Multilevel superposition for deciphering the conformational variability of protein ensembles

Area of Science:

• Structural biology
• Biophysics
• Computational chemistry

Background:

• Protein conformation dynamics are crucial for function.
• Comparative analysis of protein structures, especially from X-ray crystallography, is a powerful tool.
• Increasing numbers of experimentally determined structures facilitate such comparisons.

Purpose of the Study:

• To develop a novel multilevel model for estimating inter- and intra-ensemble variability in protein structures.
• To apply Principal Component Analysis (PCA) to these variability estimates for functional insights.
• To demonstrate the method's utility with examples like cytochrome P450 and beta-lactamase enzymes.

Main Methods:

• Development of a multilevel model to estimate two covariance matrices.
• Representation of inter- and intra-ensemble variability in Cartesian coordinate space.
• Application of PCA on the estimated covariance matrices.

Main Results:

• The model successfully identified key inter- and intra-enzyme variabilities linked to protein function.
• In cytochrome P450 enzymes, a universal active-site motion was captured, highlighting functional adaptability despite varying active site sizes.
• The method proved effective in understanding conformational variability after accounting for structural differences between enzymes.

Conclusions:

• The developed multilevel model is effective for analyzing protein conformational variability.
• The method is particularly advantageous for studies with small ensemble sizes, common in experimental structural data.
• This approach offers a promising tool for comparative studies of experimentally determined protein structures.