Weighted persistent homology for osmolyte molecular aggregation and hydrogen-bonding network analysis

It has long been observed that trimethylamine N-oxide (TMAO) and urea demonstrate dramatically different properties in a protein folding process. Even with the enormous theoretical and experimental research work on these two osmolytes, various aspects of their underlying mechanisms still remain larg...

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Main Authors: Anand, D. Vijay, Meng, Zhenyu, Xia, Kelin, Mu, Yuguang
Other Authors: School of Physical and Mathematical Sciences
Format: Article
Language:English
Published: 2021
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Online Access:https://hdl.handle.net/10356/146107
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Institution: Nanyang Technological University
Language: English
id sg-ntu-dr.10356-146107
record_format dspace
institution Nanyang Technological University
building NTU Library
continent Asia
country Singapore
Singapore
content_provider NTU Library
collection DR-NTU
language English
topic Science::Biological sciences
Biochemistry
Structural Biology
spellingShingle Science::Biological sciences
Biochemistry
Structural Biology
Anand, D. Vijay
Meng, Zhenyu
Xia, Kelin
Mu, Yuguang
Weighted persistent homology for osmolyte molecular aggregation and hydrogen-bonding network analysis
description It has long been observed that trimethylamine N-oxide (TMAO) and urea demonstrate dramatically different properties in a protein folding process. Even with the enormous theoretical and experimental research work on these two osmolytes, various aspects of their underlying mechanisms still remain largely elusive. In this paper, we propose to use the weighted persistent homology to systematically study the osmolytes molecular aggregation and their hydrogen-bonding network from a local topological perspective. We consider two weighted models, i.e., localized persistent homology (LPH) and interactive persistent homology (IPH). Boltzmann persistent entropy (BPE) is proposed to quantitatively characterize the topological features from LPH and IPH, together with persistent Betti number (PBN). More specifically, from the localized persistent homology models, we have found that TMAO and urea have very different local topology. TMAO is found to exhibit a local network structure. With the concentration increase, the circle elements in these networks show a clear increase in their total numbers and a decrease in their relative sizes. In contrast, urea shows two types of local topological patterns, i.e., local clusters around 6 Å and a few global circle elements at around 12 Å. From the interactive persistent homology models, it has been found that our persistent radial distribution function (PRDF) from the global-scale IPH has same physical properties as the traditional radial distribution function. Moreover, PRDFs from the local-scale IPH can also be generated and used to characterize the local interaction information. Other than the clear difference of the first peak value of PRDFs at filtration size 4 Å, TMAO and urea also shows very different behaviors at the second peak region from filtration size 5 Å to 10 Å. These differences are also reflected in the PBNs and BPEs of the local-scale IPH. These localized topological information has never been revealed before. Since graphs can be transferred into simplicial complexes by the clique complex, our weighted persistent homology models can be used in the analysis of various networks and graphs from any molecular structures and aggregation systems.
author2 School of Physical and Mathematical Sciences
author_facet School of Physical and Mathematical Sciences
Anand, D. Vijay
Meng, Zhenyu
Xia, Kelin
Mu, Yuguang
format Article
author Anand, D. Vijay
Meng, Zhenyu
Xia, Kelin
Mu, Yuguang
author_sort Anand, D. Vijay
title Weighted persistent homology for osmolyte molecular aggregation and hydrogen-bonding network analysis
title_short Weighted persistent homology for osmolyte molecular aggregation and hydrogen-bonding network analysis
title_full Weighted persistent homology for osmolyte molecular aggregation and hydrogen-bonding network analysis
title_fullStr Weighted persistent homology for osmolyte molecular aggregation and hydrogen-bonding network analysis
title_full_unstemmed Weighted persistent homology for osmolyte molecular aggregation and hydrogen-bonding network analysis
title_sort weighted persistent homology for osmolyte molecular aggregation and hydrogen-bonding network analysis
publishDate 2021
url https://hdl.handle.net/10356/146107
_version_ 1772825745481007104
spelling sg-ntu-dr.10356-1461072023-06-13T03:32:11Z Weighted persistent homology for osmolyte molecular aggregation and hydrogen-bonding network analysis Anand, D. Vijay Meng, Zhenyu Xia, Kelin Mu, Yuguang School of Physical and Mathematical Sciences School of Biological Sciences Science::Biological sciences Biochemistry Structural Biology It has long been observed that trimethylamine N-oxide (TMAO) and urea demonstrate dramatically different properties in a protein folding process. Even with the enormous theoretical and experimental research work on these two osmolytes, various aspects of their underlying mechanisms still remain largely elusive. In this paper, we propose to use the weighted persistent homology to systematically study the osmolytes molecular aggregation and their hydrogen-bonding network from a local topological perspective. We consider two weighted models, i.e., localized persistent homology (LPH) and interactive persistent homology (IPH). Boltzmann persistent entropy (BPE) is proposed to quantitatively characterize the topological features from LPH and IPH, together with persistent Betti number (PBN). More specifically, from the localized persistent homology models, we have found that TMAO and urea have very different local topology. TMAO is found to exhibit a local network structure. With the concentration increase, the circle elements in these networks show a clear increase in their total numbers and a decrease in their relative sizes. In contrast, urea shows two types of local topological patterns, i.e., local clusters around 6 Å and a few global circle elements at around 12 Å. From the interactive persistent homology models, it has been found that our persistent radial distribution function (PRDF) from the global-scale IPH has same physical properties as the traditional radial distribution function. Moreover, PRDFs from the local-scale IPH can also be generated and used to characterize the local interaction information. Other than the clear difference of the first peak value of PRDFs at filtration size 4 Å, TMAO and urea also shows very different behaviors at the second peak region from filtration size 5 Å to 10 Å. These differences are also reflected in the PBNs and BPEs of the local-scale IPH. These localized topological information has never been revealed before. Since graphs can be transferred into simplicial complexes by the clique complex, our weighted persistent homology models can be used in the analysis of various networks and graphs from any molecular structures and aggregation systems. Ministry of Education (MOE) Nanyang Technological University Published version This work was supported in part by Nanyang Technological University Startup Grant M4081842 and Singapore Ministry of Education Academic Research fund Tier 1 RG31/18, Tier 2 MOE2018-T2–1–033. 2021-01-26T08:58:14Z 2021-01-26T08:58:14Z 2020 Journal Article Anand, D. V., Meng, Z., Xia, K., & Mu, Y. (2020). Weighted persistent homology for osmolyte molecular aggregation and hydrogen-bonding network analysis. Scientific Reports, 10(1), 9685-. doi:10.1038/s41598-020-66710-6 2045-2322 https://hdl.handle.net/10356/146107 10.1038/s41598-020-66710-6 32546801 2-s2.0-85086588881 1 10 en M4081842 RG31/18 MOE2018-T2–1–033 Scientific Reports 10.21979/N9/IXD1QX © 2020 The Author(s). This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/. application/pdf