ProteInfer API

Article Information

Oct 24, 2023

5 min read

On this page, we will show and explain the use of ProteInfer for enzyme function prediction. As well as document the BioLM API, and demonstrate no-code and code interfaces to enzyme function prediction.

API Usage

here are 2 BioLM endpoints corresponding to ProteInfer GO and EC. These endpoints are

• ProteInfer EC with an endpoint at https://biolm.ai/api/v2/proteinfer-ec/

• ProteInfer GO with an endpoint at https://biolm.ai/api/v2/proteinfer-go/

Each of these endpoints can be accessed with the predict action by appending predict to the urls above. For example, the BioLM ProteInfer EC prediction endpoint is https://biolm.ai/api/v2/proteinfer-ec/predict/.

Making Requests

Curl

curl --location 'https://biolm.ai/api/v2/proteinfer-ec/predict/' \
--header 'Content-Type: application/json' \
--header "Authorization: Token $BIOLMAI_TOKEN" \
--data '{
    "items": [
        {
            "sequence": "MMQTVLAKIVADKAIWVEARKQQQPLASFQNEVQPSTRHFYDALQGARTAFILECKKASPSKGVIRDDFDPARIAAIYKHYASAISVLTDEKYFQGSFNFLPIVSQIAPQPILCKDFIIDPYQIYLARYYQADACLLMLSVLDDDQYRQLAAVAHSLEMGVLTEVSNEEEQERAIALGAKVVGINNRDLRDLSIDLNRTRELAPKLGHNVTVISESGINTYAQVRELSHFANGFLIGSALMAHDDLHAAVRRVLLGENKVCGLTRGQDAKAAYDAGAIYGGLIFVATSPRCVNVEQAQEVMAAAPLQYVGVFRNHDIADVVDKAKVLSLAAVQLHGNEEQLYIDTLREALPAHVAIWKALSVGETLPAREFQHVDKYVLDNGQGGSGQRFDWSLLNGQSLGNVLLAGGLGADNCVEAAQTGCAGLDFNSAVESQPGIKDARLLASVFQTLRAY"
        }
    ]
}'

Python Requests

import requests
import json

url = "https://biolm.ai/api/v2/proteinfer-ec/predict/"

payload = json.dumps({
    "items": [
        {
            "sequence": "MMQTVLAKIVADKAIWVEARKQQQPLASFQNEVQPSTRHFYDALQGARTAFILECKKASPSKGVIRDDFDPARIAAIYKHYASAISVLTDEKYFQGSFNFLPIVSQIAPQPILCKDFIIDPYQIYLARYYQADACLLMLSVLDDDQYRQLAAVAHSLEMGVLTEVSNEEEQERAIALGAKVVGINNRDLRDLSIDLNRTRELAPKLGHNVTVISESGINTYAQVRELSHFANGFLIGSALMAHDDLHAAVRRVLLGENKVCGLTRGQDAKAAYDAGAIYGGLIFVATSPRCVNVEQAQEVMAAAPLQYVGVFRNHDIADVVDKAKVLSLAAVQLHGNEEQLYIDTLREALPAHVAIWKALSVGETLPAREFQHVDKYVLDNGQGGSGQRFDWSLLNGQSLGNVLLAGGLGADNCVEAAQTGCAGLDFNSAVESQPGIKDARLLASVFQTLRAY"
        }
    ]
})
headers = {
'Content-Type': 'application/json',
'Authorization': 'Token {}'.format(os.environ['BIOLMAI_TOKEN'])
}

response = requests.request("POST", url, headers=headers, data=payload)

print(response.text)

