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

TL;DR

This paper introduces a new tool to analyze molecular dynamics simulations by estimating the intrinsic dimension of biomolecular motions.

Contribution

MDIntrinsicDimension is a novel Python package that estimates intrinsic dimension from MD trajectories using rotation- and translation-invariant projections.

Findings

The package computes both overall and time-resolved intrinsic dimension from MD trajectories.

Intrinsic dimension complements geometric descriptors by highlighting localized flexibility and structural differences.

The method identifies metastable configurations in fast folding-unfolding simulations.

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 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, differentiating
structural segments, and identifying a metastable configuration.

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