Metric Similarity and Manifold Learning of Circular Dichroism Spectra of Proteins
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arXiv
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| Format: | Preprint |
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2025
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| _version_ | 1866912372477657088 |
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| author | Marchetti, Gionni |
| author_facet | Marchetti, Gionni |
| contents | We present a machine learning analysis of circular dichroism spectra of globular proteins from the SP175 database, using the optimal transport-based $1$-Wasserstein distance $\mathcal{W}_1$ (with order $p=1$) and the manifold learning algorithm $t$-SNE. Our results demonstrate that $\mathcal{W}_1$ is consistent with both Euclidean and Manhattan metrics while exhibiting robustness to noise. On the other hand, $t$-SNE uncovers meaningful structure in the high-dimensional data. The clustering in the $t$-SNE embedding is primarily determined by proteins with distinct secondary structure compositions: one cluster predominantly contains $β$-rich proteins, while the other consists mainly of proteins with mixed $α/β$ and $α$-helical content. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2504_19355 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Metric Similarity and Manifold Learning of Circular Dichroism Spectra of Proteins Marchetti, Gionni Physics and Society Machine Learning We present a machine learning analysis of circular dichroism spectra of globular proteins from the SP175 database, using the optimal transport-based $1$-Wasserstein distance $\mathcal{W}_1$ (with order $p=1$) and the manifold learning algorithm $t$-SNE. Our results demonstrate that $\mathcal{W}_1$ is consistent with both Euclidean and Manhattan metrics while exhibiting robustness to noise. On the other hand, $t$-SNE uncovers meaningful structure in the high-dimensional data. The clustering in the $t$-SNE embedding is primarily determined by proteins with distinct secondary structure compositions: one cluster predominantly contains $β$-rich proteins, while the other consists mainly of proteins with mixed $α/β$ and $α$-helical content. |
| title | Metric Similarity and Manifold Learning of Circular Dichroism Spectra of Proteins |
| topic | Physics and Society Machine Learning |
| url | https://arxiv.org/abs/2504.19355 |