Bio Structural Biology Alphafold Predictions

AlphaFold Predictions

Download and analyze AlphaFold predicted protein structures from the AlphaFold Protein Structure Database.

Download Structures

Single Structure by UniProt ID

import requests

def download_alphafold(uniprot_id, output_dir='.'):
    '''Download AlphaFold structure for UniProt accession'''
    base_url = 'https://alphafold.ebi.ac.uk/files'
    pdb_url = f'{base_url}/AF-{uniprot_id}-F1-model_v4.pdb'
    cif_url = f'{base_url}/AF-{uniprot_id}-F1-model_v4.cif'

    response = requests.get(pdb_url)
    if response.status_code == 200:
        output_path = f'{output_dir}/AF-{uniprot_id}-F1-model_v4.pdb'
        with open(output_path, 'w') as f:
            f.write(response.text)
        return output_path
    return None

pdb_file = download_alphafold('P04637')  # Human p53

Check Availability

def check_alphafold_exists(uniprot_id):
    '''Check if AlphaFold prediction exists'''
    url = f'https://alphafold.ebi.ac.uk/api/prediction/{uniprot_id}'
    response = requests.get(url)
    return response.status_code == 200

if check_alphafold_exists('P04637'):
    print('AlphaFold structure available')

Get Metadata

def get_alphafold_info(uniprot_id):
    '''Get AlphaFold prediction metadata'''
    url = f'https://alphafold.ebi.ac.uk/api/prediction/{uniprot_id}'
    response = requests.get(url)
    if response.status_code == 200:
        return response.json()[0]
    return None

info = get_alphafold_info('P04637')
print(f"Gene: {info['gene']}")
print(f"Organism: {info['organismScientificName']}")
print(f"Model version: {info['latestVersion']}")

File Types Available

Database version v4 (current as of 2025). The version number refers to the database release, not the AlphaFold model version.

def download_pae(uniprot_id, output_dir='.'):
    '''Download PAE (predicted aligned error) matrix'''
    url = f'https://alphafold.ebi.ac.uk/files/AF-{uniprot_id}-F1-predicted_aligned_error_v4.json'
    response = requests.get(url)
    if response.status_code == 200:
        output_path = f'{output_dir}/AF-{uniprot_id}-F1-pae.json'
        with open(output_path, 'w') as f:
            f.write(response.text)
        return output_path
    return None

Analyze pLDDT Confidence Scores

Extract from PDB B-factors

AlphaFold stores pLDDT scores in the B-factor column.

from Bio.PDB import PDBParser

def extract_plddt(pdb_file):
    '''Extract pLDDT confidence scores from AlphaFold PDB'''
    parser = PDBParser(QUIET=True)
    structure = parser.get_structure('protein', pdb_file)

    residue_plddt = {}
    for model in structure:
        for chain in model:
            for residue in chain:
                if residue.id[0] == ' ':  # Standard residue
                    ca = residue['CA'] if 'CA' in residue else list(residue.get_atoms())[0]
                    residue_plddt[residue.id[1]] = ca.get_bfactor()
    return residue_plddt

plddt = extract_plddt('AF-P04637-F1-model_v4.pdb')
avg_plddt = sum(plddt.values()) / len(plddt)
print(f'Average pLDDT: {avg_plddt:.1f}')

Confidence Interpretation

Plot pLDDT per Residue

import matplotlib.pyplot as plt

def plot_plddt(plddt_dict, output='plddt_plot.png'):
    residues = sorted(plddt_dict.keys())
    scores = [plddt_dict[r] for r in residues]

    plt.figure(figsize=(12, 4))
    plt.fill_between(residues, scores, alpha=0.3)
    plt.plot(residues, scores)
    plt.axhline(y=70, color='orange', linestyle='--', label='Confident threshold')
    plt.axhline(y=90, color='green', linestyle='--', label='Very high threshold')
    plt.xlabel('Residue')
    plt.ylabel('pLDDT')
    plt.ylim(0, 100)
    plt.legend()
    plt.savefig(output)
    plt.close()

plot_plddt(plddt)

Analyze PAE (Predicted Aligned Error)

import json
import numpy as np
import matplotlib.pyplot as plt

def load_pae(pae_file):
    '''Load PAE matrix from JSON'''
    with open(pae_file) as f:
        data = json.load(f)

    # AlphaFold v4 format
    if 'predicted_aligned_error' in data[0]:
        return np.array(data[0]['predicted_aligned_error'])
    # Older format
    return np.array(data['predicted_aligned_error'])

def plot_pae(pae_matrix, output='pae_plot.png'):
    plt.figure(figsize=(8, 8))
    plt.imshow(pae_matrix, cmap='Greens_r', vmin=0, vmax=30)
    plt.colorbar(label='Expected position error (A)')
    plt.xlabel('Scored residue')
    plt.ylabel('Aligned residue')
    plt.title('Predicted Aligned Error')
    plt.savefig(output)
    plt.close()

pae = load_pae('AF-P04637-F1-pae.json')
plot_pae(pae)

