This dataset provides the script used to generate combinatorial sequences of amino acids as well as the resulting list of sequences. Also, it provides the starting points to run MD simulations of two peptides and two metallopeptides systems extracted from the previous sequences. It includes all starting coordinates and parameters generated to simulate the metal coordination interactions.
Amber, 2020
Python, 3.0
METHODOLOGICAL INFORMATION
The standard Molecular Dynamics were carried out with Amber20, using the Amber14SB force field for the protein, and the force constants and equilibrium parameters derived for the Pd(COD)-2His complex with the Seminario method from QM calculations. Point charges were derived using RESP (Restrained Electrostatic Potential) model. Finally, force field building was performed with MCPB.py. The trajectories were set up with xleap (from AmberTools20), introducing each system in a cubic box of solvate TIP3P water molecules and Cl– ions (ions1lm_126_tip3p.lib) to equilibrate charges; distance from the biomolecule to the edge of the box is 25Å. First, the (metallo)peptide is submitted to 3000 steps of minimization, then 100 ps of equilibration at constant volume followed by 500 ps of equilibration at constant pressure and finally 500ns of production, all run with Amber20. These simulations were run in triplicates.
For the Gaussian accelerated Molecular Dynamics, simulations were started from the converged end points of the MD. 52ns of equilibration to acquire statistics and calculate potential boosts were run, followed by 500ns of production. Triplicates were simulated, each starting from one of the priviously mentioned cMD simulations.
- Description of methods used for collection-generation of data:
Marenich, A. V.; Cramer, C. J.; Truhlar, D. G. Universal Solvation Model Based on Solute Electron Density and on a Continuum Model of the Solvent Defined by the Bulk Dielectric Constant and Atomic Surface Tensions. J. Phys. Chem. B 2009, 113 (18), 6378–6396.
Case, D A.; Belfon, K.; Ben-Shalom, I. Y.; Brozell, S. R.; Cerutti, D. S.; Cheatham, T. E.; Cruzeiro, V. W. D.; Darden, T. A.; Duke, R. E.; Giambasu, G.; Gilson, M. K.; Gohlke, H.; Goetz, A. W.; Harris, R.; Izadi, S.; Izmailov, S. A.; Kasavajhala, K.; Kovalenko, A.; Krasny, R.; Kurtzman, T.; Lee, T. S.; LeGrand, S.; Li, P.; Lin, C.; Liu, J.; Luchko, T.; Luo, R.; Man, V.; Merz, K. M.; Miao, Y.; Mikhailovskii, O.; Monard, G.; Nguyen, H.; Onufriev, A.; Pan, F.; Pantano, S.; Qi, R.; Roe, D. R.; Roitberg, A.; Sagui, C.; SchottVerdugo, S.; Shen, J.; Simmerling, C. L.; Skrynnikov, N. R.; Smith, J.; J. Swails; Walker, R. C.; Wang, J.; Wilson, L.; Wolf, R. M.; Wu, X.; Xiong, Y.; Xue, Y.; York, D. M.; Kollman}, P. A. AMBER 2020; Univeristy of California, San Francisco, 2020.
Hornak, V.; Abel, R.; Okur, A.; Strockbine, B.; Roitberg, A.; Simmerling, C. Comparison of Multiple Amber Force Fields and Development of Improved Protein Backbone Parameters. Proteins: Struct. Funct. Bioinf. 2006, 65 (3), 712–725.
Bayly, C. I.; Cieplak, P.; Cornell, W.; Kollman, P. A. A Well-Behaved Electrostatic Potential Based Method Using Charge Restraints for Deriving Atomic Charges: The RESP Model. J. Phys. Chem. 1993, 97 (40), 10269–10280.
Li, P.; Merz, K. M. MCPB.Py: A Python Based Metal Center Parameter Builder. J. Chem. Inf. Model. 2016, 56 (4), 599–604.
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Methods for processing the data:
N/A
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Instrument- or software- specific information needed to interpret the data:
Amber20 and AmberTools20. Not open source but non-commercial license available.
Python 3.0
; Gonzalez Gonzalez, Carmen