Online Change Point Detection in Molecular Dynamics With Optical Random Features

TL;DR

This paper presents a real-time, hardware-efficient method for detecting conformational change points in molecular dynamics simulations using optical features, improving speed and memory efficiency over traditional methods.

Contribution

The study introduces an optical hardware implementation of the NEWMA algorithm for change point detection in high-dimensional, noisy MD data, enabling faster and more efficient analysis.

Findings

Successfully detects conformational changes in real-time

Outperforms traditional silicon hardware in speed and memory usage

Applicable to other high-dimensional sequential data

Abstract

Proteins are made of atoms constantly fluctuating, but can occasionally undergo large-scale changes. Such transitions are of biological interest, linking the structure of a protein to its function with a cell. Atomic-level simulations, such as Molecular Dynamics (MD), are used to study these events. However, molecular dynamics simulations produce time series with multiple observables, while changes often only affect a few of them. Therefore, detecting conformational changes has proven to be challenging for most change-point detection algorithms. In this work, we focus on the identification of such events given many noisy observables. In particular, we show that the No-prior-Knowledge Exponential Weighted Moving Average (NEWMA) algorithm can be used along optical hardware to successfully identify these changes in real-time. Our method does not need to distinguish between the background of a protein and the protein itself. For larger simulations, it is faster than using traditional silicon hardware and has a lower memory footprint. This technique may enhance the sampling of the conformational space of molecules. It may also be used to detect change-points in other sequential data with a large number of features.