Hands-On: Accessing DeepMind's AlphaFold 3 API Through India's National AI Partnership

What You'll Build

By the end of this tutorial, you'll have a working Python script that queries DeepMind's AlphaFold 3 protein structure prediction API through the newly announced National Partnerships for AI initiative. You'll authenticate using the academic access program, submit a protein sequence, and retrieve a 3D structure prediction in PDB format that you can visualize locally.

Important clarification: As of the latest public information, DeepMind has not announced a direct AlphaFold 3 API accessible through India's National AI Partnership. This tutorial demonstrates the conceptual workflow for how such an integration would work, based on existing Google Cloud patterns and the AlphaFold Server academic access program. The actual implementation may differ when official API access becomes available.

This matters because partnerships like these could give researchers and educators access to frontier AI models. If you're working in computational biology, drug discovery, or AI-for-science education, understanding this workflow prepares you for when such access becomes available. The authentication patterns and API interaction methods shown here follow standard Google Cloud practices used across their AI services.

Prerequisites

• Python 3.10+ installed (check with python --version or python3 --version)
• pip package manager (usually included with Python)
• Academic or institutional email from an Indian university/research institution (for partnership programs when available)
• Google Cloud account - https://cloud.google.com/free (free tier includes $300 credit)
• pip packages: requests, biopython, google-auth, google-auth-oauthlib, google-auth-httplib2
• PyMOL or Mol* for visualization (optional but recommended for viewing structures)
• Basic command line familiarity (navigating directories, running scripts)
• Estimated time: 45-60 minutes including account setup

Step-by-Step Instructions

Step 1: Register for AlphaFold Server Academic Access

Currently, DeepMind provides academic access through the AlphaFold Server. Navigate to the official access portal and complete the registration form.

Actions:

1. Visit the AlphaFold Server: https://alphafoldserver.com/
2. Click "Sign in" or "Request Access"
3. Use your institutional email address (.ac.in or .edu.in domain for Indian institutions)
4. Complete the academic verification form with your research details
5. Wait for approval email (typically 24-72 hours)

What just happened: You've requested access to the academic tier of AlphaFold services. Academic programs often provide free or subsidized access to computational resources that would otherwise require significant infrastructure investment.

Note: Keep your approval email—it may contain specific API endpoints or access tokens needed for programmatic access.

Step 2: Set Up Google Cloud Project and Authentication

To interact with Google Cloud services programmatically, you need a project and service account credentials.

2a. Install Google Cloud SDK:

# For Linux/macOS:
curl https://sdk.cloud.google.com | bash

# Restart your shell
exec -l $SHELL

# For Windows: Download installer from
# https://cloud.google.com/sdk/docs/install

2b. Initialize and authenticate:

# Initialize gcloud (follow prompts to select/create project)
gcloud init

# Authenticate with your Google account
gcloud auth login

# Set up application default credentials
gcloud auth application-default login

Expected output:

You are now logged in as [your-email@domain.com].
Your current project is [your-project-id].

2c. Create a service account:

# Capture your project ID
export PROJECT_ID=$(gcloud config get-value project)

# Create service account
gcloud iam service-accounts create alphafold-access \
    --display-name="AlphaFold API Access" \
    --project=$PROJECT_ID

# Generate and download key file
gcloud iam service-accounts keys create ~/alphafold-key.json \
    --iam-account=alphafold-access@${PROJECT_ID}.iam.gserviceaccount.com

Expected output:

created key [a1b2c3d4e5f6] of type [json] as [/home/username/alphafold-key.json]

What just happened: You created a service account—a special type of Google account intended for applications rather than humans. The JSON key file contains credentials your Python script will use to authenticate. Store this file securely and never commit it to version control.

Step 3: Set Up Python Environment and Install Dependencies

Create an isolated Python environment to prevent package conflicts.

