DeepTrio: a ternary prediction system for protein–protein interaction using mask multiple parallel convolutional neural networks

          

Updates

2022-11-09: v1.0.2 - v1.0.3: fix some BUGs in build_model_for_hyperparameter_search.py, and change the command-line parameters for inputting data. model.py is renamed as build_model.py
2022-05-11: v1.0.1 - v1.0.2: fix some BUGs in model.py, and change the command-line parameters for inputting data.
2021-11-09: Note for article: the title of Section 2.2.1 should be 'Protein sequence representation', and the reference in the footnote of Table 4 should be Chen et al. (2019).
2021-09-03: v1.0.0 - v1.0.1: adding an alternative function for applying max-pooling on the outer-product of two protein feature maps.

Overview

Installation

It is recommended to install dependencies in conda virtual environment so that only few installation commands are required for running DeepTrio. You can prepare all the dependencies just by the following commands.

• Install Miniconda

  Miniconda is a free minimal installer for conda. It is a small, bootstrap version of Anaconda that includes only conda, Python, the packages they depend on, and a small number of other useful packages, including pip, zlib and a few others

  1. Download Miniconda installer for linux : https://docs.conda.io/en/latest/miniconda.html#linux-installers
  2. Check the hashes for the Miniconda from : https://docs.conda.io/en/latest/miniconda_hashes.html
  3. Go to the installation directory and run command : bash Miniconda3-latest-Linux-x86_64.sh

• Creating the environment

  If there is no environment in your Miniconda environment, it is recommeneded to create a new environment to run DeepTrio.

  1. Run conda create -n [your env name] python=3.7
  2. Run conda activate [your env name]
  3. Run conda install tensorflow-gpu==2.1
  4. Run conda install seaborn
  5. Run conda install -c conda-forge scikit-learn
  6. Run conda install -c conda-forge gpyopt
  7. Run conda install -c conda-forge dotmap

Run DeepTrio for Training

1. To run DeepTrio on your own training data you need to prepare the following two things:

   • Protein-protein Interaction File: A pure protein ID file, in which two protein IDs are separated by the Tab key, along with their label (1 for 'interacting', and 0 for 'non-interacting'). This file must be named as [(your customized name).pair.tsv].

     line1:    protein_id_1  [Tab]  protein_id_2  [Tab]  label
     line2:    protein_id_3  [Tab]  protein_id_4  [Tab]  label

   • Protein Sequence Database File: A file containing protein IDs and their sequences in fasta format, which are separated by the Tab key. This file must be named as [(your customized name).seq.tsv].

     line1:    protein_id_1  [Tab]  protein_1_sequence  
     line2:    protein_id_3  [Tab]  protein_2_sequence

2. Execute command with arguments in shell:

   python build_model.py [-h] [--interaction_data INTERACTION_DATA] [--sequence_data SEQUENCE_DATA] [--fold_index FOLD_INDEX]
                        [--epoch EPOCH] [--outer_product OUTER_PRODUCT] [--cuda]

   for example:

   python build_model.py --interaction_data data/benchmarks/yeast\ core\ dataset\ from\ DeepFE-PPI/action_pair.tsv --sequence_data data/benchmarks/yeast\ core\ dataset\ from\ DeepFE-PPI/action_dictionary.tsv

   Arguments:

   Argument	Required	Default	Description
   --interaction_data	Yes		The customized name of your Protein-protein Interaction File with its path
   --sequence_data	Yes		The customized name of your Protein Sequence Database File with its path
   --fold_index	No	0	The fold index in 5-fold cross-validation
   --outer_product	No	False	Whether apply max-pooling on outer-product of two proteins
   --epoch	No	50	The maximum number of epochs
   --cuda	No	False	Allow GPU to perform training process
   --help	No		Help message

Run DeepTrio for hyper-parameter searching

1. To run DeepTrio on your own training data and search hyper-parameters, you need to prepare the following two things:

   • Protein-protein Interaction File: A pure protein ID file, in which two protein IDs are separated by the Tab key, alonge with their label (1 for 'interacting', 0 for 'non-interacting' and 2 for 'single protein'). This file must be named as [(your customized name).pair.tsv]. For example:

     line1:    protein_id_1  [Tab]  protein_id_2  [Tab]  label
     line2:    protein_id_3  [Tab]  protein_id_4  [Tab]  label

