MESMER

MESMER performs ensemble-based quantitative analysis of macromolecular structural heterogeneity by fitting predicted data from collections of models to experimental observables.

Key Features:

• Ensemble-Based Modeling: Predicted data are computed from collections of models and compared to observed experimental results to analyze ranges and populations of accessible structures.
• Orthogonal Structural Data Integration: Simultaneous fitting of small-angle X-ray scattering (SAXS), nuclear magnetic resonance residual dipolar couplings (RDCs), and dipolar electron-electron resonance spectra is supported.
• Population Refinement: Refines thousands of candidate component collections selected from an input pool and identifies ensembles that best recapitulate experimental data by selecting those that fit better than their peers.
• Modular Python Plugins: Modular Python plugins enable computation and fitting of data from a wide range of quantitative experimental datasets.

Scientific Applications:

• Conformational Heterogeneity Analysis: Applied to analyze conformational heterogeneity across three distinct macromolecular systems.
• Structural Biology and Biophysics: Supports studies in structural biology, biophysics, and molecular dynamics by enabling quantitative interpretation of heterogeneous structural ensembles.

Methodology:

Predicted observables are computed from collections of structural models and directly compared to experimental data; orthogonal data types including SAXS, NMR RDCs, and dipolar electron-electron resonance spectra are fit simultaneously; thousands of candidate component collections from an input pool are refined to select ensembles with superior fit; modular Python plugins perform computation and fitting for diverse experimental datasets.