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DeeProtein

DOI DOI

Github repository

Code ocean compute capsule

Software for "Leveraging Implicit Knowledge in Neural Networks for Functional Dissection and Engineering of Proteins"

Online Mode

With just a few clicks, you can run DeeProtein online in our code ocean compute capsule. To do so, you need to sign up and duplicate this capsule.

This compute capsule includes two modes:

For both modes click Start Interactive Session choose Juypter. In the dropdown menu New on the righthand side choose Terminal.

Classification

Wait until the terminal opens and enter

bash /code/infer.sh

to start the inference mode.

Example:

A prompt will open where you can enter a protein sequence, e.g the sequence of Src-kinase kinase (PDB 6F3F)

Enter Sequence for Classification:

GARMVTTFVALYDYESRTETDLSFKKGERLQIVNNTEGDWWLAHSLSTGQTGYIPSNYVAPSDSIQAEEWYFGKITRRESERLLLNAENPRGTFLVRESETTKGAYCLSVSDFDNAKGLNVKHYKIRKLDSGGFYITSRTQFNSLQQLVAYYSKHADGLCHRLTTVCPTSKPQTQGLAKDAWEIPRESLRLEVKLGQGCFGEVWMGTWNGTTRVAIKTLKPGTMSPEAFLQEAQVMKKLRHEKLVQLYAVVSEEPIYIVTEYMNKGSLLDFLKGETGKYLRLPQLVDMSAQIASGMAYVERMNYVHRDLRAANILVGENLVCKVADFGLARLIEDNEYTARQGAKFPIKWTAPEAALYGRFTIKSDVWSFGILLTELTTKGRVPYPGMVNREVLDQVERGYRMPCPPECPESLHDLMCQCWRKEPEERPTFEYLQAFLEDYFTSTEPQYQPGENL

For which the result looks like this:

Predicted labels:
-----------------

GO-Term         Score   Explanation
GO:0003674      1.000   molecular_function
GO:0043168      1.000   anion binding
GO:0004672      1.000   protein kinase activity
GO:0035639      1.000   purine ribonucleoside triphosphate binding
GO:0005524      1.000   ATP binding
GO:0016773      1.000   phosphotransferase activity, alcohol group as acceptor
GO:0032559      1.000   adenyl ribonucleotide binding
GO:0032555      1.000   purine ribonucleotide binding
GO:0017076      1.000   purine nucleotide binding
GO:0032553      1.000   ribonucleotide binding
GO:0016301      1.000   kinase activity
GO:0016772      1.000   transferase activity, transferring phosphorus-containing groups
GO:0000166      1.000   nucleotide binding
GO:1901265      1.000   nucleoside phosphate binding
GO:0036094      1.000   small molecule binding
GO:0097367      1.000   carbohydrate derivative binding
GO:0030554      1.000   adenyl nucleotide binding
GO:0005488      1.000   binding
GO:0003824      1.000   catalytic activity
GO:1901363      1.000   heterocyclic compound binding
GO:0097159      1.000   organic cyclic compound binding
GO:0043167      1.000   ion binding
GO:0016740      1.000   transferase activity
--------------------------------------------------
GO:0004674      0.373   protein serine/threonine kinase activity
GO:0016787      0.012   hydrolase activity
GO:0004871      0.002   signal transducer activity

Scores below 0.5 are interpreted as negative predictions. Only non-zero scores are shown.

Use CTRL-C to leave the interactive mode, for example when running a sensitivity analysis next.

Sensitivity Analysis

Wait until the terminal opens and enter

bash /code/sense.sh

to start the sensitivity analysis mode.

