,
Ira Cooke [aut] ,
Jinlong Zhang [ctb] (R/read_faa.R),
Nan Xiao [ctb] (R/calc_pseudo_comp.R)| Title: | Predict Antimicrobial Peptides |
|---|---|
| Description: | A toolkit to predict antimicrobial peptides from protein sequences on a genome-wide scale. It incorporates two support vector machine models ("precursor" and "mature") trained on publicly available antimicrobial peptide data using calculated physico-chemical and compositional sequence properties described in Meher et al. (2017) <doi:10.1038/srep42362>. In order to support genome-wide analyses, these models are designed to accept any type of protein as input and calculation of compositional properties has been optimised for high-throughput use. For best results it is important to select the model that accurately represents your sequence type: for full length proteins, it is recommended to use the default "precursor" model. The alternative, "mature", model is best suited for mature peptide sequences that represent the final antimicrobial peptide sequence after post-translational processing. For details see Fingerhut et al. (2020) <doi:10.1093/bioinformatics/btaa653>. The 'ampir' package is also available via a Shiny based GUI at <https://ampir.marine-omics.net/>. |
| Authors: | Legana Fingerhut [aut, cre] ,
Ira Cooke [aut] ,
Jinlong Zhang [ctb] (R/read_faa.R),
Nan Xiao [ctb] (R/calc_pseudo_comp.R) |
| Maintainer: | Legana Fingerhut <[email protected]> |
| License: | GPL-2 |
| Version: | 1.1.0 |
| Built: | 2025-02-20 04:46:16 UTC |
| Source: | https://github.com/legana/ampir |
Any proteins that contains an amino acid that is not one of the 20 standard amino acids is flagged as invalid
aaseq_is_valid(seq)aaseq_is_valid(seq)
seq |
A vector of protein sequences |
A logical vector where TRUE indicates a valid protein sequence and FALSE indicates a sequence with invalid amino acids
Calculate amphiphilicity (or hydrophobic moment)
calc_amphiphilicity(seq)calc_amphiphilicity(seq)
seq |
A protein sequence |
Osorio, D., Rondon-Villarreal, P. & Torres, R. Peptides: A package for data mining of antimicrobial peptides. The R Journal. 7(1), 4–14 (2015). The imported function originates from the Peptides package (https://github.com/dosorio/Peptides/).
Calculate the hydrophobicity
calc_hydrophobicity(seq)calc_hydrophobicity(seq)
seq |
A protein sequence |
Osorio, D., Rondon-Villarreal, P. & Torres, R. Peptides: A package for data mining of antimicrobial peptides. The R Journal. 7(1), 4–14 (2015). The imported function originates from the Peptides package (https://github.com/dosorio/Peptides/).
Calculate the molecular weight
calc_mw(seq)calc_mw(seq)
seq |
A protein sequence |
Osorio, D., Rondon-Villarreal, P. & Torres, R. Peptides: A package for data mining of antimicrobial peptides. The R Journal. 7(1), 4–14 (2015). The imported function originates from the Peptides package (https://github.com/dosorio/Peptides/).
Calculate the net charge
calc_net_charge(seq)calc_net_charge(seq)
seq |
A protein sequence |
Osorio, D., Rondon-Villarreal, P. & Torres, R. Peptides: A package for data mining of antimicrobial peptides. The R Journal. 7(1), 4–14 (2015). The imported function originates from the Peptides package (https://github.com/dosorio/Peptides/).
Calculate the isoelectric point (pI)
calc_pI(seq)calc_pI(seq)
seq |
pI |
Osorio, D., Rondon-Villarreal, P. & Torres, R. Peptides: A package for data mining of antimicrobial peptides. The R Journal. 7(1), 4–14 (2015). The imported function originates from the Peptides package (https://github.com/dosorio/Peptides/).
This function is adapted from the extractPAAC function from the protr package (https://github.com/nanxstats/protr)
calc_pseudo_comp(seq, lambda_min = 4, lambda_max = 19)calc_pseudo_comp(seq, lambda_min = 4, lambda_max = 19)
seq |
A vector of protein sequences as character strings |
lambda_min |
Minimum allowable lambda. It is an error to provide a protein sequence shorter than lambda_min+1 |
lambda_max |
For each sequence lambda will be set to one less than the sequence length or lambda_max, whichever is smaller |
Nan Xiao, Dong-Sheng Cao, Min-Feng Zhu, and Qing-Song Xu. (2015). protr/ProtrWeb: R package and web server for generating various numerical representation schemes of protein sequences. Bioinformatics 31 (11), 1857-1859.
This function calculates set physicochemical and compositional features from protein sequences in preparation for supervised model learning
calculate_features(df, min_len = 10)calculate_features(df, min_len = 10)
df |
A dataframe which contains protein sequence names as the first column and amino acid sequence as the second column |
min_len |
Minimum length sequence for which features can be calculated. It is an error to provide sequences with length shorter than this |
A dataframe containing numerical values related to the protein features of each given protein
This function depends on the Peptides package
Osorio, D., Rondon-Villarreal, P. & Torres, R. Peptides: A package for data mining of antimicrobial peptides. The R Journal. 7(1), 4–14 (2015).
my_protein_df <- read_faa(system.file("extdata/bat_protein.fasta", package = "ampir")) calculate_features(my_protein_df) ## Output (showing the first six output columns) # seq_name Amphiphilicity Hydrophobicity pI Mw Charge .... # [1] G1P6H5_MYOLU 0.4145847 0.4373494 8.501312 9013.757 4.53015 ....my_protein_df <- read_faa(system.file("extdata/bat_protein.fasta", package = "ampir")) calculate_features(my_protein_df) ## Output (showing the first six output columns) # seq_name Amphiphilicity Hydrophobicity pI Mw Charge .... # [1] G1P6H5_MYOLU 0.4145847 0.4373494 8.501312 9013.757 4.53015 ....
