epitopepredict API basic usage


Predictors for each method inherit from the Predictor class and all implement a predict method for scoring a single sequence. This may wrap methods from other modules and/or call command line predictors. For example the TepitopePredictor uses the mhcpredict.tepitope module. This method should return a Pandas DataFrame. The predictProteins method is used for multiple proteins contained in a dataframe of sequences in a standard format. This is created from a genbank or fasta file (see examples below). For large numbers of sequences predictProteins you should provide a path so that the results are saved as each protein is completed to avoid memory issues, since many alleles might be called for each protein. Results are saved with one file per protein in csv format. Results can be loaded into the predictor individually or all together using the load method.

In [1]:
import os, math, time, pickle, subprocess
from importlib import reload
from collections import OrderedDict
import numpy as np
import pandas as pd
pd.set_option('display.width', 100)
import epitopepredict as ep
from epitopepredict import sequtils, tepitope, plotting, utilities, peptutils
from IPython.display import display, HTML, Image
%matplotlib inline
import matplotlib as mpl
import pylab as plt

In [2]:
#get preset alleles
m2_alleles = ep.get_preset_alleles('mhc2_supertypes')
m1_alleles = ep.get_preset_alleles('mhc1_supertypes')
print (m1_alleles)
print (m2_alleles)

['HLA-A*01:01', 'HLA-A*02:01', 'HLA-A*03:01', 'HLA-A*24:02', 'HLA-B*07:02', 'HLA-B*44:03']
['HLA-DRB1*01:01', 'HLA-DRB1*03:01', 'HLA-DRB1*04:01', 'HLA-DRB1*07:01', 'HLA-DRB1*08:01', 'HLA-DRB1*11:01', 'HLA-DRB1*13:01', 'HLA-DRB1*15:01']

predict random peptides

In [3]:
seqs = peptutils.create_random_sequences(10)
print (seqs[:5])
df = pd.DataFrame(seqs,columns=['peptide'])
P = ep.get_predictor('basicmhc1')
b = P.predict_peptides(df.peptide, alleles=m1_alleles, show_cmd=True, cpus=1)


results are returned as dataframes, sorted by score

In [4]:
print (b[:5])

     peptide  pos  log50k     score  name       allele  rank
0  FNYSGMGSY    3  0.3656    957.25  temp  HLA-A*01:01   1.0
1  ILGMLVIRA    6  0.1113  14995.97  temp  HLA-A*01:01   2.0
2  VKMVVEWMS    1  0.1049  16071.19  temp  HLA-A*01:01   3.0
3  PHKDEAIYS    0  0.1042  16193.37  temp  HLA-A*01:01   4.0
4  TAICDLPVI    4  0.0928  18319.16  temp  HLA-A*01:01   5.0

predict n-mers from a protein sequence

This example loads protein sequences from mycobacterium tuberculosis from a genbank file

In [8]:
#load protein sequences into a dataframe
prots = ep.genbank_to_dataframe('../MTB-H37Rv.gb',cds=True)

type protein_id locus_tag gene db_xref product note translation pseudo start end strand length order
2 CDS CCP42723.1 Rv0001 dnaA GI:444893470 Chromosomal replication initiator protein DnaA Rv0001, (MT0001, MTV029.01, P49993), len: 507 ... MTDDPGSGFTTVWNAVVSELNGDPKVDDGPSSDANLSAPLTPQQRA... NaN 0 1524 1 507 1
4 CDS CCP42724.1 Rv0002 dnaN GI:444893471 DNA polymerase III (beta chain) DnaN (DNA nucl... Rv0002, (MTV029.02, MTCY10H4.0), len: 402 aa. ... MDAATTRVGLTDLTFRLLRESFADAVSWVAKNLPARPAVPVLSGVL... NaN 2051 3260 1 402 2
6 CDS CCP42725.1 Rv0003 recF GI:444893472 DNA replication and repair protein RecF (singl... Rv0003, (MTCY10H4.01), len: 385 aa. RecF, DNA ... MYVRHLGLRDFRSWACVDLELHPGRTVFVGPNGYGKTNLIEALWYS... NaN 3279 4437 1 385 3
8 CDS CCP42726.1 Rv0004 NaN GI:444893473 Conserved hypothetical protein Rv0004, (MTCY10H4.02), len: 187 aa. Conserved ... MTGSVDRPDQNRGERSMKSPGLDLVRRTLDEARAAARARGQDAGRG... NaN 4433 4997 1 187 4
10 CDS CCP42727.1 Rv0005 gyrB GI:444893474 DNA gyrase (subunit B) GyrB (DNA topoisomerase... Rv0005, (MTCY10H4.03), len: 675 aa. GyrB, DNA ... MAAQKKKAQDEYGAASITILEGLEAVRKRPGMYIGSTGERGLHHLI... NaN 5239 7267 1 675 5

In [9]:
P = ep.get_predictor('tepitope')
mb_binders = P.predict_sequences(prots[:20], alleles=m2_alleles, cpus=1)

predictions done for 20 sequences in 8 alleles

In [10]:

peptide core pos score name allele rank
174 DTFVIGASNRF FVIGASNRF 174 2.00 Rv0001 HLA-DRB1*0101 1.0
175 TFVIGASNRFA FVIGASNRF 175 2.00 Rv0001 HLA-DRB1*0101 1.0
176 FVIGASNRFAH FVIGASNRF 176 2.00 Rv0001 HLA-DRB1*0101 1.0
45 AWLNLVQPLTI LNLVQPLTI 45 1.55 Rv0001 HLA-DRB1*0101 4.0
46 WLNLVQPLTIV LNLVQPLTI 46 1.55 Rv0001 HLA-DRB1*0101 4.0

get promiscuous binders - those peptides above the cutoff for at least 3 alleles

In [5]:
pb = P.promiscuous_binders(n=3)

(0, 8)

In [11]:
print (pb[:3])

Empty DataFrame
Columns: [peptide, pos, name, alleles, core, score, mean, median_rank]
Index: []

plot binders in a protein sequence

In [12]:
#get names of proteins stored in results of predictor
print (P.get_names())
ax = plotting.plot_tracks([P],name='Rv0011c',cutoff=.94,n=2)
ax = plotting.plot_binder_map(P,name='Rv0011c',cutoff=10)

['Rv0001', 'Rv0002', 'Rv0003', 'Rv0004', 'Rv0005', 'Rv0006', 'Rv0007', 'Rv0008c', 'Rv0009', 'Rv0010c', 'Rv0011c', 'Rv0012', 'Rv0013', 'Rv0014c', 'Rv0015c', 'Rv0016c', 'Rv0017c', 'Rv0018c', 'Rv0019c', 'Rv0020c']
Rv0011c tepitope 82 11 93
<Figure size 432x288 with 0 Axes>

In [13]:
from bokeh.io import show, output_notebook
p = plotting.bokeh_plot_tracks([P],name='Rv0011c',cutoff=.95,n=2,width=800)

Loading BokehJS ...
/usr/local/lib/python3.6/dist-packages/bokeh/core/json_encoder.py:80: FutureWarning: Conversion of the second argument of issubdtype from `float` to `np.floating` is deprecated. In future, it will be treated as `np.float64 == np.dtype(float).type`.
  elif np.issubdtype(type(obj), np.float):

In [ ]: