MDIntrinsicDimension: Dimensionality-Based Analysis of Collective Motions in Macromolecules from Molecular Dynamics Trajectories

TL;DR

This paper introduces MDIntrinsicDimension, a Python tool for estimating the intrinsic dimensionality of molecular dynamics trajectories, aiding in understanding biomolecular conformational complexity and flexibility.

Contribution

The paper presents a novel, open-source Python package that estimates the intrinsic dimension of MD data using invariant projections and multiple analysis modes.

Findings

ID reveals localized flexibility in biomolecules.

ID analysis distinguishes folding-unfolding states.

ID complements traditional geometric descriptors.

Abstract

Molecular dynamics (MD) simulations provide atomistic insights into the structure, dynamics, and function of biomolecules by generating time-resolved, high-dimensional trajectories. Analyzing such data benefits from estimating the minimal number of variables required to describe the explored conformational manifold, known as the intrinsic dimension (ID). We present MDIntrinsicDimension, an open-source Python package that estimates ID directly from MD trajectories by combining rotation- and translation-invariant molecular projections (e.g., backbone dihedrals and inter-residue distances) with state-of-the-art estimators. The package provides three complementary analysis modes: whole-molecule ID; sliding windows along the sequence; and per-secondary-structure elements. It computes both overall ID (a single summary value) and instantaneous, time-resolved ID that can reveal transitions and heterogeneity over time. We illustrate the approach on fast folding-unfolding trajectories from the DESRES dataset, demonstrating that ID complements conventional geometric descriptors by highlighting spatially localized flexibility and differences across structural segments.