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Area of Science:

  • Environmental Science
  • Biotechnology
  • Computational Chemistry

Background:

  • Plastic pollution, especially microplastics (MPs), is a major global environmental and health concern.
  • Biocompatible and biodegradable plastic-binding peptides (PBPs) are promising for MP detection and removal.
  • Discovering effective PBPs is challenging due to the vast number of possible peptide sequences.

Purpose of the Study:

  • To develop a robust framework for accelerated discovery of high-affinity plastic-binding peptides (PBPs).
  • To overcome the limitations of experimental and traditional computational methods in exploring the vast peptide sequence space.
  • To improve the accuracy and efficiency of PBP identification for microplastic remediation.

Main Methods:

  • Integration of biophysical modeling data (Peptide Binder Design algorithm) with evidential deep learning for predictive modeling and uncertainty quantification.
  • Application of metaheuristic search methods to efficiently explore the peptide sequence space.
  • Validation of discovered PBPs using molecular dynamics simulations to assess adsorption free energies.

Main Results:

  • Identified high-affinity PBPs for common plastics like polyethylene, polypropylene, and polystyrene.
  • Discovered PBPs exhibited significantly greater median adsorption free energies compared to previously designed peptides.
  • Demonstrated the benefit of uncertainty quantification in improving the performance of PBP design, with better results achieved when uncertainty was low.

Conclusions:

  • The developed framework effectively accelerates the discovery of high-affinity plastic-binding peptides.
  • Combining biophysical modeling, evidential deep learning, and metaheuristic search provides a powerful approach for PBP identification.
  • This work paves the way for developing effective, bio-inspired solutions for microplastic remediation.