Replication Data for: Weighted persistent homology for biomolecular data analysis (doi:10.21979/N9/4II1CF)

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Replication Data for: Weighted persistent homology for biomolecular data analysis

doi:10.21979/N9/4II1CF

DR-NTU (Data)

2023-11-08

1

Meng, Zhenyu; Anand, D Vijay; Lu, Yunpeng; Wu, Jie; Xia, Kelin, 2023, "Replication Data for: Weighted persistent homology for biomolecular data analysis", https://doi.org/10.21979/N9/4II1CF, DR-NTU (Data), V1

Replication Data for: Weighted persistent homology for biomolecular data analysis

doi:10.21979/N9/4II1CF

Meng, Zhenyu (Nanyang Technological University)

Anand, D Vijay (Nanyang Technological University)

Lu, Yunpeng (Nanyang Technological University)

Wu, Jie (Hebei Normal University, China)

Xia, Kelin (Nanyang Technological University)

MATLAB

Python

MERLION

Startup Grant M4081842

Academic Research fund Tier 1 RG31/18

Academic Research fund Tier 2 MOE2018-T2-1-033.

DR-NTU (Data)

Xia, Kelin

Yadav, Yasharth

2023-11-08

https://doi.org/10.21979/N9/4II1CF

Mathematical Sciences, Mathematical Sciences, Persistent Homology, Topology, 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 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.

Calculated Data

10.1038/s41598-019-55660-3

Meng, Z., Anand, D. V., Lu, Y., Wu, J., & Xia, K. (2020). Weighted persistent homology for biomolecular data analysis. Scientific reports, 10(1), 2079.

10356/146211

Meng, Z., Anand, D. V., Lu, Y., Wu, J., & Xia, K. (2020). Weighted persistent homology for biomolecular data analysis. Scientific Reports, 10(1), 2079-.

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WPH_code.rar

Text:

Data and codes to generate LWPH based features for A-, B-, and Z-types of DNA

application/x-rar