DeepGOWeb function prediction webserver

DeepGOWeb is a webserver for DeepGOPlus protein function prediction. It allows users to obtain predicted protein functions in three different ways. First, using the Prediction web page, users can submit protein sequences and obtain predictions which are downloadable in JSON format. Second, DeepGOWeb provides a REST API for users to access our servers programmatically. Finally, users can use our SPARQL endpoint to call DeepGOPlus within a SPARQL query.

Web page for submitting protein sequences

Users should provide the following data to use the service on the Prediction page:

  • Format: FASTA format or Raw Sequences separated by a newline.
  • Threshold: a value between 0.1 and 1.0 for filtering predictions by the confidence score of the model.
  • Data: Protein sequences in the selected format. A maximum of 10 sequences are allowed in one request.

After submitting the request, users will be redirected to results page where they can see the predictions and download them in JSON format. Users can also save the link to the results page and come back to it anytime.

Output description

The output page provides function predictions for each protein. A prediction includes GO terms for the Biological Process, Molecular Function, and Cellular Component sub-ontologies with confidence scores. Only predictions with a confidence above the threshold parameter are shown. We also show a list of proteins that are used for predictions based on sequence similarity and their similarity score to the query protein. The results are provided under the CC-BY License and can be downloaded in JSON/CSV formats. Here is the example output page.


The REST API allows accessing the DeepGOWeb service programmatically. Here we provide an example using python and Requests library.

import requests

threshold = 0.3
r ='', data={'data_format': 'enter', 'data': sequence, 'threshold': threshold})                
result = r.json()


The SPARQL endpoint allows to call the DeepGOPlus function prediction model within a SPARQL query. We provide a custom SPARQL function called "deepgo" which takes a protein sequence and prediction threshold as an input and returns the predicted functions along with the subontology, label, and prediction score. The output can be downloaded in different formats such as json, xml, csv or text.

Example queries:

  • Example 1: Simple example query
    PREFIX dg: <>
    PREFIX GO: <> 
    SELECT ?ont ?go ?label ?score
     (?ont ?go ?label ?score)
  • Example 2: Federated query which runs DeepGOPlus on two sequences from the UniProt SPARQL Endpoint
    PREFIX dg: <>
    PREFIX GO: <> 
    PREFIX rdfs: <>
    PREFIX rdf: <>
    PREFIX taxon: <>
    PREFIX up: <>
    SELECT ?protein ?organism ?isoform ?sub ?go ?label ?score
    SELECT DISTINCT ?protein ?organism ?isoform ?aa_sequence
      SERVICE <> {
        ?protein a up:Protein .
        ?protein up:organism ?organism .
        ?organism rdfs:subClassOf taxon:9606 .
        ?protein up:sequence ?isoform .
        ?isoform rdf:value ?aa_sequence .
    LIMIT 2
    (?sub ?go ?label ?score) dg:deepgo(?aa_sequence 0.3) .

Commandline tool

Users can also install DeepGOPlus on their system and run DeepGOPlus locally. This is useful when prediction functions for a large amount of protein sequences.



pip install deepgoplus
Download the data

Download all the files from and extract them into data folder


deepgoplus --data-root path_to_data_folder --in-file input_fasta_filename

Prediction Model

We use DeepGOPlus method to predict protein functions. The method is based on combination of Convolutional Neural Network (CNN) model and sequence similarity.

DeepGOPlus CNN Model

The figure describes the architecture of our deep learning model. First, the input sequence is converted to a one-hot encoded representation of size 21 × 2000, where a one-hot vector of length 21 represents an amino acid (AA) and 2000 is the input length. Sequences with a length less than 2000 are padded with zeros and longer sequences are split into smaller chunks with less than 2000 AAs. This input is passed to a set of CNN layers with different filter sizes of 8, 16, …, 128. Each of the CNN layers has 512 filters which learn specific sequence motifs of a particular size. Each filter is scanning the sequence and their maximum score is pooled using a MaxPooling layer. In total, we generate a feature vector of size 8192 where each value represents a score that indicates the presence of a relevant sequence motif. This vector is passed to the fully connected classification layer which outputs the predictions

Sequence Similarity Predictions

We find similar sequences from a training set using Diamond (Buchfink et al., 2015) with an e-value of 0.001 and obtain a bitscore for every similar sequence. We transfer all annotations of similar sequences to a query sequence with prediction scores computed using the bitscores. For a set of similar sequences E of the query sequence q, we compute the prediction score for a GO class f as

where Ts is a set of true annotations of the protein with sequence s. Then, to compute the final prediction scores of DeepGOPlus, we combine the two prediction scores using a weighted sum model (Fishburn, 1967):

where 0 ≤ α  ≤  1 is a weight parameter which balances the relative importance of the two prediction methods.