AlphaFold3 for Molecule Docking
AlphaFold3 (aka AF3) is an advanced AI model developed by DeepMind for predicting the 3D structures of proteins, nucleic acids, small molecules, and post-translational modification with high accuracy. It supports multi-chains and therefore is also a cofolding or docking method.
How to Use AlphaFold3
Inputs
- Protein Sequence(s)
String(s) representing the single-letter amino acid sequence, consisting of the common 20 amino acids.
- RNA Sequence(s)
- String(s) representing the single-letter nucleotide sequence.
- DNA Sequence(s)
- String(s) representing the single-letter nucleotide sequence.
- SMILE(s)
- String representing a small molecule.
Outputs
- PDB File
- Specifies the predicted structure of the inputs.
Example
We are serving Boltz-1, a commercially accessible version of AlphaFold3, coming soon! In the meantime, you can use DiffDock for fast protein with small molecule docking in the example below.
Dock asparagine into A0A1L9RXX7
Analyzing Alphafold3 Predictions
pLDDT Scores: The predicted local distance difference test (pLDDT) is a per-residue confidence score. Scores range from 0-50 (very low, indicating disorder/flexibility) to 90-100 (very high, suggesting highly structured/stable conformations). Understanding these scores is crucial for assessing the reliability of the predicted structure.
PAE Scores: Predicted aligned error (PAE) measures confidence between residue pairs. Scores of 0-5Å indicate high confidence (known relative positions), while scores of 20Å+ indicate low confidence (unknown relative positions). Analyzing PAE scores helps evaluate the structural alignment and potential flexibility of protein regions.