Principal component analysis of molecular clouds: can CO reveal the dynamics?
نویسندگان
چکیده
منابع مشابه
Dihedral 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 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 کاملMolecular 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 کاملCompression of Breast Cancer Images By Principal Component Analysis
The principle of dimensionality reduction with PCA is the representation of the dataset ‘X’in terms of eigenvectors ei ∈ RN of its covariance matrix. The eigenvectors oriented in the direction with the maximum variance of X in RN carry the most relevant information of X. These eigenvectors are called principal components [8]. Ass...
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: Monthly Notices of the Royal Astronomical Society
سال: 2014
ISSN: 1365-2966,0035-8711
DOI: 10.1093/mnras/stu284