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AlphaFold 3 Output

Output Directory Structure

For every input job, AlphaFold 3 writes all its outputs in a directory called by the sanitized version of the job name. E.g. for job name "My first fold (test)", AlphaFold 3 will write its outputs in a directory called my_first_fold_test.

The following structure is used within the output directory:

  • Sub-directories with results for each sample and seed. There will be num_seeds * num_samples such sub-directories. The naming pattern is seed-<seed value>_sample-<sample number>. Each of these directories contains a confidence JSON, summary confidence JSON, and the mmCIF with the predicted structure.
  • Top-ranking prediction mmCIF: <job_name>_model.cif. This file contains the predicted coordinates and should be compatible with most structural biology tools. We do not provide the output in the PDB format, the CIF file can be easily converted into one if needed.
  • Top-ranking prediction confidence JSON: <job_name>_confidences.json.
  • Top-ranking prediction summary confidence JSON: <job_name>_summary_confidences.json.
  • Job input JSON file with the MSA and template data added by the data pipeline: <job_name>_data.json.
  • Ranking scores for all predictions: ranking_scores.csv. The prediction with highest ranking is the one included in the root directory.
  • Output terms of use: TERMS_OF_USE.md.

Below is an example AlphaFold 3 output directory listing for a job called "Hello Fold", that has been ran with 1 seed and 5 samples:

hello_fold/
├── seed-1234_sample-0/
│   ├── confidences.json
│   ├── model.cif
│   └── summary_confidences.json
├── seed-1234_sample-1/
│   ├── confidences.json
│   ├── model.cif
│   └── summary_confidences.json
├── seed-1234_sample-2/
│   ├── confidences.json
│   ├── model.cif
│   └── summary_confidences.json
├── seed-1234_sample-3/
│   ├── confidences.json
│   ├── model.cif
│   └── summary_confidences.json
├── seed-1234_sample-4/
│   ├── confidences.json
│   ├── model.cif
│   └── summary_confidences.json
├── TERMS_OF_USE.md
├── hello_fold_confidences.json
├── hello_fold_data.json
├── hello_fold_model.cif
├── hello_fold_summary_confidences.json
└── ranking_scores.csv

Confidence Metrics

Similar to AlphaFold2 and AlphaFold-Multimer, AlphaFold 3 outputs include confidence metrics. The main metrics are:

  • pLDDT: a per-atom confidence estimate on a 0-100 scale where a higher value indicates higher confidence. pLDDT aims to predict a modified LDDT score that only considers distances to polymers. For proteins this is similar to the lDDT-Cα metric but with more granularity as it can vary per atom not just per residue. For ligand atoms, the modified LDDT considers the errors only between the ligand atom and polymers, not other ligand atoms. For DNA/RNA a wider radius of 30 Å is used for the modified LDDT instead of 15 Å.
  • PAE (predicted aligned error): an estimate of the error in the relative position and orientation between two tokens in the predicted structure. Higher values indicate higher predicted error and therefore lower confidence. For proteins and nucleic acids, PAE score is essentially the same as AlphaFold2, where the error is measured relative to frames constructed from the protein backbone. For small molecules and post-translational modifications, a frame is constructed for each atom from its closest neighbors from a reference conformer.
  • pTM and ipTM scores: the predicted template modeling (pTM) score and the interface predicted template modeling (ipTM) score are both derived from a measure called the template modeling (TM) score. This measures the accuracy of the entire structure (Zhang and Skolnick, 2004; Xu and Zhang, 2010). A pTM score above 0.5 means the overall predicted fold for the complex might be similar to the true structure. ipTM measures the accuracy of the predicted relative positions of the subunits within the complex. Values higher than 0.8 represent confident high-quality predictions, while values below 0.6 suggest a failed prediction. ipTM values between 0.6 and 0.8 are a gray zone where predictions could be correct or incorrect. The TM score is very strict for small structures or short chains, so pTM assigns values less than 0.05 when fewer than 20 tokens are involved; for these cases PAE or pLDDT may be more indicative of prediction quality.

