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

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 produc...

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Detalles Bibliográficos
Autor principal: Kinjo, Akira R.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: The Biophysical Society of Japan (BSJ) 2017
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5551269/
https://www.ncbi.nlm.nih.gov/pubmed/28828285
http://dx.doi.org/10.2142/biophysico.14.0_99
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author Kinjo, Akira R.
author_facet Kinjo, Akira R.
author_sort Kinjo, Akira R.
collection PubMed
description 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.
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spelling pubmed-55512692017-08-21 Monte Carlo simulation of a statistical mechanical model of multiple protein sequence alignment Kinjo, Akira R. Biophys Physicobiol Note 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. The Biophysical Society of Japan (BSJ) 2017-07-12 /pmc/articles/PMC5551269/ /pubmed/28828285 http://dx.doi.org/10.2142/biophysico.14.0_99 Text en 2017 © The Biophysical Society of Japan
spellingShingle Note
Kinjo, Akira R.
Monte Carlo simulation of a statistical mechanical model of multiple protein sequence alignment
title Monte Carlo simulation of a statistical mechanical model of multiple protein sequence alignment
title_full Monte Carlo simulation of a statistical mechanical model of multiple protein sequence alignment
title_fullStr Monte Carlo simulation of a statistical mechanical model of multiple protein sequence alignment
title_full_unstemmed Monte Carlo simulation of a statistical mechanical model of multiple protein sequence alignment
title_short Monte Carlo simulation of a statistical mechanical model of multiple protein sequence alignment
title_sort monte carlo simulation of a statistical mechanical model of multiple protein sequence alignment
topic Note
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5551269/
https://www.ncbi.nlm.nih.gov/pubmed/28828285
http://dx.doi.org/10.2142/biophysico.14.0_99
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