# Interpretable machine learning of amino acid patterns in proteins: a statistical ensemble approach

arXiv: https://arxiv.org/abs/2303.15228 · HTML: https://mlsanity.com/paper/2303.15228/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 72 references that are in the map.
- First posted on arXiv 27 Mar 2023.

## Builds on (most cited first, 11 of 11)

- [A high-bias, low-variance introduction to Machine Learning for physicists](https://mlsanity.com/paper/1803.08823/) (2018)
- [DeepAffinity: Interpretable Deep Learning of Compound-Protein Affinity through Unified Recurrent and Convolutional Neural Networks](https://mlsanity.com/paper/1806.07537/) (2018)
- [Equilibrium and non-Equilibrium regimes in the learning of Restricted Boltzmann Machines](https://mlsanity.com/paper/2105.13889/) (2021)
- [Thermodynamics of Restricted Boltzmann Machines and related learning dynamics](https://mlsanity.com/paper/1803.01960/) (2018)
- [Emergence of Compositional Representations in Restricted Boltzmann Machines](https://mlsanity.com/paper/1611.06759/) (2016)
- [Explainable Deep Relational Networks for Predicting Compound-Protein Affinities and Contacts](https://mlsanity.com/paper/1912.12553/) (2019)
- [Barriers and Dynamical Paths in Alternating Gibbs Sampling of Restricted Boltzmann Machines](https://mlsanity.com/paper/2107.06013/) (2021)
- [Disentangling representations in Restricted Boltzmann Machines without adversaries](https://mlsanity.com/paper/2206.11600/) (2022)
- [A Survey on Protein Representation Learning: Retrospect and Prospect](https://mlsanity.com/paper/2301.00813/) (2022)
- [Unsupervised hierarchical clustering using the learning dynamics of RBMs](https://mlsanity.com/paper/2302.01851/) (2023)
- [Analogy between Boltzmann machines and Feynman path integrals](https://mlsanity.com/paper/2301.06217/) (2023)

## Built on by (most cited first, 3 of 3)

- [Neural networks consisting of DNA](https://mlsanity.com/paper/2501.03235/) (2024)
- [TangledFeatures: Robust Feature Selection in Highly Correlated Spaces](https://mlsanity.com/paper/2510.15005/) (2025)
- [Emergence of Nonequilibrium Latent Cycles in Unsupervised Generative Modeling](https://mlsanity.com/paper/2512.11415/) (2025)

## Abstract (arXiv)

Explainable and interpretable unsupervised machine learning helps understand the underlying structure of data. We introduce an ensemble analysis of machine learning models to consolidate their interpretation. Its application shows that restricted Boltzmann machines compress consistently into a few bits the information stored in a sequence of five amino acids at the start or end of α-helices or β-sheets. The weights learned by the machines reveal unexpected properties of the amino acids and the secondary structure of proteins: (i) His and Thr have a negligible contribution to the amphiphilic pattern of α-helices; (ii) there is a class of α-helices particularly rich in Ala at their end; (iii) Pro occupies most often slots otherwise occupied by polar or charged amino acids, and its presence at the start of helices is relevant; (iv) Glu and especially Asp on one side, and Val, Leu, Iso, and Phe on the other, display the strongest tendency to mark amphiphilic patterns, i.e., extreme values of an "effective hydrophobicity", though they are not the most powerful (non) hydrophobic amino acids.