Determine row breakpoints for dividing a dataset into chunks for parallel processing
chunk_rows(nrows, n_cores)chunk_rows(nrows, n_cores)
nrows |
The number of rows in the dataset to be chunked |
n_cores |
The number of cores that will be used for parallel processing |
A list of integer vectors consisting of the rows in each chunk
This function writes a dataframe out as a FASTA format file
df_to_faa(df, file = "")df_to_faa(df, file = "")
df |
a dataframe containing two columns: the sequence name and amino acid sequence itself |
file |
file path to save the named file to |
A FASTA file where protein sequences are represented in two lines: The protein name preceded by a greater than symbol, and a new second line that contains the protein sequence
my_protein <- read_faa(system.file("extdata/bat_protein.fasta", package = "ampir")) # Write a dataframe to a FASTA file df_to_faa(my_protein, tempfile("my_protein.fasta", tempdir()))my_protein <- read_faa(system.file("extdata/bat_protein.fasta", package = "ampir")) # Write a dataframe to a FASTA file df_to_faa(my_protein, tempfile("my_protein.fasta", tempdir()))
This function predicts the probability of a protein to be an antimicrobial peptide
predict_amps(faa_df, min_len = 5, n_cores = 1, model = "precursor")predict_amps(faa_df, min_len = 5, n_cores = 1, model = "precursor")
faa_df |
A dataframe obtained from |
min_len |
The minimum protein length for which predictions will be generated |
n_cores |
On multicore machines split the task across this many processors. This option does not work on Windows |
model |
Either a string with the name of a built-in model (mature, precursor), OR, A train object suitable for passing to the predict.train function in the caret package. If omitted the default model will be used. |
The original input data.frame with a new column added called prob_AMP with the probability of that sequence to be an antimicrobial peptide. Any sequences that are too short or which contain invalid amin acids will have NA in this column
my_bat_faa_df <- read_faa(system.file("extdata/bat_protein.fasta", package = "ampir")) predict_amps(my_bat_faa_df) # seq_name prob_AMP # [1] G1P6H5_MYOLU 0.9723796my_bat_faa_df <- read_faa(system.file("extdata/bat_protein.fasta", package = "ampir")) predict_amps(my_bat_faa_df) # seq_name prob_AMP # [1] G1P6H5_MYOLU 0.9723796
This function reads a FASTA amino acids file into a dataframe
read_faa(file = NULL)read_faa(file = NULL)
file |
file path to the FASTA format file containing the protein sequences |
Dataframe containing the sequence name (seq_name) and sequence (seq_aa) columns
This function was adapted from 'read.fasta.R' by Jinlong Zhang ([email protected]) for the phylotools package (http://github.com/helixcn/phylotools)
read_faa(system.file("extdata/bat_protein.fasta", package = "ampir")) ## Output # seq_name seq_aa # [1] G1P6H5_MYOLU MALTVRIQAACLLLLLLASLTSYSL....read_faa(system.file("extdata/bat_protein.fasta", package = "ampir")) ## Output # seq_name seq_aa # [1] G1P6H5_MYOLU MALTVRIQAACLLLLLLASLTSYSL....
This function removes anything that is not one of the 20 standard amino acids in protein sequences
remove_nonstandard_aa(df)remove_nonstandard_aa(df)
df |
A dataframe which contains protein sequence names as the first column and amino acid sequence as the second column |
a dataframe like the input dataframe but with removed proteins that contained non standard amino acids
non_standard_df <- readRDS(system.file("extdata/non_standard_df.rds", package = "ampir")) # non_standard_df # seq_name seq_aa # [1] G1P6H5_MYOLU MALTVRIQAACLLLLLLASLTSYSLLLSQTTQLADLQTQ.... # [2] fake_sequence MKVTHEUSYR$GXMBIJIDG*M80-% remove_nonstandard_aa(non_standard_df) # seq_name seq_aa # [1] G1P6H5_MYOLU MALTVRIQAACLLLLLLASLTSYSLLLSQTTQLADLQTQ....non_standard_df <- readRDS(system.file("extdata/non_standard_df.rds", package = "ampir")) # non_standard_df # seq_name seq_aa # [1] G1P6H5_MYOLU MALTVRIQAACLLLLLLASLTSYSLLLSQTTQLADLQTQ.... # [2] fake_sequence MKVTHEUSYR$GXMBIJIDG*M80-% remove_nonstandard_aa(non_standard_df) # seq_name seq_aa # [1] G1P6H5_MYOLU MALTVRIQAACLLLLLLASLTSYSLLLSQTTQLADLQTQ....
Stop codons at the end of the amino acid sequences are removed
remove_stop_codon(faa_df)remove_stop_codon(faa_df)
faa_df |
A dataframe containing two columns: the sequence name and amino acid sequence |
The input dataframe without the stop codons at the end of sequences