A published version is here: https://github.com/enai4bio/DeepAAI

A Novel Deep Learning Method for Identifying Antigen-Antibody Interactions (DeepAAI)

DeepAAI is an advanced deep learning-based tool for identifying antigen-antibody interactions. We devise an automatically learned virtual graph to address antibodies’ high variability. The virtual graph connects seen and unseen antibodies by quantitating functional similarity based on the supervised signals from two downstream tasks: binary neutralization prediction and IC50 estimation.

We provided clear instructions on installing and running the program with the dataset specified the software and hardware requirements and exposed the modifiable settings as input parameters.

For making DeepAAI available at no cost to the community, we have set a web service predicting antigen-antibody interactions. https://aai-test.github.io/

Architecture

Installation

pip install -r requirements.txt

Usage

Execute the following scripts to predict the probability of antigen-antibody binding

# load parameters for evaluation
python deep_aai_kmer_embedding_cls_evaluate.py --infile test_data.csv --outfile pred_result.csv

Key options of this scrips:

• infile: CSV file to be predicted (default=test_data.csv).

virus_seq	heavy_seq	light_seq	label_10
MRVTGIRRNCRH...	QKQLVESGGGVV...	QSVLTQPPSVSA...	1 or 0

     If input file not contain 'label_10' column, the evaluation of prediction results will be skipped.
 

• outfile: Prediction results (default=pred_result.csv)

Descriptions

The most important files in this projects are as follow:

• dataset:
  • abs_dataset_cls.py: The object for load the HIV classification data set.
  • k_mer_utils.py: Create K-mer feature.
• baseline_trainer: Train scrips for DeepAAI and some baselines.
• models: Implementation of DeepAAI and all baselines.
• save_model_param_pred: Saved model parameters.

Training

Execute the following scripts to train antigen-antibody binding model.

python baseline_trainer/deep_aai_kmer_embedding_cls_trainer.py --mode train

Hyper-parameter in DeepAAI:

Parameter	Value
Dropout	0.4
Adj L1 loss	5e-4
Param L2 loss	5e-4
CNN kernel	7, 9, 11
CNN channel	256
Amino embedding size	7
Hidden size	512
Learning rate	5e-5