2020
DOI: 10.1021/acs.jcim.0c00722
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Experimental and Computational Druggability Exploration of the 14-3-3ζ/SOS1pS1161 PPI Interface

Abstract: The exploration of the druggability of certain protein–protein interactions (PPIs) still remains a challenging task in drug discovery. Here, we present a case study using the 14-3-3-PPI, showing how small molecules can be located that are able to modulate this key oncogenic pathway. A workflow embracing biophysical techniques and MD simulations was developed to evaluate the potential of a 14-3-3ζ PPI system to bind new tool compounds. The significance of the use of computational approaches to compensate for th… Show more

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Cited by 7 publications
(3 citation statements)
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“…The Amber18 program was used to run MD simulations [76]. In agreement with previous works [77][78][79], the following protocol was implemented herein to generate robust MD trajectories: (i) the solvent was energy minimized for 500 steps with the steepest descent algorithm (SD) followed by 2500 steps with the conjugate gradient algorithm (CG); (ii) the solvated solute was energy minimized for 1000 steps with the SD followed by 5000 steps with the CG; (iii) heating from 0 to 300 K was achieved by the Langevin thermostat at constant volume for 1 ns; iv) system density was equilibrated for 1 ns using the Berendsen barostat at constant pressure [80]; (v) the system was preliminarily relaxed for 50 ns at constant pressure; vi) final production of MD trajectories was carried out for 500 ns on each system at constant pressure without positional restraints. Time step was 2 fs in all MD simulations, which were run using the GPU version of pmemd.…”
Section: Molecular Dynamics Simulationsmentioning
confidence: 53%
“…The Amber18 program was used to run MD simulations [76]. In agreement with previous works [77][78][79], the following protocol was implemented herein to generate robust MD trajectories: (i) the solvent was energy minimized for 500 steps with the steepest descent algorithm (SD) followed by 2500 steps with the conjugate gradient algorithm (CG); (ii) the solvated solute was energy minimized for 1000 steps with the SD followed by 5000 steps with the CG; (iii) heating from 0 to 300 K was achieved by the Langevin thermostat at constant volume for 1 ns; iv) system density was equilibrated for 1 ns using the Berendsen barostat at constant pressure [80]; (v) the system was preliminarily relaxed for 50 ns at constant pressure; vi) final production of MD trajectories was carried out for 500 ns on each system at constant pressure without positional restraints. Time step was 2 fs in all MD simulations, which were run using the GPU version of pmemd.…”
Section: Molecular Dynamics Simulationsmentioning
confidence: 53%
“…An arbitrary charge of +1 was assigned to the catalytic Zn(II) ion [ 46 ], and it was bound to His94, His96, and His119 adopting force field parameters validated previously [ 63 , 64 , 65 ]. MD simulations were run according to the protocol described earlier, using a time-step of 2 fs [ 66 , 67 , 68 ]. Briefly, for each system, the solvent was first energy minimized for 500 steps using the steepest descent algorithm (SD), followed by 2500 steps with the conjugate gradient algorithm (CG) while keeping the solute frozen.…”
Section: Methodsmentioning
confidence: 99%
“…A Nuclear Magnetic Resonance (NMR) reference structure of Esc peptides was retrieved from the Protein Databank ( www.rcsb.org/pdb ) under the PDB-ID 5XDJ [ 77 ]. Parameters for d -amino acids were adapted from those implemented in Amber18 for l -amino acids, while parameters for the phosphoserine were retrieved from the AMBER parameter database (Bryce group) [ 78 ], as they have been recently tested and validated [ 79 ].…”
Section: Methodsmentioning
confidence: 99%