Protein folding dynamics : ergodic behavior 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 کاملFast Protein Superfamily Classification Using Principal Component Null Space Analysis
The protein family classification problem, which consists of determining the family memberships of given unknown protein sequences, is very important for a biologist for many practical reasons, such as drug discovery, prediction of molecular functions and medical diagnosis. Neural networks and bayesian methods have performed well on the protein classification problem, achieving accuracy ranging...
متن کاملPrincipal Component Analysis and Long Time Protein Dynamics
It has been suggested that principal component analysis can identify slow modes in proteins and, thereby, facilitate the study of long time dynamics. However, sampling errors due to finite simulation times preclude the identification of slow modes that can be used for this purpose. This is demonstrated numerically with the aid of simulations of the protein G-actin and analytically with the aid ...
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 کاملUltrametricity in Protein Folding Dynamics.
The free energy of the transition state (TS) between two nodes of an ergodic Markov state model (MSM) can be obtained from the minimum cut, which is the set of edges that has the smallest sum of the flow capacities among all the possible cuts separating the two nodes. Here, we first show that the free energy of the TS is an ultrametric distance. The ultrametric property offers a way to simplify...
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: Seibutsu Butsuri
سال: 2003
ISSN: 0582-4052,1347-4219
DOI: 10.2142/biophys.43.s92_2