For detailed description of these confidence metrics see the AlphaFold 3 paper. For protein components, the AlphaFold: A Practical guide course for structures provides additional tutorials on the confidence metrics.

If you are interested in a specific entity or interaction, then there are confidences available in the outputs which are specific to each chain or chain-pair, as opposed to the full complex. See below for more details on all the confidence metrics that are returned.

Multi-Seed and Multi-Sample Results

By default, the model samples five predictions per seed. The top-ranked prediction across all samples and seeds is available at the top-level of the output directory. All samples along with their associated confidences are available in subdirectories of the output directory.

For ranking of the full complex use the ranking_score (higher is better). This score uses overall structure confidences (pTM and ipTM), but also includes terms that penalize clashes and encourage disordered regions not to have spurious helices – these extra terms mean the score should only be used to rank structures.

If you are interested in a specific entity or interaction, you may want to rank by a metric specific to that chain or chain-pair, as opposed to the full complex. In that case, use the per chain or per chain-pair confidence metrics described below for ranking.

Metrics in Confidences JSON

For each predicted sample we provide two JSON files. One contains summary metrics – summaries for either the whole structure, per chain or per chain-pair – and the other contains full 1D or 2D arrays.

Summary outputs:

  • ptm: A scalar in the range 0-1 indicating the predicted TM-score for the full structure.
  • iptm: A scalar in the range 0-1 indicating predicted interface TM-score (confidence in the predicted interfaces) for all interfaces in the structure.
  • fraction_disordered: A scalar in the range 0-1 that indicates what fraction of the prediction structure is disordered, as measured by accessible surface area, see our paper for details.
  • has_clash: A boolean indicating if the structure has a significant number of clashing atoms (more than 50% of a chain, or a chain with more than 100 clashing atoms).
  • ranking_score: A scalar in the range [-100, 1.5] that can be used for ranking predictions, it incorporates ptm, iptm, fraction_disordered and has_clash into a single number with the following equation: 0.8 × ipTM + 0.2 × pTM + 0.5 × disorder − 100 × has_clash.
  • chain_pair_pae_min: A [num_chains, num_chains] array. Element (i, j) of the array contains the lowest PAE value across rows restricted to chain i and columns restricted to chain j. This has been found to correlate with whether two chains interact or not, and in some cases can be used to distinguish binders from non-binders.
  • chain_pair_iptm: A [num_chains, num_chains] array. Off-diagonal element (i, j) of the array contains the ipTM restricted to tokens from chains i and j. Diagonal element (i, i) contains the pTM restricted to chain i. Can be used for ranking a specific interface between two chains, when you know that they interact, e.g. for antibody-antigen interactions
  • chain_ptm: A [num_chains] array. Element i contains the pTM restricted to chain i. Can be used for ranking individual chains when the structure of that chain is most of interest, rather than the cross-chain interactions it is involved with.
  • chain_iptm: A [num_chains] array that gives the average confidence (interface pTM) in the interface between each chain and all other chains. Can be used for ranking a specific chain, when you care about where the chain binds to the rest of the complex and you do not know which other chains you expect it to interact with. This is often the case with ligands.

Full array outputs:

  • pae: A [num_tokens, num_tokens] array. Element (i, j) indicates the predicted error in the position of token j, when the prediction is aligned to the ground truth using the frame of token i.
  • atom_plddts: A [num_atoms] array, element i indicates the predicted local distance difference test (pLDDT) for atom i in the prediction.
  • contact_probs: A [num_tokens, num_tokens] array. Element (i, j) indicates the predicted probability that token i and token j are in contact (8 Å between the representative atom for each token), see paper for details.
  • token_chain_ids: A [num_tokens] array indicating the chain ids corresponding to each token in the prediction.
  • atom_chain_ids: A [num_atoms] array indicating the chain ids corresponding to each atom in the prediction.