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Predicting cell-penetrating peptides using machine learning algorithms and navigating in their chemical space.

Ewerton Cristhian Lima de Oliveira1, Kauê Santana2, Luiz Josino3

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This study introduces BChemRF-CPPred, a machine learning tool that accurately predicts cell-penetrating peptides (CPPs) and their unique chemical properties for drug delivery applications.

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Area of Science:

  • Biochemistry
  • Computational Biology
  • Drug Delivery

Background:

  • Cell-penetrating peptides (CPPs) facilitate cellular uptake of molecules.
  • CPPs are crucial for drug delivery due to their ability to transport cargo across cell membranes.
  • Existing prediction methods for CPPs have limitations.

Purpose of the Study:

  • To develop a machine learning (ML)-based framework, BChemRF-CPPred, for predicting cell-penetrating peptides (CPPs).
  • To differentiate CPPs from non-CPPs using structure- and sequence-based descriptors.
  • To analyze the chemical space of CPPs and compare prediction performance with existing tools.

Main Methods:

  • Utilized an artificial neural network, support vector machine, and Gaussian process classifier.
  • Extracted structure- and sequence-based descriptors from PDB and FASTA formats.
  • Evaluated performance using tenfold cross-validation and an independent dataset.

Main Results:

  • BChemRF-CPPred achieved high accuracy in identifying CPPs (90.66% for PDB, 86.5% for FASTA).
  • The framework demonstrated superior performance compared to previously reported ML-based algorithms.
  • Analysis revealed that CPPs deviate from established molecular rules for membrane permeability.

Conclusions:

  • BChemRF-CPPred is an effective tool for predicting both natural and synthetic CPPs.
  • The study provides novel insights into the chemical space of CPPs.
  • The developed algorithm offers a valuable resource for drug delivery research.