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The Aortic Ring Co-culture Assay: A Convenient Tool to Assess the Angiogenic Potential of Mesenchymal Stromal Cells In Vitro

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MyPMFs: a simple tool for creating statistical potentials to assess protein structural models.

Guillaume Postic¹, Thomas Hamelryck², Jacques Chomilier¹

¹Sorbonne Université, UMR 7590 CNRS, MNHN, IRD, Institut de Minéralogie de Physique des Matériaux et de Cosmochimie (IMPMC), Paris, France.

|June 2, 2018

Summary

New software, MyPMFs, allows researchers to train custom statistical potentials of mean force (PMFs) for evaluating protein structure quality. This tool adapts scoring functions to specific protein environments, improving accuracy for diverse protein types.

Keywords:

Kernel density estimations Potentials of mean force Protein model quality Residue interactions Statistical potentials

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Area of Science:

Computational biology
Structural bioinformatics
Biophysics

Background:

Protein structure quality assessment requires scoring functions tailored to specific environments.
Existing computational methods often fail for membrane or secreted proteins due to differing physicochemical properties.

Purpose of the Study:

To introduce MyPMFs, a flexible tool for training custom statistical potentials of mean force (PMFs).
To enable adaptation of scoring functions to unique protein residue compositions and structural characteristics.

Main Methods:

Development of an open-source software tool, MyPMFs.
User-trainable statistical potentials of mean force (PMFs) with adjustable parameters.
Demonstration of PMF training for transmembrane protein domains.

Main Results:

MyPMFs successfully generates accurate statistical potentials for specific protein environments.
The tool facilitates the study of how physical environments influence residue interactions.
An accurate statistical potential for transmembrane protein domains was created.

Conclusions:

MyPMFs provides a versatile solution for developing environment-specific protein scoring functions.
The software enhances the evaluation of protein model quality across diverse biological contexts.
This approach improves the study of protein structure-environment relationships.