Molecular Dynamics in Principal Component Space
نویسندگان
چکیده
منابع مشابه
Molecular dynamics in principal component space.
A molecular dynamics algorithm in principal component space is presented. It is demonstrated that sampling can be improved without changing the ensemble by assigning masses to the principal components proportional to the inverse square root of the eigenvalues. The setup of the simulation requires no prior knowledge of the system; a short initial MD simulation to extract the eigenvectors and eig...
متن کاملPerturbational formulation of principal component analysis in molecular dynamics simulation.
Conformational fluctuations of a molecule are important to its function since such intrinsic fluctuations enable the molecule to respond to the external environmental perturbations. For extracting large conformational fluctuations, which predict the primary conformational change by the perturbation, principal component analysis (PCA) has been used in molecular dynamics simulations. However, sev...
متن کاملDihedral angle principal component analysis of molecular dynamics simulations.
It has recently been suggested by Mu et al. [Proteins 58, 45 (2005)] to use backbone dihedral angles instead of Cartesian coordinates in a principal component analysis of molecular dynamics simulations. Dihedral angles may be advantageous because internal coordinates naturally provide a correct separation of internal and overall motion, which was found to be essential for the construction and i...
متن کاملMolecular dynamics of apo-adenylate kinase: a principal component analysis.
Adenylate kinase from E. coli (AKE) is studied with molecular dynamics. AKE undergoes large-scale motions of its Lid and AMP-binding domains when its open form closes over its substrates, AMP and Mg2+-ATP. The third domain, the Core, is relatively stable during closing. The resulting trajectory is analyzed with a principal component analysis method that decomposes the atom motions into modes or...
متن کاملVector ℓ0 latent-space principal component analysis
Principal component analysis (PCA) is a widely used signal processing technique. Instead of performing PCA in the data space, we consider the problem of sparse PCA in a potentially higher dimensional latent space. To do so, we zero-out groups of variables using vector `0 regularization. The estimation is based on the maximization of the penalized log-likelihood, for which we develop an efficien...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: The Journal of Physical Chemistry B
سال: 2012
ISSN: 1520-6106,1520-5207
DOI: 10.1021/jp209964a