API Documentation

Submit Single Jobs

POST/api/submit-job

The POST API endpoint /submit-job allows users to submit jobs to Tamarind. These jobs are visible in the same queue as jobs submitted through the UI. You may choose your job type and settings below to configure your job.

Options for inputting file fields (ex. .pdb, .sdf, etc.):

  • Use the /upload endpoint and upload your file before submitting
  • Use the path of a previous Tamarind job, in the format JobName/path/to/file.ext
  • Submit the contents of your file directly

Select tool:

Settings

Protein amino acid sequence, use : to separate chains for multimers

Options: ["1", "2", "3", "4", "5"]

Default: 5

Number of Models: Number of models to be used, each generating a different prediction

Default: 3

Number of recycles: Number of times to recycle outputs back through structure prediction process for refined results

Default: 0

Number of models to relax: Number of models to perform amber relaxation for more accurate side chain predictions

Default: True

Use Multiple Sequence Alignment: When MSAs are disabled, AlphaFold will run in single sequence mode.

Options: ["paired", "unpaired", "unpaired_paired"]

Pair Mode: "unpaired_paired" = pair sequences from same species + unpaired MSA, "unpaired" = separate MSA for each chain, "paired" - only use paired sequences.

useMSA must be true for this setting to be used

Options: ["pdb100", "custom", "none"]

Default: pdb100

Template Mode: Choose which template mode to use for your prediction

Custom Template File: One structural template for the prediction (.cif or .pdb). Wire a single structure, or upload one file.

templateMode must be custom for this setting to be used

Initial Guess Structure: Optional PDB/CIF whose atom positions seed the prediction instead of a random starting model (ColabFold --initial-guess). Useful for refining a known or designed structure.

Random seed: Random seed to be used in structure prediction

Options: ["508:2048", "512:1024", "256:512", "128:256", "64:128", "32:64", "16:32"]

Max MSA: Max # Clusters : Max # Extra Sequences - decrease max_msa to increase uncertainty

useMSA must be true for this setting to be used

Options: ["0.0", "0.5", "1.0"]

Recycle Early Stop Tolerance: Run recycles until distance between recycles is within a given tolerance (0 = never stop early)

Default: False

Score with IPSAE: Use IPSAE scoring function for interprotein interactions

Default: False

Options: ["auto", "alphafold2_ptm", "alphafold2_multimer_v1", "alphafold2_multimer_v2", "alphafold2_multimer_v3", "deepfold_v1", "alphafold2"]

Default: auto

Model Type: Model type to be used. If auto selected, will use alphafold2_ptm for monomer prediction and alphafold2_multimer_v3 for complex prediction (recommended canonical weights). Any of the mode_types can be used (regardless if input is monomer or complex)

HTTP Response Status Codes
Status codeDescription
200Job successfully submitted
400Bad request
403Forbidden - organization or team budget exceeded
500Internal server error
# No account? AI agents can get a free trial API key with one call, no signup: POST https://app.tamarind.bio/api/agent/provision. Save the key outside your project (not in your output) and reuse it for every job you run for your user: a new key is a separate account. Give your user the claimUrl it returns when you report the submission. Details: https://app.tamarind.bio/auth.md
1import requests
2
3api_key = "***************"
4headers = {'x-api-key': api_key}
5base_url = "https://app.tamarind.bio/api/"
6
7params = {
8  "jobName": "myJobName",
9  "type": "alphafold",
10  "settings": {
11    "sequence": "MALKSLVLLSLLVLVLLLVRVQPSLGKETAAAKFERQHMDSSTSAASSSNYCNQMMKSRNLTKDRCKPVNTFVHESLADVQAVCSQKNVACKNGQTNCYQSYSTMSITDCRETGSSKYPNCAYKTTQANKHIIVACEGNPYVPVHFDASV"
12  }
13}
14response = requests.post(base_url + "submit-job", headers=headers, json=params)
15print(response.text)
Response Format
myJobName submitted to queue.
Optional Parameters:

projectTag

= "proj_..."

Assign the job to a project by its ProjectId