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A Systematic Framework for Molecular Dynamics Simulations of Protein Post-Translational Modifications

Overview of attention for article published in PLoS Computational Biology, July 2013
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Title
A Systematic Framework for Molecular Dynamics Simulations of Protein Post-Translational Modifications
Published in
PLoS Computational Biology, July 2013
DOI 10.1371/journal.pcbi.1003154
Pubmed ID
Authors

Drazen Petrov, Christian Margreitter, Melanie Grandits, Chris Oostenbrink, Bojan Zagrovic

Abstract

By directly affecting structure, dynamics and interaction networks of their targets, post-translational modifications (PTMs) of proteins play a key role in different cellular processes ranging from enzymatic activation to regulation of signal transduction to cell-cycle control. Despite the great importance of understanding how PTMs affect proteins at the atomistic level, a systematic framework for treating post-translationally modified amino acids by molecular dynamics (MD) simulations, a premier high-resolution computational biology tool, has never been developed. Here, we report and validate force field parameters (GROMOS 45a3 and 54a7) required to run and analyze MD simulations of more than 250 different types of enzymatic and non-enzymatic PTMs. The newly developed GROMOS 54a7 parameters in particular exhibit near chemical accuracy in matching experimentally measured hydration free energies (RMSE=4.2 kJ/mol over the validation set). Using this tool, we quantitatively show that the majority of PTMs greatly alter the hydrophobicity and other physico-chemical properties of target amino acids, with the extent of change in many cases being comparable to the complete range spanned by native amino acids.

Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 143 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 2 1%
Norway 1 <1%
Korea, Republic of 1 <1%
Austria 1 <1%
Brazil 1 <1%
Finland 1 <1%
Italy 1 <1%
United Kingdom 1 <1%
India 1 <1%
Other 2 1%
Unknown 131 92%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 43 30%
Researcher 34 24%
Student > Master 16 11%
Student > Bachelor 13 9%
Professor 6 4%
Other 15 10%
Unknown 16 11%
Readers by discipline Count As %
Agricultural and Biological Sciences 37 26%
Biochemistry, Genetics and Molecular Biology 33 23%
Chemistry 18 13%
Physics and Astronomy 7 5%
Pharmacology, Toxicology and Pharmaceutical Science 6 4%
Other 22 15%
Unknown 20 14%