What is CleaveNet?
CleaveNet is a deep learning model developed by Microsoft Research that predicts matrix metalloproteinase (MMP) cleavage sites in peptide sequences. Given a peptide up to 10 residues long, CleaveNet outputs z-scores indicating how efficiently each MMP variant would cleave that sequence.
Matrix metalloproteinases are zinc-dependent enzymes that digest extracellular matrix components. They play essential roles in tissue remodeling, wound healing, and embryonic development, but are also implicated in cancer progression—where MMPs facilitate tumor invasion and metastasis by degrading physical barriers between tissues. Understanding which sequences a particular MMP will cleave is valuable for designing protease-activated therapeutics, diagnostic biosensors, and studying enzyme specificity.
CleaveNet uses a transformer architecture trained on mRNA display data from Kukreja et al. (2015), which profiled cleavage activity across thousands of peptide substrates. The model learns sequence patterns associated with efficient cleavage by different MMP family members.
How to use CleaveNet online
ProteinIQ provides cloud-based access to CleaveNet, with results returned in seconds without requiring Python installation or GPU hardware.
Modes
Settings
In Generate from z-scores mode, CleaveNet expects a conditioning CSV with the full official generator header:
MMP1,MMP10,MMP11,MMP12,MMP13,MMP14,MMP15,MMP16,MMP17,MMP19,MMP2,MMP20,MMP24,MMP25,MMP3,MMP7,MMP8,MMP9
0.1,0.0,0.2,0.1,0.3,0.0,0.1,0.1,0.2,0.0,0.1,0.1,0.0,0.2,0.3,0.1,0.0,0.2
Supported MMP variants
CleaveNet returns prediction columns for 18 matrix metalloproteinase variants:
MMP1, MMP2, MMP3, MMP7, MMP8, MMP9, MMP10, MMP11, MMP12, MMP13, MMP14, MMP15, MMP16, MMP17, MMP19, MMP20, MMP24, MMP25
The conditional generator uses the same source order shown in the CSV header above.
Output
In prediction mode, results are returned as a spreadsheet with one row per input peptide. Each MMP variant has both a z-score and an uncertainty column.
When evaluation mode is enabled, ProteinIQ also returns the official CleaveNet output files, including score CSVs and any evaluation plots CleaveNet generates.
In generator modes, the spreadsheet output contains one row per generated peptide:
ProteinIQ also supports the native artifact bundle as downloadable files, including all_scores.csv, all_uncertainty.csv, weighted_all_scores.csv, per-MMP top-hit CSVs, allmmp_top20_cleaved.csv, and generated sequence CSVs when available.
Interpreting z-scores
Z-scores quantify cleavage efficiency relative to the training distribution. Higher positive values indicate stronger predicted cleavage. The uncertainty value is reported separately and is not already folded into the z-score.
When designing selective substrates, compare z-scores across MMP variants. A peptide with high z-score for MMP13 but low scores for other MMPs would be a candidate MMP13-selective substrate.
How does CleaveNet work?
CleaveNet uses a transformer neural network to predict cleavage from peptide sequence alone. The model tokenizes each amino acid in the input sequence, processes them through self-attention layers that capture dependencies between positions, and outputs a z-score for each MMP variant.
The predictor was trained on data from Kukreja et al. (2015), an mRNA display experiment that measured cleavage rates for thousands of randomized peptide substrates across multiple MMPs. Each peptide in the training set has experimentally measured cleavage values, allowing the model to learn which sequence motifs correlate with efficient cleavage by specific proteases.
Input peptides shorter than 10 residues are padded with special tokens. The model processes the padded sequence and predicts z-scores independently for each MMP—these scores represent how far above or below the mean cleavage rate a given peptide falls, normalized by the standard deviation of the training distribution.
An alternative LSTM architecture is also available in the original codebase for cases where the transformer may overfit to training patterns. The LSTM backbone can sometimes extrapolate better to novel cleavage motifs not well-represented in the training data.
The official CleaveNet codebase also includes a peptide generator. ProteinIQ now supports both the unconditional generator and the z-score-conditioned generator through the same tool.
Applications
Protease-activated drug delivery: Peptide linkers connecting antibodies to cytotoxic payloads can be designed to release the drug only when cleaved by tumor-associated MMPs, reducing off-target toxicity.
Diagnostic biosensors: Peptides that fluoresce upon cleavage serve as activity-based probes. CleaveNet helps identify sequences with appropriate MMP selectivity for detecting specific cancers or inflammatory conditions.
Enzyme specificity studies: Comparing z-scores across the 18 MMP variants reveals which positions in a peptide drive selectivity, informing mechanistic understanding of MMP substrate preferences.
Limitations
- Predictions are calibrated for 10-residue peptides. The model has not been extensively validated on shorter or longer sequences.
- CleaveNet predicts relative cleavage efficiency, not absolute kinetic rates. Z-scores indicate ranking, not quantitative Km or kcat values.
- Training data comes from in vitro mRNA display experiments; in vivo cleavage may differ due to protein folding, localization, and inhibitor presence.
- Generated peptides are model samples, not experimentally validated substrates.
