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MolClustPy: A Python Package to Characterize Multivalent Biomolecular Clusters

Low-affinity interactions among multivalent biomolecules may lead to the formation of molecular complexes that undergo phase transitions to become extra-large clusters. Characterizing the physical properties of these clusters is important in recent biophysical research. Due to weak interactions such...

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Detalles Bibliográficos
Autores principales: Chattaraj, Aniruddha, Nalagandla, Indivar, Loew, Leslie M., Blinov, Michael L
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Cold Spring Harbor Laboratory 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10055112/
https://www.ncbi.nlm.nih.gov/pubmed/36993613
http://dx.doi.org/10.1101/2023.03.14.532640
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author Chattaraj, Aniruddha
Nalagandla, Indivar
Loew, Leslie M.
Blinov, Michael L
author_facet Chattaraj, Aniruddha
Nalagandla, Indivar
Loew, Leslie M.
Blinov, Michael L
author_sort Chattaraj, Aniruddha
collection PubMed
description Low-affinity interactions among multivalent biomolecules may lead to the formation of molecular complexes that undergo phase transitions to become extra-large clusters. Characterizing the physical properties of these clusters is important in recent biophysical research. Due to weak interactions such clusters are highly stochastic, demonstrating a wide range of sizes and compositions. We have developed a Python package to perform multiple stochastic simulation runs using NFsim (Network-Free stochastic simulator), characterize and visualize the distribution of cluster sizes, molecular composition, and bonds across molecular clusters and individual molecules of different types.
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spelling pubmed-100551122023-03-30 MolClustPy: A Python Package to Characterize Multivalent Biomolecular Clusters Chattaraj, Aniruddha Nalagandla, Indivar Loew, Leslie M. Blinov, Michael L bioRxiv Article Low-affinity interactions among multivalent biomolecules may lead to the formation of molecular complexes that undergo phase transitions to become extra-large clusters. Characterizing the physical properties of these clusters is important in recent biophysical research. Due to weak interactions such clusters are highly stochastic, demonstrating a wide range of sizes and compositions. We have developed a Python package to perform multiple stochastic simulation runs using NFsim (Network-Free stochastic simulator), characterize and visualize the distribution of cluster sizes, molecular composition, and bonds across molecular clusters and individual molecules of different types. Cold Spring Harbor Laboratory 2023-03-15 /pmc/articles/PMC10055112/ /pubmed/36993613 http://dx.doi.org/10.1101/2023.03.14.532640 Text en https://creativecommons.org/licenses/by-nc-nd/4.0/This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License (https://creativecommons.org/licenses/by-nc-nd/4.0/) , which allows reusers to copy and distribute the material in any medium or format in unadapted form only, for noncommercial purposes only, and only so long as attribution is given to the creator.
spellingShingle Article
Chattaraj, Aniruddha
Nalagandla, Indivar
Loew, Leslie M.
Blinov, Michael L
MolClustPy: A Python Package to Characterize Multivalent Biomolecular Clusters
title MolClustPy: A Python Package to Characterize Multivalent Biomolecular Clusters
title_full MolClustPy: A Python Package to Characterize Multivalent Biomolecular Clusters
title_fullStr MolClustPy: A Python Package to Characterize Multivalent Biomolecular Clusters
title_full_unstemmed MolClustPy: A Python Package to Characterize Multivalent Biomolecular Clusters
title_short MolClustPy: A Python Package to Characterize Multivalent Biomolecular Clusters
title_sort molclustpy: a python package to characterize multivalent biomolecular clusters
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10055112/
https://www.ncbi.nlm.nih.gov/pubmed/36993613
http://dx.doi.org/10.1101/2023.03.14.532640
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