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The Equilibrium Binding Constant and Binding Strength02:18

The Equilibrium Binding Constant and Binding Strength

The equilibrium binding constant (Kb) quantifies the strength of a protein-ligand interaction. Kb can be calculated as follows when the reaction is at equilibrium:
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Proteins are dynamic macromolecules that carry out a wide variety of essential processes; however, the activities of most proteins depend on their interactions with other molecules or ions, known as ligands.
Protein-ligand interactions are quite specific; even though numerous potential ligands surround a cellular protein at any given time, only a particular ligand can bind to that protein. Moreover, a ligand binds only to a dedicated area on the surface of the protein, known as the...

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Hironobu Kitajima1, Carlos Bistafa1, Takao Kobayashi2

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Protein WISDOM: A Workbench for In silico De novo Design of BioMolecules
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Protein WISDOM: A Workbench for In silico De novo Design of BioMolecules

Published on: July 25, 2013

Area of Science:

  • Quantum computing
  • Computational chemistry
  • Drug discovery

Background:

  • Protein-ligand interactions are crucial in drug design.
  • Accurate binding energy calculations are computationally intensive.
  • Quantum algorithms offer a potential solution for complex molecular simulations.

Purpose of the Study:

  • To evaluate the performance of quantum algorithms for protein-ligand binding energy calculations.
  • To assess the feasibility of using quantum computing on real hardware for drug design.
  • To investigate the impact of noise and approximations on quantum algorithm accuracy.

Main Methods:

  • A decomposition strategy using density matrix embedded theory was applied to protein-ligand systems (thrombin and 5 ligands).
  • Variational Quantum Eigensolver (VQE) was used for fragment energy calculation on a state vector simulator.
  • Coupled Cluster Singles and Doubles (CCSD) was used for remaining fragments, with protein treated as point charges.
  • Noisy Intermediate-Scale Quantum (NISQ) era approximations were evaluated.
  • VQE calculations were performed on a superconducting quantum computer.

Main Results:

  • Noiseless simulations validated the decomposition strategy using the Unitary CCSD ansatz.
  • Approximations necessary for NISQ hardware were assessed for their impact on computational cost and accuracy.
  • VQE calculations on a superconducting quantum computer demonstrated the potential of quantum algorithms.
  • Noise effects were analyzed through simulations and hardware experiments.
  • Quantum algorithms enhanced the binding energy correlation value.

Conclusions:

  • Quantum algorithms, including VQE, are viable for enhancing binding energy calculations in protein-ligand systems.
  • The employed decomposition strategy is effective for tackling complex systems on current and future quantum hardware.
  • This approach holds significant potential for advancing computer-aided drug design workflows.