments

pssmmat

The PSSM computed by extractPSSM.

lag

The lag parameter. Must be less than the number of amino acids in the sequence (i.e. the number of columns in the PSSM matrix).

Value

A length lag * 20^2 named numeric vector, the element names are derived by the amino acid name abbreviation (crossed amino acid name abbreviation) and lag index.

Author(s)

Nan Xiao <https://nanx.me>

References

Examples

if (Sys.which("makeblastdb") == "" | Sys.which("psiblast") == "") {
  cat("Cannot find makeblastdb or psiblast. Please install NCBI Blast+")
} else {
  x <- readFASTA(system.file(
    "protseq/P00750.fasta",
    package = "protr"
  ))[[1]]
  dbpath <- tempfile("tempdb", fileext = ".fasta")
  invisible(file.copy(from = system.file(
    "protseq/Plasminogen.fasta",
    package = "protr"
  ), to = dbpath))

  pssmmat <- extractPSSM(seq = x, database.path = dbpath)
  pssmacc <- extractPSSMAcc(pssmmat, lag = 3)
  tail(pssmacc)
}

Profile-based protein representation derived by PSSM (Position-Specific Scoring Matrix)

Description

This function calculates the profile-based protein representation derived by PSSM. The feature vector is based on the PSSM computed by extractPSSM.

Usage

extractPSSMFeature(pssmmat)

Arguments

pssmmat

The PSSM computed by extractPSSM.

Details

For a given sequence, the PSSM feature represents the log-likelihood of the substitution of the 20 types of amino acids at that position in the sequence.

Each PSSM feature value in the vector represents the degree of conservation of a given amino acid type. The value is normalized to interval (0, 1) by the transformation 1/(1+e^(-x)).

Value

A numeric vector which has 20 x N named elements, where N is the size of the window (number of rows of the PSSM).

Author(s)

Nan Xiao <https://nanx.me>

References

Ye, Xugang, Guoli Wang, and Stephen F. Altschul. "An assessment of substitution scores for protein profile-profile comparison." Bioinformatics 27.24 (2011): 3356–3363.

Rangwala, Huzefa, and George Karypis. "Profile-based direct kernels for remote homology detection and fold recognition." Bioinformatics 21.23 (2005): 4239–4247.

Examples

if (Sys.which("makeblastdb") == "" | Sys.which("psiblast") == "") {
  cat("Cannot find makeblastdb or psiblast. Please install NCBI Blast+")
} else {
  x <- readFASTA(system.file(
    "protseq/P00750.fasta",
    package = "protr"
  ))[[1]]
  dbpath <- tempfile("tempdb", fileext = ".fasta")
  invisible(file.copy(from = system.file(
    "protseq/Plasminogen.fasta",
    package = "protr"
  ), to = dbpath))

  pssmmat <- extractPSSM(seq = x, database.path = dbpath)
  pssmfeature <- extractPSSMFeature(pssmmat)
  head(pssmfeature)
}

Amino Acid Properties Based Scales Descriptors (Protein Fingerprint)

Description

This function calculates amino acid properties based scales descriptors (protein fingerprint). Users can specify which AAindex properties to select from the AAindex database by specify the numerical or character index of the properties in the AAindex database.

Usage

extractProtFP(x, index = NULL, pc, lag, scale = TRUE, silent = TRUE)

Arguments

x

A character vector, as the input protein sequence.

index

Integer vector or character vector. Specify which AAindex properties to select from the AAindex database by specify the numerical or character index of the properties in the AAindex database. Default is NULL, means selecting all the AA properties in the AAindex database.

pc

Integer. Use the first pc principal components as the scales. Must be no greater than the number of AA properties provided.

lag

The lag parameter. Must be less than the amino acids.

scale

Logical. Should we auto-scale the property matrix before PCA? Default is TRUE.

silent

Logical. Whether we print the standard deviation, proportion of variance and the cumulative proportion of the selected principal components or not. Default is TRUE.

Value

A length lag * p^2 named vector, p is the number of scales (principal components) selected.