   • Protein Sequence Database File: A file containing protein IDs and their sequences in fasta format, which are separated by the Tab key. This file must be named as [(your customized name).seq.tsv]. For example:

     line1:    protein_id_1  [Tab]  protein_1_sequence  
     line2:    protein_id_3  [Tab]  protein_2_sequence

2. Execute command with arguments in shell:

   python build_model_for_hyperparameter_search.py [-h] [--interaction_data INTERACTION_DATA] [--sequence_data SEQUENCE_DATA]
                                                   [--epoch EPOCH] [--outer_product OUTER_PRODUCT] [--cuda]

   for example:

   python build_model_for_hyperparameter_search.py --interaction_data data/benchmarks/yeast\ core\ dataset\ from\ DeepFE-PPI/action_pair.tsv --sequence_data data/benchmarks/yeast\ core\ dataset\ from\ DeepFE-PPI/action_dictionary.tsv --cuda

   Arguments:

   Argument	Required	Default	Description
   --interaction_data	Yes		The customized name of your Protein-protein Interaction File with its path
   --sequence_data	Yes		The customized name of your Protein Sequence Database File with its path
   --epoch	No	100	The maximum number of epochs
   --cuda	No	False	Allow GPU to perform training process
   --help	No		Help message

3. Select the best model according to GpyOpt log file:

   DeepTrio_search_1.h5
   DeepTrio_search_2.h5
   DeepTrio_search_3.h5
   DeepTrio_search_4.h5
   ...
   search_log.txt

   • The search_log.txt shows the details of all the candidate models' parameters and the best model parameters.
   
   

   result: 
       parameter   em_dim:         15.0
       parameter   sp_drop:        0.005
       parameter   kernel_rate_1:  0.16
       ...
       evaluation: 0.9795729

Run DeepTrio for Prediction

1. To run DeepTrio for prediction on your own query protein pairs you need to prepare the following three things:

   • The first protein File: It can contain multiple proteins in fasta format. For example:

     line1:    >protein_id_1
     line2:    protein_1_sequence
     line3:    >protein_id_2
     line4:    protein_2_sequence

   • The second protein File: It can contain multiple proteins in fasta format. For example:

     line1:    >protein_id_3
     line2:    protein_3_sequence

   • The model file name and its path.

   • The inputs of DeepTrio will be:

     the first query protein pair:   protein_1 and protein_3
     the second query protein pair:  protein_2 and protein_3

2. Execute command with arguments in shell:

   python main.py [-h] -p1 PROTEIN1 -p2 PROTEIN2 -m MODEL [-o OUTPUT]

   Arguments:

   Abbreviation	Argument	Required	Description
   -p1	--protein1	Yes	The first protein group in fasta format with its path
   -p2	--protein2	Yes	The second protein group in fasta format with its path
   -m	--model	Yes	The DeepTrio model with its path
   -o	--output	No	The output file name
   -h	--help	No	Help message

Run DeepTrio for Visualization

1. To run DeepSol for visualization on your own query protein pairs you need to prepare the following three things:

   • The first protein File: which must contain only one protein in fasta format. For example:

     line1:    >protein_id_1
     line2:    protein_1_sequence

   • The second protein File: which must contain only one protein, like the first protein File.

   • The model file name and its path.

2. Execute command with arguments in shell:

   python visual_DeepTrio.py [-h] -p1 PROTEIN1 -p2 PROTEIN2 -m MODEL

   Arguments:

   Abbreviation	Argument	Required	Description
   -p1	--protein1	Yes	The first protein group in fasta format with its path
   -p2	--protein2	Yes	The second protein group in fasta format with its path
   -m	--model	Yes	The DeepTrio model with its path
   -h	--help	No	Help message

FAQ

1. Can I use pip to install the environment dependencies?

A) Yes, you need to install some addtional libraries, like GPU drivers, matplotlib, numpy, Gpy and so on, so we recommend to use conda to install dependencies.

2. Can DeepTrio run on Windows?

A) Yes, you can configure conda virtual environment on your Windows PC.

3. If I am not good at using Unix software, is there any conventient way to use DeepTrio?

A) Yes, you can visit our online website : http://bis.zju.edu.cn/deeptrio, where you can predicti PPIs and draw importance maps on the DeepTrio model without any configurations.

Citation

If you find DeepTrio useful, please consider citing our publication:

Hu, X., Feng, C., Zhou, Y., Harrison, A., & Chen, M. (2021). DeepTrio: a ternary prediction system for protein-protein interaction using mask multiple parallel convolutional neural networks. Bioinformatics, btab737.