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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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Formato: | Online Artículo Texto |
Lenguaje: | English |
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The Biophysical Society of Japan (BSJ)
2017
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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. |
format | Online Article Text |
id | pubmed-5551269 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2017 |
publisher | The Biophysical Society of Japan (BSJ) |
record_format | MEDLINE/PubMed |
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 |
work_keys_str_mv | AT kinjoakirar montecarlosimulationofastatisticalmechanicalmodelofmultipleproteinsequencealignment |