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Rapid and accurate structure-based therapeutic peptide design using GPU accelerated thermodynamic integration.

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GPU accelerated thermodynamic integration (TI) enhances peptide drug discovery by accurately predicting binding affinities. This method successfully optimized a peptide targeting EME1, reducing pancreatic cancer cell viability.

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Area of Science:

  • Computational chemistry
  • Drug discovery
  • Biophysics

Background:

  • Peptide therapeutics offer advantages over small molecules, including specificity and lower toxicity.
  • Optimizing peptide binding affinity is crucial for developing effective therapeutics.
  • Advancements in GPU computing enable faster and more accurate computational methods.

Purpose of the Study:

  • To leverage GPU-accelerated thermodynamic integration (TI) for rapid and accurate peptide binding affinity optimization.
  • To benchmark the accuracy of TI predictions, particularly for mutations involving charged side-chains.
  • To optimize a peptide targeting the cancer-associated protein EME1 and evaluate its therapeutic potential.

Main Methods:

  • Utilized GPU-accelerated thermodynamic integration (TI) for peptide binding affinity calculations.
  • Benchmarked TI predictions against published peptide optimization studies.
  • Employed a 3-step TI protocol for mutations involving charged side-chains to enhance accuracy.
  • Validated computationally predicted peptide mutations using fluorescence polarization assays.
  • Assessed the impact of optimized peptides on pancreatic cancer cell viability.

Main Results:

  • GPU TI demonstrated high accuracy in predicting peptide binding affinities.
  • A 3-step TI protocol improved accuracy for charged side-chain mutations.
  • TI calculations successfully predicted beneficial mutations for a peptide targeting EME1, including non-canonical amino acids.
  • Validated mutations significantly increased peptide-target binding affinity.
  • Optimized peptides demonstrated a significant reduction in pancreatic cancer cell viability.

Conclusions:

  • GPU-accelerated TI is a powerful tool for accelerating the optimization of peptide therapeutics.
  • The developed pipeline accurately predicts beneficial mutations, leading to enhanced binding affinity.
  • Optimized peptides targeting EME1 show promise as a novel therapeutic strategy for pancreatic cancer.