ProtNLM for Function Prediction
ProtNLM predicts the function of a protein based on its amino acid sequence. It is a T5-based model developed by Google which returns a number of suggested descriptive names, rank ordered by their score (0.0, 1.0) where larger is better. These predictions help scientists explore the potential functions of new proteins and evaluate computer-designed proteins, with sequence comparison to known proteins as a common first step.
How to Use ProtNLM
Predict the function of ‘DIHICGICKQQFNNLDAFVAHKQSGSQ’
| Name | Score |
|---|---|
| C2H2-type domain-containing protein | 0.54 |
| General transcription factor II-I repeat ... | 0.02 |
| Zinc finger protein | 0.019 |
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
- Text Descriptions
Short text descriptions of the protein, ranked by confidence scores (0.0 to 1.0).
- Confidence Score
Confidence level for each description (0.0 to 1.0). Higher scores are better.
Examples
Predict the function of M4332
| Name | Score |
|---|---|
| Tyrosine-protein kinase | 0.592 |
| 2.7.10.2 | 0.249 |
| Non-specific protein-tyrosine kinase | 0.019 |
Load Q9UPY3 and predict its function
| Name | Score |
|---|---|
| Endoribonuclease Dicer | 0.466 |
| 3.1.26.3 | 0.409 |
| Endoribonuclease Dcr-1 | 0.033 |
