AI-Driven Cell-Penetrating Peptide Prediction | #sciencefather #phenomenology #researchawards #CellPenetratingPeptides #PeptideTherapeutics #CPPprediction

Improving CPP Prediction with Hybrid Feature Integration and Ensemble Machine Learning

Background & Motivation

Cell-penetrating peptides (CPPs) are short peptides capable of traversing biological membranes, playing a transformative role in modern drug delivery systems. Their ability to facilitate the intracellular transport of therapeutic agents—including nucleic acids, proteins, and small molecules—makes them key candidates in developing targeted therapies for conditions like cancer, genetic disorders, and neurodegenerative diseases. However, experimental identification of novel CPPs is resource-intensive, slow, and impractical for large-scale screening of potential sequences.

Limitations in Existing Methods

While existing computational methods have made progress in CPP prediction using either:

  • Conventional features (such as amino acid composition, charge, and hydrophobicity), or

  • Protein Language Models (PLMs) (deep learning-based models trained on large-scale protein data),

...each approach has its drawbacks. Conventional models often lack the power to capture sequence-level dependencies, while PLM-only models may struggle with functional specificity and interpretability. Hybrid models exist but are typically limited in diversity and scope—either using only one PLM or a narrow range of handcrafted features.

Proposed Solution: CPPpred-En

To overcome these challenges, we developed CPPpred-En, a comprehensive ensemble-learning framework that integrates:

  • 69 conventional peptide features (such as CTDC, TPC_1, AAC)

  • 8 pre-trained PLM embeddings (e.g., ESM1b, ESM2, ProtT5_XL_BFD)

  • Multiple machine learning classifiers (CatBoost, Gradient Boosting, Extra Trees, etc.)

Our approach involves:

  1. Evaluating feature–classifier combinations for predictive power.

  2. Selecting high-performing pairs.

  3. Ensembling them into a unified model to enhance generalisability and reduce overfitting.

This ensures the strengths of different representations and learning models are fully leveraged.

Datasets and Evaluation

CPPpred-En was trained and validated on two widely used benchmark datasets:

  • CPP924 – a curated dataset with positive and negative peptide samples.

  • MLCPP 2.0 – a more recent and balanced dataset with expanded sequence diversity.

On these datasets, CPPpred-En achieved:

  • Accuracy (Acc): 97.27% and MCC: 0.964 on CPP924

  • Accuracy (Acc): 96.10% and MCC: 0.707 on MLCPP 2.0

These results surpass existing state-of-the-art methods such as StackCPPred, SiameseCPP, and EnDM-CPP, demonstrating both high predictive power and robustness across diverse peptide data.

Key Innovations

  • Hybrid Feature Integration: The model benefits from both interpretable physicochemical features and high-dimensional, context-aware PLM embeddings.

  • Automated Feature–Classifier Selection: Rather than relying on pre-selected combinations, CPPpred-En systematically evaluates performance-driven pairs.

  • Generalisation Across Datasets: Consistent results on multiple datasets show its wide applicability.

  • Scalability: The model can be extended to incorporate new feature types and PLMs as they become available.

Applications and Impact

CPPpred-En is a practical and high-performing solution for:

  • Accelerating peptide-based drug discovery.

  • Designing targeted delivery systems.

  • Supporting research in gene therapy, immunotherapy, and molecular biology.

It can also be adapted to discover other functional peptide types, offering a versatile platform for peptide informatics.

Future Directions

  • Integration into web-based tools or open-source platforms.

  • Fine-tuning with transformer-based deep learning models.

  • Expanding the training datasets with experimentally validated novel CPPs.

  • Exploring explainable AI techniques to enhance biological interpretation.

#sciencefather #phenomenology #researchawards #CellPenetratingPeptides #PeptideTherapeutics #CPPprediction

International Phenomenological Research Awards
Contact Us For Enquiry: contact@phenomenologicalresearch.com

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