Author(s)

Nan Xiao <https://nanx.me>

Examples

x <- readFASTA(system.file("protseq/P00750.fasta", package = "protr"))[[1]]
fp <- extractProtFP(x, index = c(160:165, 258:296), pc = 5, lag = 7, silent = FALSE)

Amino Acid Properties Based Scales Descriptors (Protein Fingerprint) with Gap Support

Description

This function calculates amino acid properties based scales descriptors (protein fingerprint) with gap support. Users can specify which AAindex properties to select from the AAindex database by specify the numerical or character index of the properties in the AAindex database.

Usage

extractProtFPGap(x, index = NULL, pc, lag, scale = TRUE, silent = TRUE)

Arguments

x

A character vector, as the input protein sequence. Use '-' to represent gaps in the sequence.

index

pc

Integer. Use the first pc principal components as the scales. Must be no greater than the number of AA properties provided.

lag

The lag parameter. Must be less than the amino acids.

scale

Logical. Should we auto-scale the property matrix before PCA? Default is TRUE.

silent

Logical. Whether we print the standard deviation, proportion of variance and the cumulative proportion of the selected principal components or not. Default is TRUE.

Value

A length lag * p^2 named vector, p is the number of scales (principal components) selected.

Author(s)

Nan Xiao <https://nanx.me>

Examples

# amino acid sequence with gaps
x <- readFASTA(system.file("protseq/align.fasta", package = "protr"))$`IXI_235`
fp <- extractProtFPGap(x, index = c(160:165, 258:296), pc = 5, lag = 7, silent = FALSE)

Quasi-Sequence-Order (QSO) Descriptor

Description

This function calculates the Quasi-Sequence-Order (QSO) descriptor (dim: 20 + 20 + (2 * nlag), default is 100).

Usage

extractQSO(x, nlag = 30, w = 0.1)

Arguments

x

A character vector, as the input protein sequence.

nlag

The maximum lag, defualt is 30.

w

The weighting factor, default is 0.1.

Value

A length 20 + 20 + (2 * nlag) named vector

Author(s)

Nan Xiao <https://nanx.me>

References

Kuo-Chen Chou. Prediction of Protein Subcellar Locations by Incorporating Quasi-Sequence-Order Effect. Biochemical and Biophysical Research Communications, 2000, 278, 477-483.

Kuo-Chen Chou and Yu-Dong Cai. Prediction of Protein Sucellular Locations by GO-FunD-PseAA Predictor. Biochemical and Biophysical Research Communications, 2004, 320, 1236-1239.

Gisbert Schneider and Paul Wrede. The Rational Design of Amino Acid Sequences by Artifical Neural Networks and Simulated Molecular Evolution: Do Novo Design of an Idealized Leader Cleavge Site. Biophys Journal, 1994, 66, 335-344.

Examples

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

Sequence-Order-Coupling Numbers

Description

This function calculates the Sequence-Order-Coupling Numbers (dim: nlag * 2, default is 60).

Usage

extractSOCN(x, nlag = 30)

Arguments

x

A character vector, as the input protein sequence.

nlag

The maximum lag, defualt is 30.

Value

A length nlag * 2 named vector

Author(s)

Nan Xiao <https://nanx.me>

References

Kuo-Chen Chou. Prediction of Protein Subcellar Locations by Incorporating Quasi-Sequence-Order Effect. Biochemical and Biophysical Research Communications, 2000, 278, 477-483.

Kuo-Chen Chou and Yu-Dong Cai. Prediction of Protein Sucellular Locations by GO-FunD-PseAA Predictor. Biochemical and Biophysical Research Communications, 2004, 320, 1236-1239.

Examples

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

Scales-Based Descriptors derived by Principal Components Analysis

Description

This function calculates scales-based descriptors derived by Principal Components Analysis (PCA). Users can provide customized amino acid property matrices. This function implements the core computation procedure needed for the scales-based descriptors derived by AA-Properties (AAindex) and scales-based descriptors derived by 20+ classes of 2D and 3D molecular descriptors (Topological, WHIM, VHSE, etc.) in the protr package.

Usage

extractScales(x, propmat, pc, lag, scale = TRUE, silent = TRUE)

Arguments

x

A character vector, as the input protein sequence.

propmat

pc

Integer. Use the first pc principal components as the scales. Must be no greater than the number of AA properties provided.

lag

The lag parameter. Must be less than the amino acids.

scale

Logical. Should we auto-scale the property matrix (propmat) before PCA? Default is TRUE.

silent

Logical. Whether we print the standard deviation, proportion of variance and the cumulative proportion of the selected principal components or not. Default is TRUE.

