MMM: Integrative ensemble modeling and ensemble analysis.

Area of Science:

• Structural Biology
• Computational Biology
• Biophysics

Background:

• Proteins and their complexes often exhibit heterogeneous disorder, posing challenges for ensemble modeling.
• Existing methods struggle with integrating diverse experimental restraints, estimating ensemble width, sampling conformational space, and representing large ensembles.

Purpose of the Study:

• To introduce novel computational tools, RigiFlex and EnsembleFit, to address key problems in ensemble modeling of disordered protein systems.
• To enable accurate integration of diverse experimental data and provide interpretable, reduced ensembles.

Main Methods:

• The RigiFlex approach integrates distance distribution information with high-resolution structures and small-angle scattering data.
• EnsembleFit module reduces ensemble size by fitting conformer populations and discarding low-population conformers, balancing fit quality.
• Pair correlation and local compaction analyses are employed for feature detection.

Main Results:

• RigiFlex accurately recovered a simulated 70 kDa protein-RNA complex (RsmE/RsmZ) with an ensemble width and difference from ground truth around 4.2 Å.
• EnsembleFit significantly reduced the conformer count for p15^PAF from 4,939 to 75 while maintaining good restraint fit quality.
• Local compaction analysis revealed correlations between compactness and residual dipolar couplings in the PaaA2 antitoxin.

Conclusions:

• The RigiFlex pipeline and EnsembleFit module offer effective solutions for modeling heterogeneous protein disorder.
• These tools facilitate the integration of multi-technique data and the generation of interpretable, reduced conformational ensembles.
• The methods provide site-specific insights into protein dynamics and weak order.