biolmai SDK

import biolmai
seqs = ["MMQTVLAKIVADKAIWVEARKQQQPLASFQNEVQPSTRHFYDALQGARTAFILECKKASPSKGVIRDDFDPARIAAIYKHYASAISVLTDEKYFQGSFNFLPIVSQIAPQPILCKDFIIDPYQIYLARYYQADACLLMLSVLDDDQYRQLAAVAHSLEMGVLTEVSNEEEQERAIALGAKVVGINNRDLRDLSIDLNRTRELAPKLGHNVTVISESGINTYAQVRELSHFANGFLIGSALMAHDDLHAAVRRVLLGENKVCGLTRGQDAKAAYDAGAIYGGLIFVATSPRCVNVEQAQEVMAAAPLQYVGVFRNHDIADVVDKAKVLSLAAVQLHGNEEQLYIDTLREALPAHVAIWKALSVGETLPAREFQHVDKYVLDNGQGGSGQRFDWSLLNGQSLGNVLLAGGLGADNCVEAAQTGCAGLDFNSAVESQPGIKDARLLASVFQTLRAY"]

cls = biolmai.ProteInferEC()
resp = cls.generate(seqs)

R

library(RCurl)
headers = c(
"Content-Type" = "application/json",
'Authorization' = paste('Token', Sys.getenv('BIOLMAI_TOKEN'))
)
payload = "{
    \"items\": [
        {
            \"sequence\": \"MMQTVLAKIVADKAIWVEARKQQQPLASFQNEVQPSTRHFYDALQGARTAFILECKKASPSKGVIRDDFDPARIAAIYKHYASAISVLTDEKYFQGSFNFLPIVSQIAPQPILCKDFIIDPYQIYLARYYQADACLLMLSVLDDDQYRQLAAVAHSLEMGVLTEVSNEEEQERAIALGAKVVGINNRDLRDLSIDLNRTRELAPKLGHNVTVISESGINTYAQVRELSHFANGFLIGSALMAHDDLHAAVRRVLLGENKVCGLTRGQDAKAAYDAGAIYGGLIFVATSPRCVNVEQAQEVMAAAPLQYVGVFRNHDIADVVDKAKVLSLAAVQLHGNEEQLYIDTLREALPAHVAIWKALSVGETLPAREFQHVDKYVLDNGQGGSGQRFDWSLLNGQSLGNVLLAGGLGADNCVEAAQTGCAGLDFNSAVESQPGIKDARLLASVFQTLRAY\"
        }
    ]
}"
res <- postForm("https://biolm.ai/api/v2/proteinfer-ec/predict/", .opts=list(postfields = payload, httpheader = headers, followlocation = TRUE), style = "httppost")
cat(res)

JSON Response

Expand Example Response

{
    "results": [
        {
            "sequence_id": "0",
            "predictions": [
                {
                    "label": "EC:4.-.-.-",
                    "confidence": 1.0,
                    "description": "Lyases."
                },
                {
                    "label": "EC:4.1.-.-",
                    "confidence": 1.0,
                    "description": "Carbon-carbon lyases."
                },
                {
                    "label": "EC:4.1.1.-",
                    "confidence": 1.0,
                    "description": "Carboxy-lyases."
                }

Request Definitions

items:

Inside items are a list of dictionaries with each dictionary corresponding to one model input.

sequence:

The input sequence for the model

Response Definitions

results:

This is the main key in the JSON object that contains an array of model results. Each element in the array represents a set of predictions for one input instance.

predictions:

This key holds a list of dictionaries, each containing a prediction result. Each item in the list represents a predicted Enzyme Commission (EC) number or Gene Ontology (GO) terms along with additional information related to the prediction.

sequence_id:

Identifier for the input protein sequence for which the EC numbers are being predicted.

label:

Represents the predicted EC number or GO term. EC numbers are used to classify enzymes and includes four levels of classification, each separated by a dot. ( “EC:3.-.-.-” and “EC:3.2.1.-” are examples of predicted EC numbers). “GO:0008150” and “GO:0003674” are examples of predicted GO term IDs.

confidence:

This is a measure of the model’s certainty or confidence in the predicted EC number, ranging from 0 to 1, with higher values indicating higher confidence.

description:

This provides a textual description or annotation related to the predicted EC number or GO term. For EC number this gives some information about the type of reaction the enzyme catalyzes. FOr GO term Textual description or name of the predicted GO term descriptions like “biological_process” and “molecular_function” provide a brief understanding of what each GO term represents in biological terminology.

Related

ProteInfer Info