Value

A length lag * p^2 named vector, p is the number of scales (principal components) selected.

Author(s)

Nan Xiao <https://nanx.me>

Examples

x <- readFASTA(system.file("protseq/P00750.fasta", package = "protr"))[[1]]
data(AAindex)
AAidxmat <- t(na.omit(as.matrix(AAindex[, 7:26])))
scales <- extractScales(x, propmat = AAidxmat, pc = 5, lag = 7, silent = FALSE)

Scales-Based Descriptors derived by Principal Components Analysis (with Gap Support)

Description

This function calculates scales-based descriptors derived by Principal Components Analysis (PCA), with gap support. Users can provide customized amino acid property matrices. This function implements the core computation procedure needed for the scales-based descriptors derived by AA-Properties (AAindex) and scales-based descriptors derived by 20+ classes of 2D and 3D molecular descriptors (Topological, WHIM, VHSE, etc.) in the protr package.

Usage

extractScalesGap(x, propmat, pc, lag, scale = TRUE, silent = TRUE)

Arguments

x

A character vector, as the input protein sequence. Use '-' to represent gaps in the sequence.

propmat

pc

Integer. Use the first pc principal components as the scales. Must be no greater than the number of AA properties provided.

lag

The lag parameter. Must be less than the amino acids.

scale

Logical. Should we auto-scale the property matrix (propmat) before PCA? Default is TRUE.

silent

Logical. Whether to print the standard deviation, proportion of variance and the cumulative proportion of the selected principal components or not. Default is TRUE.

Value

A length lag * p^2 named vector, p is the number of scales (principal components) selected.

Author(s)

Nan Xiao <https://nanx.me>

Examples

# amino acid sequence with gaps
x <- readFASTA(system.file("protseq/align.fasta", package = "protr"))$`IXI_235`
data(AAindex)
AAidxmat <- t(na.omit(as.matrix(AAindex[, 7:26])))
scales <- extractScalesGap(x, propmat = AAidxmat, pc = 5, lag = 7, silent = FALSE)

Tripeptide Composition Descriptor

Description

This function calculates the Tripeptide Composition descriptor (dim: 8,000).

Usage

extractTC(x)

Arguments

x

A character vector, as the input protein sequence.

Value

A length 8,000 named vector

Author(s)

Nan Xiao <https://nanx.me>

References

M. Bhasin, G. P. S. Raghava. Classification of Nuclear Receptors Based on Amino Acid Composition and Dipeptide Composition. Journal of Biological Chemistry, 2004, 279, 23262.

Examples

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

Retrieve Protein Sequences from UniProt by Protein ID

Description

This function retrieves protein sequences from uniprot.org by protein ID(s).

Usage

getUniProt(id)

Arguments

id

A character vector, as the protein ID(s).

Value

A list, each component contains one protein sequence.

Author(s)

Nan Xiao <https://nanx.me>

Examples

## Not run: 
# Network latency may slow down this example
# Only test this when your connection is fast enough
ids <- c("P00750", "P00751", "P00752")
getUniProt(ids)

## End(Not run)

Protein Similarity Calculation based on Gene Ontology (GO) Similarity

Description

This function calculates protein similarity based on Gene Ontology (GO) similarity.

Usage

parGOSim(
  golist,
  type = c("go", "gene"),
  ont = c("MF", "BP", "CC"),
  organism = "human",
  measure = "Resnik",
  combine = "BMA"
)

Arguments

golist

A list, each component contains a character vector of GO terms or one Entrez Gene ID.

type

Input type for golist, "go" for GO Terms, "gene" for gene ID.

ont

Default is "MF", can be one of "MF", "BP", or "CC" subontologies.

organism

Organism name. Default is "human", can be one of "anopheles", "arabidopsis", "bovine", "canine", "chicken", "chimp", "coelicolor", "ecolik12", "ecsakai", "fly", "human", "malaria", "mouse", "pig", "rat", "rhesus", "worm", "xenopus", "yeast" or "zebrafish". Before specifying the organism, please install the corresponding genome wide annotation data package for the selected organism.

measure

Default is "Resnik", can be one of "Resnik", "Lin", "Rel", "Jiang" or "Wang".

combine

Default is "BMA", can be one of "max", "average", "rcmax" or "BMA" for combining semantic similarity scores of multiple GO terms associated with proteins.

