MESMER


Minimal Ensemble Solutions to Multiple Experimental Restraints

Historically, scientists have attempted to understand the behavior of proteins and nucleic acids from static representations of their structures. In the cell, however, proteins and nucleic acids are in constant motion and constantly interacting with other components. This dynamic behavior, although of utmost importance, is difficult to probe experimentally.

MESMER is a cross-platform suite of tools that uses collections of static structures (ensembles) to fit available experimental observations. This provides an intuitive way to quantitatively investigate the variety of conformations and/or configurations adopted by a single component or even a complex mixture of components.

What makes MESMER unique is its ease of use and its ability to simultaneously integrate multiple datasets from a variety of techniques to restrain potential ensembles. If you are faced with a collection of data from a complex system, MESMER may be just the tool you’re looking for.

The MESMER suite

  • Native graphical interface for Mac, Windows, and Linux
  • Sampling of protein conformation using Monte-Carlo randomization of Phi-Psi angles
  • Integrated calculation of predicted experimental data (e.g. SAXS, RDCs, PREs)
  • Powerful genetic algorithm engine for ensemble optimization
  • Fully extendable via plugins for nearly any type of experimental data
  • HPC/Supercomputer aware
  • Full complement of command-line tools for workflow automation



Download

MESMER documentation

Download MESMER

Look at the MESMER source code

Citing

MESMER is Open Source and licensed under the GNU General Public License (GPL). This makes it free to anyone to download, use, and modify. MESMER was originally developed by Elihu Ihms’ in Mark Foster’s lab at The Ohio State University.

If you use MESMER in your work, please cite the MESMER paper in Bioinformatics:

Ihms, Elihu C., and Mark P. Foster. “MESMER: minimal ensemble solutions to multiple experimental restraints.” Bioinformatics (2015): 10.1093/bioinformatics/btv079