Principal Component Analysis of MD Trajectories

By BioDockify Computational Research Team · August 9, 2026 · MD Analysis

How to use PCA to extract dominant motions from MD trajectories. Covers eigenvalue analysis, essential dynamics, free energy landscapes, and dynamic cross-correlation analysis from PCA modes.

Read the complete article with tables, code and references on BioDockify.

Key References

• Amadei, A. et al. Essential dynamics of proteins. Proteins 17, 412-425 (1993).
• David, C.C. & Jacobs, D.J. Principal component analysis: a method for determining the essential dynamics of large proteins. J. Mol. Graph. Model. 50, 58-64 (2014).
• Ichiye, T. & Karplus, M. Collective motions in proteins: a covariance analysis of atomic fluctuations. Proteins 11, 205-217 (1991).

Scope & Limitations

PCA requires sufficient frames for covariance estimation Linear method — cannot capture non-linear motions Cross-correlation captures only linear coupling between residues