Value

A n x n similarity matrix.

Author(s)

Nan Xiao <https://nanx.me>

Examples

## Not run: 

# Be careful when testing this since it involves GO similarity computation
# and might produce unpredictable results in some environments

library("GOSemSim")
library("org.Hs.eg.db")

# By GO Terms
# AP4B1
go1 <- c(
  "GO:0005215", "GO:0005488", "GO:0005515",
  "GO:0005625", "GO:0005802", "GO:0005905"
)
# BCAS2
go2 <- c(
  "GO:0005515", "GO:0005634", "GO:0005681",
  "GO:0008380", "GO:0031202"
)
# PDE4DIP
go3 <- c(
  "GO:0003735", "GO:0005622", "GO:0005840",
  "GO:0006412"
)
golist <- list(go1, go2, go3)
parGOSim(golist, type = "go", ont = "CC", measure = "Wang")

# By Entrez gene id
genelist <- list(c("150", "151", "152", "1814", "1815", "1816"))
parGOSim(genelist, type = "gene", ont = "BP", measure = "Wang")

## End(Not run)

Parallel Protein Sequence Similarity Calculation Based on Sequence Alignment (In-Memory Version)

Description

Parallel calculation of protein sequence similarity based on sequence alignment.

Usage

parSeqSim(
  protlist,
  cores = 2,
  batches = 1,
  verbose = FALSE,
  type = "local",
  submat = "BLOSUM62",
  gap.opening = 10,
  gap.extension = 4
)

Arguments

protlist

A length n list containing n protein sequences, each component of the list is a character string, storing one protein sequence. Unknown sequences should be represented as "".

cores

Integer. The number of CPU cores to use for parallel execution, default is 2. Users can use the availableCores() function in the parallelly package to see how many cores they could use.

batches

verbose

Print the computation progress? Useful when batches > 1.

type

Type of alignment, default is "local", can be "global" or "local", where "global" represents Needleman-Wunsch global alignment; "local" represents Smith-Waterman local alignment.

submat

Substitution matrix, default is "BLOSUM62", can be one of "BLOSUM45", "BLOSUM50", "BLOSUM62", "BLOSUM80", "BLOSUM100", "PAM30", "PAM40", "PAM70", "PAM120", or "PAM250".

gap.opening

The cost required to open a gap of any length in the alignment. Defaults to 10.

gap.extension

The cost to extend the length of an existing gap by 1. Defaults to 4.

Value

A n x n similarity matrix.

Author(s)

Nan Xiao <https://nanx.me>

Examples

## Not run: 

# Be careful when testing this since it involves parallelization
# and might produce unpredictable results in some environments

library("Biostrings")
library("foreach")
library("doParallel")

s1 <- readFASTA(system.file("protseq/P00750.fasta", package = "protr"))[[1]]
s2 <- readFASTA(system.file("protseq/P08218.fasta", package = "protr"))[[1]]
s3 <- readFASTA(system.file("protseq/P10323.fasta", package = "protr"))[[1]]
s4 <- readFASTA(system.file("protseq/P20160.fasta", package = "protr"))[[1]]
s5 <- readFASTA(system.file("protseq/Q9NZP8.fasta", package = "protr"))[[1]]
plist <- list(s1, s2, s3, s4, s5)
(psimmat <- parSeqSim(plist, cores = 2, type = "local", submat = "BLOSUM62"))

## End(Not run)

Parallel Protein Sequence Similarity Calculation Based on Sequence Alignment (Disk-Based Version)

Description

Parallel calculation of protein sequence similarity based on sequence alignment. This version offloads the partial results in each batch to the hard drive and merges the results together in the end, which reduces the memory usage.

