This is a repo for the Machine Learning Immunogenicity Team in the August 2016 NCBI Hackathon
This project looks into the application of Machine Learning (ML) techniques in the prediction of Immunogenicity (Categorical; Positive or Negative) based on a peptide and its associated amino acid properties. This study uses peptide data from the Immune Epitope Database (IEDB). The R package "Peptides" has been used to compute the amino acid properties and mashup with peptide data to enable the use of ML algorithms for immunogenicity analysis, particularly, the algorithms that are more efficient with numeric and categorical data instead of string sequence.
Tensorflow is an open source software library ML that provides linear regression and classification algorithms (open sourced by Google in Nov 2015) for multi-dimensional arrays (aka “Tensors”). K-fold cross-validation as well as hold-out of test data was used to train and test the generated models.
Initial application of Logistic Regression (LR) and Neural Networks (NN) looks promising with approximately 82% and 90% predictive accuracy respectively. Note: Further cross-validation and rigorous analysis needs to be performed to validate these performance metrics. Various other ML algorithms such as variants of Neural Networks such as Convoluted NN, RESNET, MUST-CNN as well as Random Forest, Bayesian Networks should be considered as part of future work.
The following are provided:
- R scripts for data wrangling of IEDB data and mashup with Amino Acid properties
- Python Notebook for application of Logistic Regression and Neural Networks using Tensorflow
- Python script for computing binding affinities using several published approaches based on several datasets
As part of initial results, the convergence of predictive accuracy for Neural Network is presented below.
- Tiny.Molecular.Percent
- Small.Molecular.Percent
- Aliphatic.Molecular.Percent
- Aromatic.Molecular.Percent
- Polar.Molecular.Percent
- Charged.Molecular.Percent
- Basic.Molecular.Percent
- Acidic.Molecular.Percent
- Peptide.Aliphatic.Index
- Peptide.Boman
- Peptide.Charge
- Peptide.hmoment
- Peptide.hydrophobicity
- Peptide.instaindex
- Peptide.Kidera.helix.bend.pref
- Peptide.Kidera.side.chain.size
- Peptide.Kidera.extended.str.pref
- Peptide.Kidera.hydrophobicity
- Peptide.Kidera.double.bend.pref
- Peptide.Kidera.partial.spec.vol
- Peptide.Kidera.flat.ext.pref
- Peptide.Kidera.occurrence.alpha.reg
- Peptide.Kidera.pK.C
- Peptide.Kidera.surrounding.hydrop
- Peptide.mw
- Peptide.pI
This script allows to run published epitope binding predictors on immunogenicity data from IEDB T-cell and MHC assays (http://www.iedb.org/), as well as data from IMMA2. This script can be used to test whether binding predictions also predict immugenicity (Note: they don't). We use the data interface implemented in the pepdata package from the Hammer Lab (https://github.com/hammerlab/pepdata) and the implementations of various predictors in the Fred 2 framework for computational immunogenomics by Schubert et al. (https://github.com/FRED-2/Fred2 and http://bioinformatics.oxfordjournals.org/content/32/13/2044).
usage: run-data-on-predictor.py [-h] --predictor PREDICTOR --dataset DATASET
[-n N] [--allele ALLELE]
Call epitope predictors on data.
optional arguments:
-h, --help show this help message and exit
-n N Number of rows to take from dataset
--allele ALLELE Allelle
required arguments:
--predictor PREDICTOR
Epitope predictors [see all with --predictor=list]
--dataset DATASET Immunogenic dataset [see all with --dataset=list]
run-data-on-predictor.py --predictor list --dataset list
Set one of the predictors with --predictor: ['smmpmbec', 'syfpeithi', 'netctlpan', 'smm', 'tepitopepan', 'netmhcii', 'arb', 'pickpocket', 'epidemix', 'unitope', 'netmhciipan', 'comblibsidney', 'netmhcpan', 'calisimm', 'hammer', 'svmhc', 'bimas']
Details from https://bioinformatics.oxfordjournals.org/content/suppl/2016/02/26/btw113.DC1/S1.pdf
| Method | Type | Publication |
|---|---|---|
| SYFPEITHI | T-cell epitope | (Rammensee, et al., 1999) |
| BIMAS | MHC-I binding | (Parker, et al., 1994) |
| SVMHC | MHC-I binding | (Dönnes and Elofsson, 2002) |
| ARB | MHC-I binding | (Bui, et al., 2005) |
| SMM | MHC-I binding | (Peters and Sette, 2005) |
| SMMPMBEC | MHC-I binding | (Kim, et al., 2009) |
| Epidemix | MHC-I binding | (Feldhahn, et al., 2009) |
| Comblib | MHC-I binding | (Sidney, et al., 2008) |
| PickPocket* | MHC-I binding | (Zhang, et al., 2009) |
| NetMHC* | MHC-I binding | (Lundegaard, et al., 2008) |
| NetMHCpan* | MHC-I binding | (Hoof, et al., 2009) |
| HAMMER | MHC-II binding | (Sturniolo, et al., 1999) |
| TEPITOPEpan | MHC-II binding | (Zhang, et al., 2012) |
| NetMHCII* | MHC-II binding | (Nielsen, et al., 2007) |
| NetMHCIIpan* | MHC-II binding | (Karosiene, et al., 2013) |
| UniTope | T-cell epitope | (Toussaint, et al., 2011) |
| NetCTLpan* | T-cell epitope | (Stranzl, et al., 2010) |
available datasets: iedb.tcell, ideb.mhc, imma2
python -W ignore src/run-data-on-predictor.py --predictor all --dataset iedb.mhc -n 10
smmpmbec immunogenic peptides
A*01:01 A*02:01 B*15:01
Seq Method
(A, T, F, S, V, P, M, E, K) smmpmbec 17077.331150 15646.960997 71277.096461
(E, V, M, P, V, S, M, A, K) smmpmbec 51691.602975 56419.599637 20603.926881
(K, L, E, D, L, E, R, D, L) smmpmbec 76281.636225 2096.186888 72435.256110
(K, T, F, P, P, T, E, P, K) smmpmbec 50748.124892 51455.286421 13550.333994
(L, I, T, G, R, L, Q, S, L) smmpmbec 47360.905813 1504.630981 3147.385937
A*01:01 A*02:01 B*15:01
count 5.000000 5.000000 5.000000
mean 48631.920211 25424.532985 36202.799877
std 21069.256898 26693.953616 33136.522923
min 17077.331150 1504.630981 3147.385937
25% 47360.905813 2096.186888 13550.333994
50% 50748.124892 15646.960997 20603.926881
75% 51691.602975 51455.286421 71277.096461
max 76281.636225 56419.599637 72435.256110