Recovering a representative conformational ensemble from underdetermined macromolecular structural data.

Area of Science:

• Biophysics
• Structural Biology
• Computational Biology

Background:

• Macromolecular flexibility, due to linkers between domains, necessitates representing molecules as ensembles of conformations rather than single structures.
• Experimental data represent averages from multiple conformations, making direct deconvolution an ill-posed problem.
• Sparse approximations and compressive sensing offer solutions for recovering information from underdetermined systems.

Purpose of the Study:

• To develop a novel method, Sparse Ensemble Selection (SES), for recovering conformational ensembles from limited experimental data.
• To apply SES to Lys48-linked diubiquitin to obtain representative conformational ensembles under varying pH conditions.
• To validate and compare SES results against existing methods and experimental data.

Main Methods:

• Designed the Sparse Ensemble Selection (SES) method, inspired by sparse approximations and compressive sensing.
• Applied SES to Lys48-linked diubiquitin using residual dipolar coupling data measured at multiple pH values.
• Validated SES-derived ensembles against NMR chemical shift perturbation data and compared them with maximum-entropy results.

Main Results:

• SES accurately recovered representative conformational ensembles for Lys48-linked diubiquitin.
• The method reproduced and quantified the pH dependence of the major diubiquitin conformation.
• SES identified lesser-populated conformations preorganized for binding diubiquitin receptors.

Conclusions:

• SES provides a more general and accurate approach for determining conformational ensembles compared to previous methods.
• The identified conformations offer insights into the mechanisms of polyubiquitin-mediated receptor recognition.
• SES is broadly applicable to various experimental observables representable as linear combinations of state data.