# Create virtual environment
python3 -m venv alphafold-env

# Activate it
# On Linux/macOS:
source alphafold-env/bin/activate

# On Windows:
# alphafold-env\Scripts\activate

# Verify activation (you should see (alphafold-env) in your prompt)
which python

# Install required packages
pip install --upgrade pip
pip install requests==2.31.0 biopython==1.83 google-auth==2.27.0 google-auth-oauthlib==1.2.0 google-auth-httplib2==0.2.0

Expected output:

Successfully installed requests-2.31.0 biopython-1.83 google-auth-2.27.0 google-auth-oauthlib-1.2.0 google-auth-httplib2-0.2.0

What just happened: You've installed the HTTP client (requests), biological sequence handling library (biopython), and Google authentication libraries needed to securely communicate with Google Cloud APIs.

Step 4: Create the Protein Structure Prediction Script

Build the core script that handles authentication, API communication, and result processing. Create a new file called predict_structure.py.

#!/usr/bin/env python3
"""
AlphaFold API Interaction Script
Demonstrates protein structure prediction workflow
"""

import os
import sys
import requests
import json
from google.auth.transport.requests import Request
from google.oauth2 import service_account

# ============================================================================
# CONFIGURATION
# ============================================================================

# Path to your service account key file
SERVICE_ACCOUNT_FILE = os.path.expanduser('~/alphafold-key.json')

# API endpoint (NOTE: This is a placeholder - actual endpoint will be provided
# when API access is granted through official channels)
API_ENDPOINT = 'https://alphafoldserver.com/api/v1/predict'

# ============================================================================
# AUTHENTICATION
# ============================================================================

def authenticate():
    """
    Authenticate using service account credentials.
    Returns an authenticated credentials object.
    """
    if not os.path.exists(SERVICE_ACCOUNT_FILE):
        print(f"ERROR: Service account key not found at {SERVICE_ACCOUNT_FILE}")
        print("Please ensure you've completed Step 2 and the file exists.")
        sys.exit(1)
    
    try:
        credentials = service_account.Credentials.from_service_account_file(
            SERVICE_ACCOUNT_FILE,
            scopes=['https://www.googleapis.com/auth/cloud-platform']
        )
        # Refresh to get valid token
        credentials.refresh(Request())
        return credentials
    except Exception as e:
        print(f"ERROR: Authentication failed: {e}")
        sys.exit(1)

# ============================================================================
# PREDICTION FUNCTION
# ============================================================================

def predict_structure(sequence, sequence_id="protein_1"):
    """
    Submit a protein sequence for structure prediction.
    
    Args:
        sequence: String of amino acid letters (standard 20 amino acids)
        sequence_id: Identifier for this sequence
    
    Returns:
        Dictionary containing prediction results
    """
    # Validate sequence
    valid_amino_acids = set("ACDEFGHIKLMNPQRSTVWY")
    if not all(aa in valid_amino_acids for aa in sequence.upper()):
        invalid = set(sequence.upper()) - valid_amino_acids
        raise ValueError(f"Invalid amino acids found: {invalid}")
    
    # Authenticate
    print("Authenticating...")
    credentials = authenticate()
    
    # Prepare request payload
    payload = {
        "sequence": sequence.upper(),
        "id": sequence_id
    }
    
    # Set up headers with authentication token
    headers = {
        'Authorization': f'Bearer {credentials.token}',
        'Content-Type': 'application/json'
    }
    
    # Submit prediction request
    print(f"\nSubmitting sequence: {sequence}")
    print(f"Length: {len(sequence)} amino acids")
    print(f"Sequence ID: {sequence_id}")
    print("Calling API (this may take 1-5 minutes)...")
    
    try:
        response = requests.post(
            API_ENDPOINT,
            headers=headers,
            json=payload,
            timeout=600  # 10 minute timeout
        )
        
        # Check response status
        if response.status_code == 200:
            return response.json()
        else:
            print(f"\nERROR {response.status_code}: {response.text}")
            return None
            
    except requests.exceptions.Timeout:
        print("\nERROR: Request timed out. Try a shorter sequence or increase timeout.")
        return None
    except requests.exceptions.RequestException as e:
        print(f"\nERROR: Request failed: {e}")
        return None

# ============================================================================
# MAIN EXECUTION
# ============================================================================

if __name__ == "__main__":
    # Test sequence: Human insulin A-chain (21 amino acids)
    # This is a well-studied small protein, ideal for testing
    test_sequence = "GIVEQCCTSICSLYQLENYCN"
    
    print("=" * 70)
    print("AlphaFold Structure Prediction")
    print("=" * 70)
    
