defense-predictor-2 0.0.1

Python module for running Defense Predictor 2, a machine learning model to predict antiphage defense systems

DefensePredictor2: A Machine Learning Model to Discover Novel Prokaryotic Immune Systems

Python package to run DefensePredictor2, a machine-learning model that leverages embeddings from a protein language model, ESM2, to classify proteins as anti-phage defensive.

This repo is for the second version of DefensePredictor, which was trained with larger ESM2 embeddings and an updated training set. This model has not been experimentally tested, so use with caution.

For additional details, read the original DefensePredictor paper here.

Installation

In a fresh conda or other virutal environment, run:

pip install defense_predictor_2
defense_predictor_2_download

The first command downloads the python package from PyPI and the second downloads the model weights. Once model weights are downloaded you do not need to run this command again.

Requirements

Requires python >= 3.10

Usage

defense_predictor_2 can be run as python code

import defense_predictor_2 as dfp

ncbi_feature_table = 'GCF_003333385.1_ASM333338v1_feature_table.txt'
ncbi_cds_from_genomic = 'GCF_003333385.1_ASM333338v1_cds_from_genomic.fna'
ncbi_protein_fasta = 'GCF_003333385.1_ASM333338v1_protein.faa'
output_df, feature_matrix = dfp.defense_predictor(ft_file=ncbi_feature_table, fna_file=ncbi_cds_from_genomic, faa_file=ncbi_protein_fasta)
output_df.head()                                    

Or from the command line

defense_predictor_2 \
     --ncbi_feature_table GCF_003333385.1_ASM333338v1_feature_table.txt \
     --ncbi_cds_from_genomic GCF_003333385.1_ASM333338v1_cds_from_genomic.fna \ 
     --ncbi_protein_fasta GCF_003333385.1_ASM333338v1_protein.faa \
     --output GCF_003333385_defense_predictor_output.csv

Alternatively, defense_predictor_2 can take a single GFF3 file with embedded genomic FASTA:

output_df, feature_matrix = dfp.defense_predictor(gff='annot_with_genomic_fasta.gff')

defense_predictor_2 \
     --gff annot_with_genomic_fasta.gff \
     --output defense_predictor_2_output.csv

When given a GFF, defense_predictor_2 translates proteins from the embedded genomic sequence using the bacterial codon table (transl_table=11) and uses each CDS's locus_tag as its identifier.

defense_predictor_2 outputs the predicted log-odds of defense for each input protein in the columns mean_log_odds. We reccomend using a stringent log-odds cutoff of 4 to call a protein predicted defensive.

To see an example you can run the defense_predictor_2_example.ipynb in colab:

We reccomend running defense_predictor_2 on a computer with a cuda-enabled GPU, to maximize computational efficiency.

Inputs

The NCBI input files can be downloaded from the ftp webpage for any gemone of interest, which is linked on its assembly page.

For an unannotated nucleotide assembly, run NCBI's Prokaryotic Genome Annotation Pipeline (PGAP) or prokka and pass its *.gff output directly via --gff.