Raffaele Fiorentini^1,2*, Thomas Tarenzi^1,2,3*, Giovanni Mattiotti^1,2*, Raffaello Potestio^1,2*

1 Physics Department, University of Trento, via Sommarive, 14 I-38123 Trento, Italy

2 INFN-TIFPA, Trento Institute for Fundamental Physics and Applications, I-38123 Trento, Italy

3 School of Chemistry, University of Birmingham, B15 2TT Birmingham, UK

* Corresponding authors emails: raffaele.fiorentini@unitn.it, thomas.tarenzi@unitn.it, giovanni.mattiotti@unitn.it, raffaello.potestio@unitn.it

DOI10.24435/materialscloud:a7-r8 [version v1]

Publication date: Nov 13, 2023

How to cite this record

Raffaele Fiorentini, Thomas Tarenzi, Giovanni Mattiotti, Raffaello Potestio, The optimal resolution level of a protein is an emergent property of its structure and dynamics, Materials Cloud Archive 2023.172 (2023), https://doi.org/10.24435/materialscloud:a7-r8

Description

Molecular dynamics simulations provide a wealth of data whose in-depth analysis can be computationally demanding and, sometimes, even unnecessary. Dimensionality reduction techniques are thus routinely employed to simplify and improve the interpretation of trajectories focusing on specific subsets of the system's atoms; a key issue, in this context, is to determine the optimal resolution level, i.e. the smallest number of atoms needed to preserve the largest information content from the full atomistic trajectory. Here, we introduce the protein optimal resolution identification method (PROPRE), an unsupervised approach built on information theory principles that determines the smallest number of atoms that need to be retained to attain a synthetic yet informative description of a protein. By applying the method to a protein dataset and two particular case studies, we show that this number is typically between 1.5 and 2 times the number of residues in a protein; nonetheless, the degree of conformational variability of the system influences the specific number importantly, in that a broader range of large-scale conformations correlates with fewer retained sites. The PROPRE method is implemented in efficient and user-friendly python scripts, which are made available for download on a github repository. Here, the raw data employed for the preparation of the associated manuscript are made available.

Materials Cloud sections using this data

Files

File name	Size	Description
PROPRE_rawdata.zip MD5md5:37c96d3a255dee1e93693b91e214653e	151.4 MiB	Zip file containing input files and output files employed in the article.
README.rtf MD5md5:7ca43e209d15806d373aceec65a74707	2.9 KiB	Readme file describing the content of the compressed folder PROPRE_rawdata.zip

License

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External references

Keywords

Coarse-graining molecular dynamics Model resolution