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Area of Science:

  • Biochemistry and structural biology
  • Computational chemistry
  • Drug discovery

Background:

  • Biomacromolecule structures are crucial for drug development and biocatalysis.
  • Quantum refinement (QR) improves biomacromolecule structural quality but is computationally expensive.
  • Existing quantum mechanics/molecular mechanics (QM/MM) setups for QR are complex.

Purpose of the Study:

  • To develop a more efficient and accurate QR method for biomacromolecule structures.
  • To reduce the computational cost associated with traditional QR methods.
  • To provide deeper atomistic insights into protein-drug interactions.

Main Methods:

  • Incorporation of machine learning potentials (MLPs) into multiscale ONIOM(QM:MM) schemes.
  • Utilizing two levels of MLPs to enhance accuracy and overcome limitations.
  • Applying the developed MLPs+ONIOM-based QR method to protein-drug complexes.

Main Results:

  • Achieved quantum mechanics (QM)-level accuracy with significantly improved computational efficiency.
  • Successfully refined protein-drug complexes, including the SARS-CoV-2 main protease.
  • Provided computational evidence for distinct bonded and nonbonded forms of nirmatrelvir.

Conclusions:

  • MLPs combined with ONIOM-based QR offer a powerful and efficient approach for biomacromolecule structure refinement.
  • This method accelerates the analysis of protein-drug complexes, aiding drug development.
  • The approach provides unprecedented atomistic detail for understanding drug mechanisms.