ESMFold
ESMFold is a machine learning model that predicts protein 3D structures from amino acid sequences using transformer-based neural networks. Developed under the Evolutionary Scale Modeling project, it leverages evolutionary data for fast and accurate predictions. While local installation and coding are required to run ESMFold, it can be easily accessed on the Copilot web-based platform with simple text prompts.
Fold ‘DIHICGICKQQFNNLDAFVAHKQSGSQ’
Fold A0A7S7MT40
Inputs:
- Protein Sequence: A string representing the single-letter amino acid sequence, consisting of the common 20 amino acids. This input is intended for single-chain (monomeric) proteins only.
Outputs:
- PDB file: File containing the predicted 3D coordinates of each residue in the input sequence.
- pLDDT Scores: Specified in the b-factor column of the PDB file. A per residue confidence score between 0 and 100 with higher being better.
Fold 'GIGDPVTCLKSGAICHPVFCPRRYKQIGTCGLPGTKCCKKP'
Evaluating ESMFold Results
Evaluating ESMFold results involves analyzing predicted protein structures for accuracy and relevance. Key aspects include reviewing pLDDT scores and interpreting PAE scores. This analysis informs potential functions and guides experimental validation, enhancing understanding of protein dynamics and interactions.
pLDDT Scores:
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 0-5 angstroms indicate low confidence (known relative positions), while scores of 20+ angstroms indicate high confidence (unknown relative positions). Analyzing PAE scores helps evaluate the structural alignment and potential flexibility of protein regions.