Metric Similarity and Manifold Learning of Circular Dichroism Spectra of Proteins

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.

@article{marchetti2025_2504.19355,
  title={ Metric Similarity and Manifold Learning of Circular Dichroism Spectra of Proteins },
  author={ Gionni Marchetti },
  journal={arXiv preprint arXiv:2504.19355},
  year={ 2025 }
}