Unknown

Dataset Information

0

Monte Carlo simulation of a statistical mechanical model of multiple protein sequence alignment.


ABSTRACT: A grand canonical Monte Carlo (MC) algorithm is presented for studying the lattice gas model (LGM) of multiple protein sequence alignment, which coherently combines long-range interactions and variable-length insertions. MC simulations are used for both parameter optimization of the model and production runs to explore the sequence subspace around a given protein family. In this Note, I describe the details of the MC algorithm as well as some preliminary results of MC simulations with various temperatures and chemical potentials, and compare them with the mean-field approximation. The existence of a two-state transition in the sequence space is suggested for the SH3 domain family, and inappropriateness of the mean-field approximation for the LGM is demonstrated.

SUBMITTER: Kinjo AR 

PROVIDER: S-EPMC5551269 | biostudies-literature | 2017

REPOSITORIES: biostudies-literature

altmetric image

Publications

Monte Carlo simulation of a statistical mechanical model of multiple protein sequence alignment.

Kinjo Akira R AR  

Biophysics and physicobiology 20170712


A grand canonical Monte Carlo (MC) algorithm is presented for studying the lattice gas model (LGM) of multiple protein sequence alignment, which coherently combines long-range interactions and variable-length insertions. MC simulations are used for both parameter optimization of the model and production runs to explore the sequence subspace around a given protein family. In this Note, I describe the details of the MC algorithm as well as some preliminary results of MC simulations with various te  ...[more]

Similar Datasets

| S-EPMC3102636 | biostudies-literature
| S-EPMC9263979 | biostudies-literature
| S-EPMC5042171 | biostudies-literature
| S-EPMC6083219 | biostudies-literature
| S-EPMC7251717 | biostudies-literature
| S-EPMC3223366 | biostudies-literature
| S-EPMC6472439 | biostudies-literature
| S-EPMC4999523 | biostudies-other
| S-EPMC5123300 | biostudies-literature
| S-EPMC1861770 | biostudies-literature