Login / Signup

Molecular simulations by generalized-ensemble algorithms in isothermal-isobaric ensemble.

Masataka YamauchiYoshiharu MoriHisashi Okumura
Published in: Biophysical reviews (2019)
Generalized-ensemble algorithms are powerful techniques for investigating biomolecules such as protein, DNA, lipid membrane, and glycan. The generalized-ensemble algorithms were originally developed in the canonical ensemble. On the other hand, not only temperature but also pressure is controlled in experiments. Additionally, pressure is used as perturbation to study relationship between function and structure of biomolecules. For this reason, it is important to perform efficient conformation sampling based on the isothermal-isobaric ensemble. In this article, we review a series of the generalized-ensemble algorithms in the isothermal-isobaric ensemble: multibaric-multithermal, pressure- and temperature-simulated tempering, replica-exchange, and replica-permutation methods. These methods achieve more efficient simulation than the conventional isothermal-isobaric simulation. Furthermore, the isothermal-isobaric generalized-ensemble simulation samples conformations of biomolecules from wider range of temperature and pressure. Thus, we can estimate physical quantities more accurately at any temperature and pressure values. The applications to the biomolecular system are also presented.
Keyphrases
  • convolutional neural network
  • machine learning
  • neural network
  • deep learning
  • nucleic acid
  • molecular dynamics simulations
  • mental health
  • physical activity
  • single molecule
  • binding protein