Charging and supercharging of proteins for mass spectrometry: recent insights into the mechanisms of electrospray ionization

The Analyst ◽  
2019 ◽  
Vol 144 (21) ◽  
pp. 6157-6171 ◽  
Author(s):  
Lars Konermann ◽  
Haidy Metwally ◽  
Quentin Duez ◽  
Insa Peters
Keyword(s):  
Mass Spectrometry ◽  
Molecular Dynamics ◽  
Molecular Dynamics Simulations ◽  
Electrospray Ionization ◽  
Experimental Conditions ◽  
Dynamics Simulations

Molecular dynamics simulations have uncovered mechanistic details of the protein ESI process under various experimental conditions.

scholarly journals Study of the thermal decomposition mechanism of FOX-7 by molecular dynamics simulation and online photoionization mass spectrometry

RSC Advances ◽  
2020 ◽  
Vol 10 (36) ◽  
pp. 21147-21157
Author(s):  
Liping Jiang ◽  
Xiaolong Fu ◽  
Zhongyue Zhou ◽  
Chongmin Zhang ◽  
Jizhen Li ◽  
...  
Keyword(s):  
Mass Spectrometry ◽  
Molecular Dynamics ◽  
Thermal Decomposition ◽  
Molecular Dynamics Simulation ◽  
Molecular Dynamics Simulations ◽  
Decomposition Mechanism ◽  
Dynamics Simulation ◽  
Thermal Decomposition Mechanism ◽  
Photoionization Mass Spectrometry ◽  
Dynamics Simulations

In this work, the primary thermal decomposition mechanism of 1,1-diamino-2,2-dinitroethylene (FOX-7) was studied by ReaxFF molecular dynamics simulations and online photoionization mass spectrometry.

scholarly journals Machine Learning for Accurate Force Calculations in Molecular Dynamics Simulations

2020 ◽  
Author(s):  
Punyaslok Pattnaik ◽  
Shampa Raghunathan ◽  
Tarun Kalluri ◽  
Prabhakar Bhimalapuram ◽  
C. V. Jawahar ◽  
...  
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Molecular Dynamics ◽  
Molecular Dynamics Simulations ◽  
Time Scales ◽  
Density Functional ◽  
Ab Initio Molecular Dynamics ◽  
Experimental Conditions ◽  
The Neural Network ◽  
Dynamics Simulations

<p>The computationally expensive nature of ab initio molecular dynamics simulations severely limits its ability to simulate large system sizes and long time scales, both of which are necessary to imitate experimental conditions. In this work, we explore an approach to make use of the data obtained using the quantum mechanical density functional theory (DFT) on small systems and use deep learning to subsequently simulate large systems by taking liquid argon as a test case. A suitable vector representation was chosen to represent the surrounding environment of each Ar atom, and a DNetFF machine learning model where, the neural network was trained to predict the difference in resultant forces obtained by DFT and classical force fields was introduced. Molecular dynamics simulations were then performed using forces from the neural network for various system sizes and time scales depending on the properties we calculated. A comparison of properties obtained from the classical force field and the neural network model was presented alongside available experimental data to validate the proposed method.</p>

Molecular Dynamics Simulations Provide Atomistic Insight into Hydrogen Exchange Mass Spectrometry Experiments

2012 ◽  
Vol 9 (1) ◽  
pp. 658-669 ◽  
Author(s):  
Ariel A. Petruk ◽  
Lucas A. Defelipe ◽  
Ramiro G. Rodríguez Limardo ◽  
Hernán Bucci ◽  
Marcelo A. Marti ◽  
...  
Keyword(s):  
Mass Spectrometry ◽  
Molecular Dynamics ◽  
Molecular Dynamics Simulations ◽  
Hydrogen Exchange ◽  
Exchange Mass Spectrometry ◽  
Hydrogen Exchange Mass Spectrometry ◽  
Dynamics Simulations ◽  
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