Example:

A prompt will open where you can enter a protein sequence, e.g the sequence of Src-kinase kinase (PDB 6F3F)

Please enter the sequence to analyze:

GARMVTTFVALYDYESRTETDLSFKKGERLQIVNNTEGDWWLAHSLSTGQTGYIPSNYVAPSDSIQAEEWYFGKITRRESERLLLNAENPRGTFLVRESETTKGAYCLSVSDFDNAKGLNVKHYKIRKLDSGGFYITSRTQFNSLQQLVAYYSKHADGLCHRLTTVCPTSKPQTQGLAKDAWEIPRESLRLEVKLGQGCFGEVWMGTWNGTTRVAIKTLKPGTMSPEAFLQEAQVMKKLRHEKLVQLYAVVSEEPIYIVTEYMNKGSLLDFLKGETGKYLRLPQLVDMSAQIASGMAYVERMNYVHRDLRAANILVGENLVCKVADFGLARLIEDNEYTARQGAKFPIKWTAPEAALYGRFTIKSDVWSFGILLTELTTKGRVPYPGMVNREVLDQVERGYRMPCPPECPESLHDLMCQCWRKEPEERPTFEYLQAFLEDYFTSTEPQYQPGENL

and the GO-terms of interest in comma-seperated form, e.g.

Please enter the GO terms to analyze sperarated by commas:

GO:0016301,GO:0005524

The sensitivity analysis can take a few minutes. Results will be written to a tab-separated .txt file and a plot in .png format. To view and download these, click on the 'Jupyter' logo and move to the directory 'results/'.

There you will find the sensitivities.txt, e.g.

Pos	AA	sec	dis	GO:0016301	GO:0005524
-1	wt	wt	wt	0.0	0.0
0	G	.	_	0.6929230000000004	0.1063629999999982
1	A	.	_	0.01167100000000332	-0.12102099999999892
2	R	.	_	1.0941490000000016	-0.7712800000000009
3	M	.	_	0.9523300000000036	0.6096879999999985
4	V	.	_	1.514687000000002	1.8048670000000016
5	T	.	_	-0.01615099999999714	0.1625770000000024
6	T	.	_	-0.09454199999999703	0.202874999999998
...

Note: The output has more than 300 lines and was truncated here.

Each Line contains information about one position in the protein sequence. Its index is given under Pos and the respective amino acid under AA. For each GO term GO:_______, there is one column containing the sensitivity value in that position for that GO term. The second line and the third and fourth columns ("sec", "dis") are for technical reasons and can be ignored.

Note Sequences far off the protein sequence space as covered by SwissProt might give unexpected results. The same applies to very short sequences since DeeProtein was trained on sequences with more than 150 amino acids.

Additionally, a plot of the sensitivity values along the sequence is produces and saves as sensitivities.png, e.g.

<img src="examples/sensitivity_1D.png">

N-terminus enlarged: <img src="examples/sensitivity_1D_short.png">

You can download these by marking them and clicking 'Download'.

Visualization in PyMOL

For visualization of the sensitivity data, we provide a plugin for PyMOL in /DeeProtein/pymol/visualize_sensitivity.py. You need PyMOL installed on your computer and our plugin to perform the visualization as follows. PyMOL must have access to the following packages:

The plugin assumes the sensitivity values to be in a 'labels' directory, therefore move sensitivities.txt to a directory called 'labels'

mkdir labels
mv sensitivities.txt labels/

Next, import the plugin in PyMOL

PyMOL> run visualize_sensitivity.py

and color the structure according to one of the GO-terms sensitivites (e.g. kinase activity):

PyMOL> color_sensitivity(file="sensitivities.txt", column="GO:0016301", on_pdb="6F3F", on_chain="A", show_hetatm=True, normalize=False, min_val=-5, max_val=5)

position the protein and save an image

PyMOL> png sensitivity_3D.png
<img src="examples/sensitivity_3D.png" height="350">

For details see the section Visualization in PyMOL.

Setup

This and the following sections describe how to setup and deploy DeeProtein.

Download the GeneOntology

$ wget http://purl.obolibrary.org/obo/go.obo

Clone this git repository:

$ git clone https://github.com/juzb/DeeProtein && cd DeeProtein

Requirements

Optional

General Information

Sensitivity data Sensitivity data is stored in our zenodo repository.

