Weighted persistent homology for biomolecular data analysis
In this paper, we systematically review weighted persistent homology (WPH) models and their applications in biomolecular data analysis. Essentially, the weight value, which reflects physical, chemical and biological properties, can be assigned to vertices (atom centers), edges (bonds), or higher ord...
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sg-ntu-dr.10356-1462112023-11-10T00:38:10Z Weighted persistent homology for biomolecular data analysis Meng, Zhenyu Anand, D. Vijay Lu, Yunpeng Wu, Jie Xia, Kelin School of Physical and Mathematical Sciences School of Biological Sciences Science::Biological sciences Biophysical Chemistry Structural Biology In this paper, we systematically review weighted persistent homology (WPH) models and their applications in biomolecular data analysis. Essentially, the weight value, which reflects physical, chemical and biological properties, can be assigned to vertices (atom centers), edges (bonds), or higher order simplexes (cluster of atoms), depending on the biomolecular structure, function, and dynamics properties. Further, we propose the first localized weighted persistent homology (LWPH). Inspired by the great success of element specific persistent homology (ESPH), we do not treat biomolecules as an inseparable system like all previous weighted models, instead we decompose them into a series of local domains, which may be overlapped with each other. The general persistent homology or weighted persistent homology analysis is then applied on each of these local domains. In this way, functional properties, that are embedded in local structures, can be revealed. Our model has been applied to systematically study DNA structures. It has been found that our LWPH based features can be used to successfully discriminate the A-, B-, and Z-types of DNA. More importantly, our LWPH based principal component analysis (PCA) model can identify two configurational states of DNA structures in ion liquid environment, which can be revealed only by the complicated helical coordinate system. The great consistence with the helical-coordinate model demonstrates that our model captures local structure variations so well that it is comparable with geometric models. Moreover, geometric measurements are usually defined in local regions. For instance, the helical-coordinate system is limited to one or two basepairs. However, our LWPH can quantitatively characterize structure information in regions or domains with arbitrary sizes and shapes, where traditional geometrical measurements fail. 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. 2021-02-02T06:04:47Z 2021-02-02T06:04:47Z 2020 Journal Article Meng, Z., Anand, D. V., Lu, Y., Wu, J., & Xia, K. (2020). Weighted persistent homology for biomolecular data analysis. Scientific Reports, 10(1), 2079-. doi:10.1038/s41598-019-55660-3 2045-2322 https://hdl.handle.net/10356/146211 10.1038/s41598-019-55660-3 32034168 2-s2.0-85079080103 1 10 en M4081842 RG31/18 MOE2018-T2-1-033 Scientific Reports 10.21979/N9/4II1CF © 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 |
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Science::Biological sciences Biophysical Chemistry Structural Biology Meng, Zhenyu Anand, D. Vijay Lu, Yunpeng Wu, Jie Xia, Kelin Weighted persistent homology for biomolecular data analysis |
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In this paper, we systematically review weighted persistent homology (WPH) models and their applications in biomolecular data analysis. Essentially, the weight value, which reflects physical, chemical and biological properties, can be assigned to vertices (atom centers), edges (bonds), or higher order simplexes (cluster of atoms), depending on the biomolecular structure, function, and dynamics properties. Further, we propose the first localized weighted persistent homology (LWPH). Inspired by the great success of element specific persistent homology (ESPH), we do not treat biomolecules as an inseparable system like all previous weighted models, instead we decompose them into a series of local domains, which may be overlapped with each other. The general persistent homology or weighted persistent homology analysis is then applied on each of these local domains. In this way, functional properties, that are embedded in local structures, can be revealed. Our model has been applied to systematically study DNA structures. It has been found that our LWPH based features can be used to successfully discriminate the A-, B-, and Z-types of DNA. More importantly, our LWPH based principal component analysis (PCA) model can identify two configurational states of DNA structures in ion liquid environment, which can be revealed only by the complicated helical coordinate system. The great consistence with the helical-coordinate model demonstrates that our model captures local structure variations so well that it is comparable with geometric models. Moreover, geometric measurements are usually defined in local regions. For instance, the helical-coordinate system is limited to one or two basepairs. However, our LWPH can quantitatively characterize structure information in regions or domains with arbitrary sizes and shapes, where traditional geometrical measurements fail. |
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School of Physical and Mathematical Sciences |
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School of Physical and Mathematical Sciences Meng, Zhenyu Anand, D. Vijay Lu, Yunpeng Wu, Jie Xia, Kelin |
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Article |
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Meng, Zhenyu Anand, D. Vijay Lu, Yunpeng Wu, Jie Xia, Kelin |
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Meng, Zhenyu |
title |
Weighted persistent homology for biomolecular data analysis |
title_short |
Weighted persistent homology for biomolecular data analysis |
title_full |
Weighted persistent homology for biomolecular data analysis |
title_fullStr |
Weighted persistent homology for biomolecular data analysis |
title_full_unstemmed |
Weighted persistent homology for biomolecular data analysis |
title_sort |
weighted persistent homology for biomolecular data analysis |
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2021 |
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https://hdl.handle.net/10356/146211 |
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