PAE Interpretation

• Low PAE (green): Residues have well-defined relative positions
• High PAE (white): Uncertain relative positions (flexible linkers, domains)
• Diagonal blocks: Distinct structural domains

Batch Download

def batch_download_alphafold(uniprot_ids, output_dir='.'):
    '''Download multiple AlphaFold structures'''
    import os
    os.makedirs(output_dir, exist_ok=True)

    results = {}
    for uid in uniprot_ids:
        pdb_file = download_alphafold(uid, output_dir)
        results[uid] = pdb_file
        if pdb_file:
            print(f'Downloaded: {uid}')
        else:
            print(f'Not found: {uid}')
    return results

ids = ['P04637', 'P53_HUMAN', 'Q9Y6K9']
files = batch_download_alphafold(ids, 'alphafold_structures')

Compare with Experimental Structure

from Bio.PDB import PDBParser, Superimposer

def compare_structures(alphafold_pdb, experimental_pdb):
    '''Calculate RMSD between AlphaFold and experimental structure'''
    parser = PDBParser(QUIET=True)
    af_struct = parser.get_structure('af', alphafold_pdb)
    exp_struct = parser.get_structure('exp', experimental_pdb)

    # Get CA atoms from first chain
    af_atoms = [r['CA'] for r in af_struct[0].get_residues() if 'CA' in r]
    exp_atoms = [r['CA'] for r in exp_struct[0].get_residues() if 'CA' in r]

    # Align by length (simple approach)
    min_len = min(len(af_atoms), len(exp_atoms))
    af_atoms = af_atoms[:min_len]
    exp_atoms = exp_atoms[:min_len]

    super_imposer = Superimposer()
    super_imposer.set_atoms(exp_atoms, af_atoms)
    rmsd = super_imposer.rms
    return rmsd

Related Skills

• structural-biology/structure-io - Load and parse PDB/mmCIF files
• structural-biology/geometric-analysis - RMSD, superimposition
• database-access/uniprot-access - Get UniProt IDs for proteins
• structural-biology/structure-navigation - Navigate structure hierarchy

AlphaFold Predictions - Usage Guide

Overview

Download and analyze AI-predicted protein structures from the AlphaFold database, including confidence scores (pLDDT) and predicted aligned error (PAE) for assessing prediction quality.

Prerequisites

pip install biopython requests numpy

Quick Start

Tell your AI agent what you want to do:

• "Download the AlphaFold structure for UniProt P04637"
• "Check the confidence scores for this AlphaFold prediction"
• "Identify low-confidence regions in this predicted structure"

Example Prompts

Downloading Structures

"Download the AlphaFold model for UniProt ID P53_HUMAN"

"Get the AlphaFold structure for P04637 in mmCIF format"

"Fetch AlphaFold predictions for these 5 UniProt IDs"

Analyzing Confidence

"Show me the pLDDT scores for this AlphaFold structure"

"Which regions have low confidence (pLDDT < 70)?"

"Plot the per-residue confidence scores"

PAE Analysis

"Download and visualize the PAE matrix for this protein"

"Identify domain boundaries from the PAE data"

"Which domain-domain interactions are reliable?"

Quality Assessment

"Compare this AlphaFold prediction with the experimental structure"

"Highlight the confident vs disordered regions"

"Is this prediction reliable enough for docking?"

What the Agent Will Do

1. Construct the AlphaFold database URL from UniProt ID 2. Download the structure file (PDB or mmCIF format) 3. Parse confidence scores from B-factor column (pLDDT) 4. Optionally fetch PAE matrix for inter-residue error estimates 5. Analyze and report on prediction quality

Database Access

Direct download URLs:

• Structure: https://alphafold.ebi.ac.uk/files/AF-{UNIPROT_ID}-F1-model_v4.pdb
• mmCIF: https://alphafold.ebi.ac.uk/files/AF-{UNIPROT_ID}-F1-model_v4.cif
• PAE: https://alphafold.ebi.ac.uk/files/AF-{UNIPROT_ID}-F1-predicted_aligned_error_v4.json

API endpoint: https://alphafold.ebi.ac.uk/api/prediction/{UNIPROT_ID}

Confidence Score Interpretation

Tips

• Check pLDDT before using - Regions with pLDDT < 70 should be treated cautiously
• Use PAE for domains - Low PAE between residues indicates reliable relative positioning
• B-factor column holds pLDDT - Parse with Bio.PDB and read atom.bfactor
• Single conformation only - AlphaFold predicts one state, not conformational ensembles
• Compare when possible - Validate against experimental structures if available
• 200M+ structures available - Coverage includes most UniProt sequences