# Metric Similarity and Manifold Learning of Circular Dichroism Spectra of Proteins

arXiv: https://arxiv.org/abs/2504.19355 · HTML: https://mlsanity.com/paper/2504.19355/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 2 of its 56 references that are in the map.
- First posted on arXiv 27 Apr 2025.

## Builds on (most cited first, 2 of 2)

- [Computational Optimal Transport](https://mlsanity.com/paper/1803.00567/) (2018)
- [A Transportation Lp Distance for Signal Analysis](https://mlsanity.com/paper/1609.08669/) (2016)

## Abstract (arXiv)

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.