Evaluate Unconditional

Runs unconditional evaluation pipeline on the outputs of Alphafold or Colabfold models. Selects a folding that is most similar to the generation and returns a scores table.

Quick Start

1. Connect generated proteins (PDB) to generation_pdb input.
2. Connect foldings (PDB) to folding_pdb input.
3. Run the node to obtain the best folding and evaluation scores.

Setup Guide

1. Prepare Inputs

1. Generate proteins using a diffusion or design node.
2. Obtain foldings from Alphafold or Colabfold nodes.

2. Connect and Run

1. Connect the generated proteins to generation_pdb.
2. Connect the foldings to folding_pdb.
3. Execute the node to receive evaluation results.

Basic Usage

Unconditional Evaluation

• Compare generated proteins to predicted foldings.
• Select the best matching folding for each design.
• Output a CSV table with evaluation metrics (pst, rmsd, pae, plddt).

Configuration

Required Inputs

Field	Description	Type	Example
generation_pdb	Generated proteins to evaluate.	PDB	{"design_0": "...PDB..."}
folding_pdb	Foldings to evaluate proteins against.	PDB	{"design_0_ranked_0": "...PDB..."}

Optional Inputs

None

Outputs

Field	Description	Example
best_folding.pdb	Best of the predicted foldings.	{"design_0": "...PDB..."}
score.csv	Scores of the foldings (pst, rmsd, pae, plddt).	"design_0,0.95,1.2,10.5,85.0"

Best Practices

Input Preparation

• Ensure that generation_pdb and folding_pdb have matching or corresponding IDs for accurate evaluation.
• Use outputs from supported generation and folding nodes for compatibility.

Efficient Evaluation

• For large batches, consider running in TEST mode to limit evaluation to the first structure for faster feedback.

Troubleshooting

Common Issues

• Mismatched IDs: Ensure that the IDs in generation_pdb and folding_pdb correspond to the same designs.
• Empty Outputs: Check that both inputs contain valid PDB data.

Need Help?

• Contact support for further assistance.