  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.
