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 W1 (with order p=1) and the manifold learning algorithm t-SNE. Our results demonstrate that W1 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.