CPPLocPred

Prediction of Cell-Penetrating Peptides and their Subcellular Localization

CPPLocPred is a two-stage machine learning tool:

• Stage 1 — CPP vs Non-CPP (ExtraTrees classifier, AAC features)
• Stage 2 — Subcellular localization (CatBoost classifiers, DDR features): Cytoplasm · Nucleus · Mitochondria · Endo/Lysosome · Others

Web server: https://webs.iiitd.edu.in/raghava/cpplocpred/ GitHub: https://github.com/namanm04/CPPLocPred

Installation

pip install cpplocpred

All model .pkl files, motif databases, and MERCI_motif_locator.pl are bundled inside the package — no separate download needed.

Dependencies installed automatically: pandas, scikit-learn, catboost

Motif search requires Perl to be installed and on your PATH.

Usage

Job 1 — CPP Prediction

cpplocpred -i input.fasta -o results.csv -j 1
cpplocpred -i input.fasta -o results.csv -j 1 -t 0.5

Job 2 — Motif Search

cpplocpred -i input.fasta -o motifs.csv -j 2 -l Nucleus
cpplocpred -i input.fasta -o motifs.csv -j 2 -l Mitochondria -c Koolman

All arguments

Output

Job 1 — results.csv

Job 2 — motifs.csv + motifs_hits.csv

• motifs.csv — one row per sequence (ID, Length, Total_Hits, Motif_Patterns, …)
• motifs_hits.csv — one row per hit (ID, Start, End, Hit_Length, Motif_Pattern, Matched_Residues)

Input format

>seq1
RQIKIWFQNRRMKWKK
>seq2
GRKKRRQRRRPPQ

Only standard 20 amino acid single-letter codes accepted (ACDEFGHIKLMNPQRSTVWY).

Citation

Bajiya N., Mehta N.K., Raghava G.P.S. (2025) CPPLocPred: Machine learning-based prediction and subcellular localization of cell-penetrating peptides. IIIT Delhi. https://webs.iiitd.edu.in/raghava/cpplocpred/

License

MIT © 2025 Raghava Group, IIIT Delhi