Usage

parSeqSimDisk(
  protlist,
  cores = 2,
  batches = 1,
  path = tempdir(),
  verbose = FALSE,
  type = "local",
  submat = "BLOSUM62",
  gap.opening = 10,
  gap.extension = 4
)

Arguments

protlist

A length n list containing n protein sequences, each component of the list is a character string, storing one protein sequence. Unknown sequences should be represented as "".

cores

Integer. The number of CPU cores to use for parallel execution, default is 2. Users can use the availableCores() function in the parallelly package to see how many cores they could use.

batches

path

Directory for caching the results in each batch. Defaults to the temporary directory.

verbose

Print the computation progress? Useful when batches > 1.

type

Type of alignment, default is "local", can be "global" or "local", where "global" represents Needleman-Wunsch global alignment; "local" represents Smith-Waterman local alignment.

submat

Substitution matrix, default is "BLOSUM62", can be one of "BLOSUM45", "BLOSUM50", "BLOSUM62", "BLOSUM80", "BLOSUM100", "PAM30", "PAM40", "PAM70", "PAM120", or "PAM250".

gap.opening

The cost required to open a gap of any length in the alignment. Defaults to 10.

gap.extension

The cost to extend the length of an existing gap by 1. Defaults to 4.

Value

A n x n similarity matrix.

Author(s)

Nan Xiao <https://nanx.me>

Examples

## Not run: 

# Be careful when testing this since it involves parallelization
# and might produce unpredictable results in some environments

library("Biostrings")
library("foreach")
library("doParallel")

s1 <- readFASTA(system.file("protseq/P00750.fasta", package = "protr"))[[1]]
s2 <- readFASTA(system.file("protseq/P08218.fasta", package = "protr"))[[1]]
s3 <- readFASTA(system.file("protseq/P10323.fasta", package = "protr"))[[1]]
s4 <- readFASTA(system.file("protseq/P20160.fasta", package = "protr"))[[1]]
s5 <- readFASTA(system.file("protseq/Q9NZP8.fasta", package = "protr"))[[1]]
set.seed(1010)
plist <- as.list(c(s1, s2, s3, s4, s5)[sample(1:5, 100, replace = TRUE)])
psimmat <- parSeqSimDisk(
  plist,
  cores = 2, batches = 10, verbose = TRUE,
  type = "local", submat = "BLOSUM62"
)

## End(Not run)

Protein sequence amino acid type sanity check

Description

This function checks if the protein sequence's amino acid types are in the 20 default types.

Usage

protcheck(x)

Arguments

x

A character vector, as the input protein sequence.

Value

Logical. TRUE if all of the amino acid types of the sequence are within the 20 default types.

Author(s)

Nan Xiao <https://nanx.me>

Examples

x <- readFASTA(system.file("protseq/P00750.fasta", package = "protr"))[[1]]
protcheck(x) # TRUE
protcheck(paste(x, "Z", sep = "")) # FALSE

Protein Sequence Segmentation/Partition

Description

This function extracts the segmentations from the protein sequence.

Usage

protseg(
  x,
  aa = c("A", "R", "N", "D", "C", "E", "Q", "G", "H", "I", "L", "K", "M", "F", "P", "S",
    "T", "W", "Y", "V"),
  k = 7
)

Arguments

x

A character vector, as the input protein sequence.

aa

A character, the amino acid type. One of 'A', 'R', 'N', 'D', 'C', 'E', 'Q', 'G', 'H', 'I', 'L', 'K', 'M', 'F', 'P', 'S', 'T', 'W', 'Y', 'V'.

k

A positive integer, specifys the window size (half of the window), default is 7.

Value

A named list, each component contains one of the segmentations (a character string), names of the list components are the positions of the specified amino acid in the sequence.

Author(s)

Nan Xiao <https://nanx.me>

Examples

x <- readFASTA(system.file("protseq/P00750.fasta", package = "protr"))[[1]]
protseg(x, aa = "R", k = 5)

Read Protein Sequences in FASTA Format

Description

This function reads protein sequences in FASTA format.

Usage

readFASTA(
  file = system.file("protseq/P00750.fasta", package = "protr"),
  legacy.mode = TRUE,
  seqonly = FALSE
)

Arguments

file

Path to the file containing the protein sequences in FASTA format. If it does not contain an absolute or relative path, the file name is relative to the current working directory, getwd. The default here is to read the P00750.fasta file which is present in the protseq directory of the protr package.

legacy.mode

If set to TRUE, lines starting with a semicolon (;) are ignored. Default value is TRUE.

seqonly

If set to TRUE, only sequences as returned without attempt to modify them or to get their names and annotations (execution time is divided approximately by a factor 3). Default value is FALSE.

Value

Character vector of the protein sequences.

The three returned argument are just different forms of the same output. If one is interested in a Mahalanobis metric over the original data space, the first argument is all she/he needs. If a transformation into another space (where one can use the Euclidean metric) is preferred, the second returned argument is sufficient. Using A and B is equivalent in the following sense.

Author(s)

Nan Xiao <https://nanx.me>

References

Pearson, W.R. and Lipman, D.J. (1988) Improved tools for biological sequence comparison. Proceedings of the National Academy of Sciences of the United States of America, 85: 2444–2448.

Examples

P00750 <- readFASTA(system.file("protseq/P00750.fasta", package = "protr"))

Read Protein Sequences in PDB Format

Description

This function reads protein sequences in PDB (Protein Data Bank) format, and return the amino acid sequences represented by single-letter code.

Usage

readPDB(file = system.file("protseq/4HHB.pdb", package = "protr"))

Arguments

file

Path to the file containing the protein sequences in PDB format. If it does not contain an absolute or relative path, the file name is relative to the current working directory, getwd. The default here is to read the 4HHB.PDB file which is present in the protseq directory of the protr package.

Value

Character vector of the protein sequence.

Author(s)

Nan Xiao <https://nanx.me>

References

Protein Data Bank Contents Guide: Atomic Coordinate Entry Format Description, Version 3.30. Accessed 2013-06-26. https://files.wwpdb.org/pub/pdb/doc/format_descriptions/Format_v33_Letter.pdf

Examples

Seq4HHB <- readPDB(system.file("protseq/4HHB.pdb", package = "protr"))

Remove or replace gaps from protein sequences.

Description

Remove/replace gaps or any irregular characters from protein sequences, to make them suitable for feature extraction or sequence alignment based similarity computation.

Usage

removeGaps(x, pattern = "-", replacement = "", ...)

Arguments

x

character vector, containing the input protein sequence(s).

pattern

character string contains the gap (or other irregular) character to be removed or replaced. Default is "-". For advanced usage, see gsub.

replacement

a replacement for matched characters. Default is "" (remove the matched character).

...

addtional parameters for gsub.

Value

a vector of protein sequence(s) with gaps or irregular characters removed/replaced.

Author(s)

Nan Xiao <https://nanx.me>

Examples

# amino acid sequences that contain gaps ("-")
aaseq <- list(
  "MHGDTPTLHEYMLDLQPETTDLYCYEQLSDSSE-EEDEIDGPAGQAEPDRAHYNIVTFCCKCDSTLRLCVQS",
  "MHGDTPTLHEYMLDLQPETTDLYCYEQLNDSSE-EEDEIDGPAGQAEPDRAHYNIVTFCCKCDSTLRLCVQS"
)
## Not run: 
#' # gaps create issues for alignment
parSeqSim(aaseq)

# remove the gaps
nogapseq <- removeGaps(aaseq)
parSeqSim(nogapseq)

## End(Not run)

Protein Similarity Calculation based on Gene Ontology (GO) Similarity

Description

This function calculates the Gene Ontology (GO) similarity between two groups of GO terms or two Entrez gene IDs.

Usage

twoGOSim(
  id1,
  id2,
  type = c("go", "gene"),
  ont = c("MF", "BP", "CC"),
  organism = "human",
  measure = "Resnik",
  combine = "BMA"
)

Arguments

id1

Character vector. When length > 1: each element is a GO term; when length = 1: the Entrez Gene ID.

id2

Character vector. When length > 1: each element is a GO term; when length = 1: the Entrez Gene ID.

type

Input type of id1 and id2, 'go' for GO Terms, "gene" for gene ID.

ont

Default is "MF", can be one of "MF", "BP", or "CC" subontologies.

organism

measure

Default is "Resnik", can be one of "Resnik", "Lin", "Rel", "Jiang" or "Wang".

combine

Default is "BMA", can be one of "max", "average", "rcmax" or "BMA" for combining semantic similarity scores of multiple GO terms associated with proteins.

Value

Similarity value.

