Input formats, result interpretation, and frequently asked questions.
CPPLocPred is a two-stage machine learning tool for predicting cell-penetrating peptides (CPPs) and their subcellular localization. It accepts peptide or protein sequences in FASTA format and returns:
A separate Motif Search tool scans sequences for known CPP-associated motifs linked to specific subcellular compartments using the MERCI motif locator.
All tools accept sequences in standard FASTA format. Each sequence must
have a header line beginning with > followed by the sequence on the next line(s).
Multiple sequences can be submitted at once.
Only the 20 standard amino acid single-letter codes are accepted:
There is no strict length limit. However, CPPLocPred is optimised for short-to-medium peptides (typically 5–50 amino acids). Very long sequences (>500 aa) can be submitted but are not the primary use case.
You can either paste sequences directly into the text box or upload a
.fasta / .txt file. If both are provided, the uploaded
file takes priority and the text box is cleared.
The prediction tool runs a two-stage classification pipeline on each submitted sequence.
The CPP threshold (default 0.4) controls sensitivity vs specificity. Lower values increase sensitivity (more CPPs predicted, more false positives). Higher values increase specificity (fewer CPPs predicted, fewer false positives).
After submission the results page shows a sortable table with one row per sequence. Probability scores are colour-coded for quick scanning:
The Predicted Localization column may contain multiple
semicolon-separated locations (e.g. Cytoplasm;Nucleus) if the sequence
scores above threshold for more than one compartment.
Results can be downloaded as CSV or TSV using the buttons above the table.
CPPLocPred predicts five destination categories:
| Location | Description |
|---|---|
| Cytoplasm | General cytosolic distribution following membrane translocation. The most common CPP destination. |
| Nucleus | Nuclear-targeted CPPs typically carry NLS-like (nuclear localisation signal) motifs such as polyarginine stretches. |
| Mitochondria | Mitochondria-penetrating peptides often carry MTS (mitochondrial targeting sequence) signals and amphipathic helices. |
| Endo/Lysosome | CPPs that traffic through endosomal or lysosomal compartments after endocytic uptake. |
| Others | Sequences targeting other intracellular compartments (ER, Golgi, plasma membrane, etc.) not covered by the four classes above. |
The motif search tool scans your sequences for known CPP-associated motifs using MERCI (Motif EmeRging and with Classes of Importance), a Perl-based motif locator.
The four colour classes correspond to different motif databases derived from amino acid property groupings:
The results page shows:
| Column | Description |
|---|---|
| ID | Sequence identifier from the FASTA header |
| Sequence | Amino acid sequence |
| CPP_Probability | Stage 1 probability score (0–1). Higher = more likely CPP. |
| CPP_Prediction | CPP, Non-CPP, or Invalid |
| Cytoplasm_Probability | Stage 2 localization score for Cytoplasm (only for CPPs) |
| Nucleus_Probability | Stage 2 localization score for Nucleus |
| Mitochondria_Probability | Stage 2 localization score for Mitochondria |
| Endo_lysosome_Probability | Stage 2 localization score for Endo/Lysosome |
| Others_Probability | Stage 2 localization score for Others |
| Final_Localization | Predicted location(s). NA for Non-CPPs. May be semicolon-separated if multiple locations are predicted. |
| Column | Description |
|---|---|
| Sequence_ID | Sequence identifier |
| Sequence | Full amino acid sequence |
| Motif_Pattern | The MERCI motif pattern that was matched (space-separated residue codes) |
| Start | Start position of the hit (1-based) |
| End | End position of the hit (1-based) |
| Matched_Residues | Actual amino acid string at the matched position |
CPPLocPred can be run locally via the command line. Download the standalone package from the Download page.
pandas, scikit-learn, catboost| Flag | Description | Default |
|---|---|---|
| -i | Input FASTA file | required |
| -o | Output CSV file | required |
| -j | Job: 1 = Prediction, 2 = Motif Search | 1 |
| -t | CPP probability threshold (Job 1 only) | 0.44 |
| -m | Directory containing model .pkl files (Job 1 only) | ./ |
| -l | Location for motif search (Job 2 only) | Cytoplasm |
| -c | Motif class: None, Koolman, Betts-Russell, Rasmol (Job 2 only) | None |
| --motif_dir | Directory containing motif files (Job 2 only) | ./motifs |
There is no hard limit, but we recommend submitting up to 500 sequences per job for reasonable response times. For larger datasets, use the standalone tool.
Your sequence contains characters not in the 20 standard amino acid alphabet. Check for spaces, numbers, or non-standard residue codes (B, J, O, U, X, Z). Remove or replace these before resubmitting.
When a CPP is predicted (Stage 1 passes) but none of the five location classifiers
(Stage 2) score above their respective thresholds, the highest-scoring location is
reported as Uncertain (Best=Location, Prob=X.XXX). This means the
sequence is likely a CPP but its localization is not confidently determined.
Yes. If a CPP scores above the threshold for more than one location in Stage 2,
all predicted locations are reported, separated by semicolons
(e.g. Cytoplasm;Nucleus).
Each colour class (None, Koolman, Betts-Russell, RasMol) uses a different motif database derived from CPP sequences grouped by amino acid property schemes. If you are unsure which to choose, start with None, which uses the general motif set without property-based grouping.
Not necessarily. The motif search and the prediction tool are independent. A sequence can be predicted as CPP without matching any known motif patterns, and vice versa. We recommend using both tools together for a comprehensive analysis.
If you use CPPLocPred in your research, please cite: