A homogeneous dataset of polyglutamine and glutamine rich aggregating peptides simulations
Description
This dataset contains a collection of molecular dynamics (MD) simulations of polyglutamine (polyQ) and glutamine-rich (Q-rich) peptides in the multi microsecond timescale. Primary data from coarse-grained simulations performed using the SIRAH force field has been processed to provide fully atomistic coordinates. The dataset encloses MD trajectories of polyQs of 4 (Q4), 11 (Q11), and 36 (Q36) amino acids long. In the case of Q11, simulations in presence of Q5 and QEQQQ peptides, which modulate aggregation, are also included. The dataset also comprises MD trajectories of the gliadin related p31-43 peptide, and Insulin’s C-peptide at pH=7 and pH=3.2, which constitute examples of Q-rich and Q-poor aggregating peptides. The dataset grants molecular insights on the role of glutamines in the spontaneous and unbiased ab-initio aggregation of a series of peptides using a homogeneous set of simulations. The dataset is provided in Protein Data Bank (pdb) format. Further analyses of the trajectories can be performed directly using any molecular visualization/analysis software suites. If you use this data, please cite: *"Dissecting the role of glutamine in seeding peptide aggregation." Exequiel E.Barrera, Francesco Zonta, Sergio Pantano. *"A homogeneous dataset of polyglutamine and glutamine rich aggregating peptides simulations." Exequiel E.Barrera, Francesco Zonta, Sergio Pantano.
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Steps to reproduce
A detailed description of the protocol followed to generate the primary data is detailed in the associated paper. Briefly, for each system we started from fully atomistic peptide copies that were uniformly distributed in simulation boxes listed in Table 1. Systems were mapped to coarse-grain using SIRAH Tools, and solvated. In the simulations of the C-peptide at pH =7 and pH=3.2, KCl ions were added to a concentration of 150 mM. MD simulations were performed in the NPT ensemble at 300 K and 1 atm using the SIRAH force field version 2.0 using GROMACS 2018.4 as simulation engine