Tag Archives: MDanalysis

Taming the Trajectory Beast: A Simpler Way to Sample Your MD Simulations

If you’ve ever run a molecular dynamics (MD) simulation, you know the feeling. You spend days, weeks, or even months of precious compute time watching your favourite molecule wiggle and jiggle. The result? A trajectory file bursting with thousands, or even millions, of frames. It’s a treasure trove of data, but it’s also a monster…

Analyzing every single frame is often impossible and, let’s be honest, usually pointless. Many adjacent frames are nearly identical. What we really want are the key representative structures that capture the important shapes, or conformations, your molecule adopted. So, how do we find them?

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Estimating uncertainty in MD observables using block averaging

When running molecular dynamics (MD) simulations, we are usually interested in measuring an ensemble average of some metric (e.g., RMSD, RMSF, radius of gyration, …) and use this to draw conclusions about the investigated system. While calculating the average value of a metric is straightforward (we can simply measure the metric in each frame and average it) calculating a statistical uncertainty is a little more tricky and often forgotten. The main challange when trying to calculate an uncertainty of MD oveservables is that individual frames of the simulation are not samped independently but they are time correlated (i.e., frame N depends on frame N-1). In this blog post, I will breifly introduce block averaging, a statistical technique to estimate uncertainty in correlated data.

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