DLKcat

Predict enzyme kcat values from protein sequences and substrate structures or names.

Docs

Input

Input mode

Enzyme sequence(s)

Substrate(s) - SMILES or names

1 credit

Output

Configure input settings on the left, then click "Submit job"orLoad an example (it's free)

Alcohol dehydrogenase with ethanol

Specific enzyme-substrate pairs

What is DLKcat?

DLKcat predicts enzyme turnover numbers (kcat values) from protein sequences and substrate structures. The turnover number represents how many substrate molecules an enzyme can convert to product per second under saturating conditions—a fundamental kinetic parameter for understanding enzyme efficiency.

Developed at Chalmers University of Technology and published in Nature Catalysis (2022), DLKcat combines a convolutional neural network (CNN) for processing protein sequences with a graph neural network (GNN) for analyzing substrate molecular structures. This dual-network architecture allows the model to learn patterns in enzyme-substrate interactions that correlate with catalytic rates.

The model was trained on over 16,000 experimentally measured kcat values from the BRENDA and SABIO-RK enzyme databases, covering both wild-type and engineered enzymes across diverse species.

How does DLKcat work?

DLKcat processes enzyme-substrate pairs through two parallel neural networks:

Protein encoding: The enzyme sequence is split into overlapping 3-gram amino acid fragments. A CNN with three layers extracts features that capture sequence patterns associated with catalytic activity.
Substrate encoding: The substrate's SMILES representation is converted to a molecular graph where atoms are nodes and bonds are edges. A GNN with three time steps propagates information through the graph, learning structural features relevant to enzymatic processing.

The outputs from both networks are concatenated and passed through fully connected layers to predict log10(kcat). Training used radius-2 substrate subgraphs and 20-dimensional vector embeddings.

How to use DLKcat online

ProteinIQ runs DLKcat on cloud GPUs, delivering turnover number predictions in seconds without local installation.

Inputs

Input	Description
`Enzyme sequence(s)`	FASTA format protein sequences. Multiple enzymes supported for batch analysis.
`Substrate(s)`	SMILES strings or compound names (one per line). The tool will pair each enzyme with each substrate.

For specific enzyme-substrate pairs, switch to paired TSV input and provide one row per pair:

Column	Description
`Substrate Name`	Compound name used for labeling and PubChem lookup when SMILES is blank
`Substrate SMILES`	Substrate SMILES, recommended for reliable predictions
`Protein Sequence`	Enzyme amino acid sequence

Settings

Setting	Description
`Input mode`	Choose separate enzyme/substrate inputs for all combinations, or paired TSV rows for specific enzyme-substrate pairs.

Results

The output table contains predicted kcat values for each enzyme-substrate pair:

Column	Description
`Substrate Name`	The substrate label
`Substrate SMILES`	The substrate structure used for prediction
`Protein Sequence`	The full input protein sequence
`Kcat value (1/s)`	Turnover number in reactions per second
`Log2 Kcat`	Raw model output before conversion to linear scale

Interpreting predictions

DLKcat predicts log2(kcat), then converts to linear scale. Predictions span a wide range:

kcat (1/s)	Interpretation
> 1000	Fast enzyme, typical of metabolic enzymes
100–1000	Moderate catalytic rate
10–100	Slow enzyme
< 10	Very slow, may indicate poor substrate match

The model achieves Pearson's r ≈ 0.71 and RMSE < 1 (log10 scale) on held-out test data. Performance is best for enzymes similar to the training set—predictions become less reliable as sequence similarity to training enzymes decreases.

Limitations

DLKcat has important constraints to consider:

Sequence similarity dependence: The model performs well when test enzymes share >70% sequence identity with training examples. For dissimilar enzymes, predictions may not outperform simple kcat averages.

No environmental factors: DLKcat was not trained with temperature or pH condition data, so predictions are based on enzyme sequence and substrate structure.

Mutant enzymes: While trained on some mutant data, predictions for novel mutations—especially those affecting catalytic residues—should be validated experimentally.

Substrate coverage: Predictions are most reliable for substrates similar to those in the BRENDA/SABIO-RK training data.

Applications

Enzyme turnover predictions support several research applications:

Metabolic modeling: Parameterizing enzyme-constrained genome-scale models (ecGEMs) with predicted kcat values
Enzyme engineering: Screening mutant libraries computationally before experimental characterization
Pathway design: Identifying rate-limiting enzymes in synthetic biology pathways
Comparative enzymology: Analyzing kcat distributions across species or enzyme families

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