    # Run prediction
    result = predict_structure(test_sequence, sequence_id="insulin_a_chain")
    
    if result:
        # Extract and save PDB structure
        pdb_data = result.get('pdb_content', '')
        confidence = result.get('confidence_score', 'N/A')
        
        if pdb_data:
            output_file = 'insulin_a_chain_predicted.pdb'
            with open(output_file, 'w') as f:
                f.write(pdb_data)
            
            print("\n" + "=" * 70)
            print("✓ SUCCESS!")
            print("=" * 70)
            print(f"Structure saved to: {output_file}")
            print(f"Confidence score: {confidence}")
            print(f"File size: {len(pdb_data)} bytes")
            print("\nNext: Visualize with PyMOL or upload to https://molstar.org/viewer/")
        else:
            print("\nWARNING: No PDB structure data in response")
            print(f"Response keys: {result.keys()}")
    else:
        print("\n✗ Prediction failed. Check error messages above.")
        sys.exit(1)

What this code does:

• Authentication: Loads your service account credentials and obtains an access token
• Validation: Checks that your sequence contains only valid amino acid codes
• API Communication: Sends a POST request with your sequence to the prediction endpoint
• Result Handling: Saves the returned PDB structure file to disk
• Error Handling: Provides clear error messages for common failure modes

The insulin A-chain sequence is used as a test case because it's small (fast prediction), well-characterized (you can verify results), and contains interesting structural features (alpha helices and disulfide bonds).

Step 5: Run Your First Prediction

With your virtual environment still activated and the script created, execute the prediction:

# Ensure you're in the directory containing predict_structure.py
# and your virtual environment is activated

python predict_structure.py

Expected output (if API were available):

======================================================================
AlphaFold Structure Prediction
======================================================================
Authenticating...

Submitting sequence: GIVEQCCTSICSLYQLENYCN
Length: 21 amino acids
Sequence ID: insulin_a_chain
Calling API (this may take 1-5 minutes)...

======================================================================
✓ SUCCESS!
======================================================================
Structure saved to: insulin_a_chain_predicted.pdb
Confidence score: N/A
File size: 8432 bytes

Next: Visualize with PyMOL or upload to https://molstar.org/viewer/

What just happened: Your script authenticated with Google Cloud, submitted a protein sequence, and received a predicted 3D structure. The confidence score (often reported as pLDDT - predicted Local Distance Difference Test) indicates prediction reliability: >90 is very high confidence, 70-90 is good, 50-70 is low confidence, <50 is unreliable.

Step 6: Visualize the Predicted Structure

PDB files contain 3D coordinates but need specialized software to visualize.

Option A: PyMOL (Desktop Application)

# If PyMOL is installed, launch with your structure
pymol insulin_a_chain_predicted.pdb

Then in PyMOL's command interface:

hide everything
show cartoon
color spectrum, insulin_a_chain_predicted
bg_color white
orient

Option B: Mol* Web Viewer (No Installation Required)

1. Navigate to https://molstar.org/viewer/
2. Click "Open Files" in the top-left
3. Select your insulin_a_chain_predicted.pdb file
4. The structure will load automatically with default visualization

What you should see: Insulin A-chain typically shows an alpha-helix structure with two disulfide bonds (cysteine-cysteine connections). The structure should appear as a ribbon or cartoon representation, not a tangled mess of atoms. Look for regular helical turns—this indicates the prediction captured the known secondary structure.

Verification

Confirm your setup is working correctly with these checks:

Check 1: Verify PDB file was created

# Check file exists and has reasonable size
ls -lh insulin_a_chain_predicted.pdb

# Should show something like:
# -rw-r--r-- 1 user user 8.2K Dec 15 10:30 insulin_a_chain_predicted.pdb

Check 2: Inspect PDB file format

# View first 20 lines of the PDB file
head -20 insulin_a_chain_predicted.pdb

Expected output: You should see lines starting with ATOM containing coordinate data:

ATOM      1  N   GLY A   1      10.123  12.456   8.789  1.00 85.23           N
ATOM      2  CA  GLY A   1      11.234  13.567   9.890  1.00 87.45           C
ATOM      3  C   GLY A   1      12.345  14.678  10.901  1.00 88.12           C
...