DeeProtein Weights Weights for DeeProtein are stored in our zenodo repository.

Usage

Note:

The following usage guide has been tailored to meet the requirements of the code-ocean compute capsule. It allows the execution of DeeProtein's functionality:

In order to work with DeeProtein in greater detail, i.e. to alter and/or combine models please refer to the main.sh script in our (GitHub repository)[https://github.com/juzb/DeeProtein]

Inference with DeeProtein

Download the weights for DeeProtein from our zenodo repository

$ wget https://zenodo.org/record/2574979/files/complete_model_0.npz
mv complete_model_0.npz complete_model.npz

  1. Specify the options in config.JSON, see FLAGS explanations.

    $ vim config.JSON

    Alternatively, options can be superseded by specifying the appropriate FLAG in the function call (as seen below).

  2. To infer a sequence on the pretrained DeeProtein Model use the sense.sh script as explained in the Classification section.

Sensitivity Analysis

Use the sense.sh file as described in the Sensitivity Analysis section or

  1. Write a masked_dataset.txt file having the following format: Each line consists of

     <PDB ID>;<Chain ID>;<GO:1>,<GO:2>, ...;<Sequence>;<Secondary structure>;<dis string>

    e.g.

     6F3F;A;GO:0016301,GO:0005524;QRKLEALIRDPRSPINVESLLDGLNSLVLDLDFPALRKNKNIDNFLNRYEKIVKKIRGLQMKAEDYDVVKVIGRGAFGEVQLVRHKASQKVYAMKLLSKFEMIKRSDSAFFWEERDIMAFANSPWVVQLFCAFQDDKYLYMVMEYMPGGDLVNLMSNYDVPEKWAKFYTAEVVLALDAIHSMGLIHRDVKPDNMLLDKHGHLKLADFGTCMKMDETGMVHCDTAVGTPDYISPEVLKSQGGDGYYGRECDWWSVGVFLFEMLVGDTPFYADSLVGTYSKIMDHKNSLCFPEDAEISKHAKNLICAFLTDREVRLGRNGVEEIKQHPFFKNDQWNWDNIRETAAPVVPELSSDIDSSNFDDITFPIPKAFVGNQLPFIGFTYYR;...............................................................................................................................................................................................................................................................................................................................................................................................;_______________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________

    where, sequence, secondary structure and dis string are of the same length. If the secondary structure is unknown a string containing only points is valid. If the dis string is unknown, a string containing only underscores is valid.

  2. Score the occluded sequences using

    $ python DeeProtein.py -mask=True -restore=True -restorepath=path/to/saves -model_to_use=model/model.py -v path/to/test/parent-directory-of-masked_dataset

    then

    $ python DeeProtein/scripts/calculate_sensitivity.py  path/to/test/parent-directory-of-masked_dataset/masked_dataset.txt path/to/test/parent-directory-of-masked_dataset/masked_dataset.txt

  3. Visualize the computed sensitivities in a sensitivity trace plot:

    $ python DeeProtein/scripts/plot_sensitivity.py path/to/test/parent-directory-of-masked_dataset/masked_dataset.txt "GO-to-plot" /outputdir/sensitivity_1D.png

FLAGS

A number of FLAGS is available in config.JSON to specify the behavior of DeeProtein, both for inference and training. While all FLAGS may be superseded in the function calls, we recommend to set up the config.JSON prior to usage:

FOR INFERENCE AND SENSITIVITY ANALYSIS:

FOR TRAINING A NEW MODEL:

Visualization in PyMOL

PyMOL> color_sensitivity(file,
                         column=None,
                         show_hetatm=True,
                         show_chains=True, 
                         on_chain=None,    
                         on_pdb=None,      
                         reload=True,     
                         normalize=True,   
                         min_val=-1,      
                         max_val=1        
                        )

Use to automatically color a structure by sensitivity. Is called from the PyMOL console. If the 3D structure needed to show a given proteins sensitivity is not available in the specified 'prot_path', the PDB file is automatically downloaded.