Author(s)

Nan Xiao <https://nanx.me>

Examples

## Not run: 

# Be careful when testing this since it involves GO similarity computation
# and might produce unpredictable results in some environments

library("GOSemSim")
library("org.Hs.eg.db")

# By GO terms
go1 <- c("GO:0004022", "GO:0004024", "GO:0004023")
go2 <- c("GO:0009055", "GO:0020037")
twoGOSim(go1, go2, type = "go", ont = "MF", measure = "Wang")

# By Entrez gene id
gene1 <- "1956" # EGFR
gene2 <- "2261" # FGFR3
twoGOSim(gene1, gene2, type = "gene", ont = "BP", measure = "Lin")

## End(Not run)

Protein Sequence Alignment for Two Protein Sequences

Description

Sequence alignment between two protein sequences.

Usage

twoSeqSim(
  seq1,
  seq2,
  type = "local",
  submat = "BLOSUM62",
  gap.opening = 10,
  gap.extension = 4
)

Arguments

seq1

Character string, containing one protein sequence.

seq2

Character string, containing another protein sequence.

type

Type of alignment, default is "local", could be "global" or "local", where "global" represents Needleman-Wunsch global alignment; "local" represents Smith-Waterman local alignment.

submat

Substitution matrix, default is "BLOSUM62", can be one of "BLOSUM45", "BLOSUM50", "BLOSUM62", "BLOSUM80", "BLOSUM100", "PAM30", "PAM40", "PAM70", "PAM120", or "PAM250".

gap.opening

The cost required to open a gap of any length in the alignment. Defaults to 10.

gap.extension

The cost to extend the length of an existing gap by 1. Defaults to 4.

Value

A Biostrings object containing the alignment scores and other alignment information.

Author(s)

Nan Xiao <https://nanx.me>

Examples

## Not run: 

# Be careful when testing this since it involves sequence alignment
# and might produce unpredictable results in some environments

library("Biostrings")

s1 <- readFASTA(system.file("protseq/P00750.fasta", package = "protr"))[[1]]
s2 <- readFASTA(system.file("protseq/P10323.fasta", package = "protr"))[[1]]
seqalign <- twoSeqSim(s1, s2)
summary(seqalign)
score(seqalign)

## End(Not run)

protr: Generating Various Numerical Representation Schemes for Protein Sequences

Description

Author(s)

See Also

2D Autocorrelations Descriptors for 20 Amino Acids calculated by Dragon

Description

Examples

3D-MoRSE Descriptors for 20 Amino Acids calculated by Dragon

Description

Examples

Atom-Centred Fragments Descriptors for 20 Amino Acids calculated by Dragon

Description

Examples

BLOSUM100 Matrix for 20 Amino Acids

Description

Examples

BLOSUM45 Matrix for 20 Amino Acids

Description

Examples

BLOSUM50 Matrix for 20 Amino Acids

Description

Examples

BLOSUM62 Matrix for 20 Amino Acids

Description

Examples

BLOSUM80 Matrix for 20 Amino Acids

Description

Examples

Burden Eigenvalues Descriptors for 20 Amino Acids calculated by Dragon

Description

Examples

CPSA Descriptors for 20 Amino Acids calculated by Discovery Studio

Description

Details

Examples

Connectivity Indices Descriptors for 20 Amino Acids calculated by Dragon

Description

Examples

Constitutional Descriptors for 20 Amino Acids calculated by Dragon

Description

Examples

All 2D Descriptors for 20 Amino Acids calculated by Dragon

Description

Examples

Edge Adjacency Indices Descriptors for 20 Amino Acids calculated by Dragon

Description

Examples

Eigenvalue-Based Indices Descriptors for 20 Amino Acids calculated by Dragon

Description

Examples

Functional Group Counts Descriptors for 20 Amino Acids calculated by Dragon

Description

Examples

GETAWAY Descriptors for 20 Amino Acids calculated by Dragon

Description

Examples

Geometrical Descriptors for 20 Amino Acids calculated by Dragon

Description

Examples

Information Indices Descriptors for 20 Amino Acids calculated by Dragon

Description

Examples

2D Descriptors for 20 Amino Acids calculated by MOE 2011.10

Description

Examples

3D Descriptors for 20 Amino Acids calculated by MOE 2011.10

Description

Examples

Meta Information for the 20 Amino Acids

Description

Examples

Molecular Properties Descriptors for 20 Amino Acids calculated by Dragon

Description

Examples

PAM120 Matrix for 20 Amino Acids

Description

Examples

PAM250 Matrix for 20 Amino Acids

Description

Examples