Check 3: Validate structure in viewer

Load the file in either PyMOL or Mol* and verify:

• Structure loads without errors
• You can see clear secondary structure elements (helices, not random coils)
• The structure appears compact, not stretched across the entire viewing area
• For insulin A-chain: expect to see helical regions in the N-terminal and C-terminal portions

Success criteria:

• ✓ PDB file exists and is 5-15 KB in size
• ✓ File contains properly formatted ATOM records
• ✓ Structure displays recognizable secondary structure in visualization software
• ✓ Confidence score (if reported) is above 70

Common Issues & Fixes

Issue 1: "Service account key not found" Error

Error message:

ERROR: Service account key not found at /home/user/alphafold-key.json

Cause: The script cannot locate your service account JSON key file.

Fix:

# Verify the file exists
ls -l ~/alphafold-key.json

# If missing, regenerate it (Step 2c)
gcloud iam service-accounts keys create ~/alphafold-key.json \
    --iam-account=alphafold-access@$(gcloud config get-value project).iam.gserviceaccount.com

# Or update the SERVICE_ACCOUNT_FILE path in the script to match actual location

Issue 2: "Invalid amino acids found" Error

Error message:

ValueError: Invalid amino acids found: {'X', 'B', 'Z'}

Cause: Your sequence contains non-standard amino acid codes. Only the 20 standard amino acids are accepted: ACDEFGHIKLMNPQRSTVWY.

Fix:

# Add this validation before calling predict_structure()
sequence = "YOUR_SEQUENCE_HERE"

# Remove any whitespace or newlines
sequence = sequence.replace(" ", "").replace("\n", "").replace("\r", "")

# Check for invalid characters
valid_aa = set("ACDEFGHIKLMNPQRSTVWY")
invalid = set(sequence.upper()) - valid_aa

if invalid:
    print(f"Found invalid characters: {invalid}")
    print("Valid amino acids: A C D E F G H I K L M N P Q R S T V W Y")
    # Remove invalid characters (or fix your source sequence)
    sequence = ''.join(aa for aa in sequence.upper() if aa in valid_aa)

Issue 3: Authentication Failures

Error message:

ERROR: Authentication failed: Could not automatically determine credentials

Cause: Google Cloud SDK isn't properly authenticated or the service account lacks necessary permissions.

Fix:

# Re-authenticate your gcloud session
gcloud auth application-default login

# Verify your project is set
gcloud config get-value project

# Check service account exists
gcloud iam service-accounts list

# If the service account is missing, recreate it (Step 2c)

Issue 4: Connection Timeout for Large Proteins

Error message:

ERROR: Request timed out. Try a shorter sequence or increase timeout.

Cause: Large proteins (>200 amino acids) can take 10+ minutes to predict. The default timeout is too short.

Fix:

# In predict_structure.py, increase the timeout parameter:

response = requests.post(
    API_ENDPOINT,
    headers=headers,
    json=payload,
    timeout=1800  # Increase to 30 minutes for large proteins
)

Alternatively, test with shorter sequences first (50-100 amino acids) to verify your setup works before attempting large predictions.

Issue 5: Module Import Errors

Error message:

ModuleNotFoundError: No module named 'google.auth'

Cause: Required packages aren't installed, or you're not using the virtual environment.

Fix:

# Ensure virtual environment is activated
# You should see (alphafold-env) in your prompt
source alphafold-env/bin/activate  # Linux/macOS
# or
alphafold-env\Scripts\activate  # Windows

# Reinstall packages
pip install --upgrade requests biopython google-auth google-auth-oauthlib google-auth-httplib2

# Verify installation
pip list | grep google-auth

Next Steps

Now that you understand the workflow for API-based protein structure prediction, here are ways to extend your capabilities:

Immediate Next Steps:

• Process multiple sequences: Modify the script to read from a FASTA file and batch process proteins
• Add error logging: Implement proper logging with the logging module to track prediction jobs
• Parse confidence scores: Extract per-residue pLDDT scores from the PDB file's B-factor column to identify low-confidence regions
• Automate