Circula-based multivariate distributions on the flat torus, with applications in structural biology

TL;DR

This paper introduces a novel class of normalized multivariate distributions on the flat torus using circula models, with applications to modeling torsion angles in proteins, advancing structural biology analysis.

Contribution

It presents the first closed-form normalized distribution on the flat torus with covariance structure and models for joint torsion angles in proteins.

Findings

Models achieve state-of-the-art likelihood and sparsity.

Mixtures on flat torii from T^2 to T^14 are effective.

Framework advances understanding of protein torsion angles.

Abstract

Modeling dependencies between random variables independently from their marginals is fundamental in applications ranging from finance to (structural) biology. In this work, we undertake this problem using circula to model data living on the $d$-dimensional flat torus $\mathbb{T}^d$, making two contributions. First, using a low rank covariance structure to define circulae based on a latent variable model, we design the first closed-form normalized distribution on the flat torus $\mathbb{T}^d$--with covariance structure. Second, building on this framework, we propose the first models for joint distributions of torsion angles (backbone and side-chains) for neighboring amino-acids in proteins. In practice, we fit mixtures on flat torii from $\mathbb{T}^{2}$ to $\mathbb{T}^{14}$, and show they are SOTA in terms of likelihood and sparsity. We anticipate that these models will prove fundamental to move from discrete structural studies like in AlphaFold2, to thermodynamics and kinetics, which are the ultimate goals in theoretical biophysics.