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AlphaFold2

AlphaFold2 is an advanced AI model developed by DeepMind for predicting the 3D structures of proteins, nucleic acids, and small molecules with high accuracy. It utilizes diffusion techniques and supports multi-chain predictions to enhance folding accuracy, providing insights into the interactions of all biological molecules, including ligands.

Inputs​

  • Sequence: Provide the protein, RNA or DNA sequence in FASTA format.
  • Templates: Include any available template structures (PDB files) to enhance accuracy.
  • Features: Specify relevant features such as known mutations, post-translational modifications, and secondary structure information.
  • Environment Settings: Define the computational environment, including hardware specifications and parallel processing options.

Outputs​

  • PDB File: A file containing the predicted 3D coordinates of each atom in the input, providing a detailed representation of the molecular structure.
  • pLDDT Scores: Specified in the b-factor column of the PDB file, these per-residue confidence scores range from 0 to 100, with higher scores indicating greater confidence in the prediction.

Analyzing Alphafold2 Predictions​

pLDDT Scores:​

A per-residue confidence score. 0-50 indicates very low confidence (disorder/flexibility), 50-70 low, 70-90 high, and 90-100 very high (structured/stable)

PAE Scores:​

Assesses confidence between residue pairs. 0-5 Å indicates low confidence (known relative positions), while 20+ Å indicates high confidence (unknown relative positions).