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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...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Cold Spring Harbor Laboratory
2023
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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. |
format | Online Article Text |
id | pubmed-10055112 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Cold Spring Harbor Laboratory |
record_format | MEDLINE/PubMed |
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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