Metric Similarity and Manifold Learning of Circular Dichroism Spectra of Proteins

• URL: http://arxiv.org/abs/2504.19355v2
• Date: Tue, 13 May 2025 14:15:55 GMT
• Title: Metric Similarity and Manifold Learning of Circular Dichroism Spectra of Proteins
• Abstract summary: We present a machine learning analysis of circular dichroism spectra of globular proteins from the SP175 database.<n>Our results demonstrate that $mathcalW_1$ is consistent with both Euclidean and Manhattan metrics while exhibiting robustness to noise.<n>On the other hand, $t$-SNE uncovers meaningful structure in the high-dimensional data.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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 $\beta$-rich proteins, while the other consists mainly of proteins with mixed $\alpha/\beta$ and $\alpha$-helical content.

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