scholarly journals Procedure to construct a multi-scale coarse-grained model of DNA-coated colloids from experimental data

Soft Matter ◽  
2013 ◽  
Vol 9 (30) ◽  
pp. 7342 ◽  
Author(s):  
Bianca M. Mladek ◽  
Julia Fornleitner ◽  
Francisco J. Martinez-Veracoechea ◽  
Alexandre Dawid ◽  
Daan Frenkel
2018 ◽  
Vol 498 (2) ◽  
pp. 296-304 ◽  
Author(s):  
Fabio Sterpone ◽  
Sébastien Doutreligne ◽  
Thanh Thuy Tran ◽  
Simone Melchionna ◽  
Marc Baaden ◽  
...  

2015 ◽  
Vol 17 (34) ◽  
pp. 22054-22063 ◽  
Author(s):  
Ananya Debnath ◽  
Sabine Wiegand ◽  
Harald Paulsen ◽  
Kurt Kremer ◽  
Christine Peter

A coarse-grained model is derived for chlorophyll molecules in lipid bilayers using a multi-scale simulation ansatz aiming to understand the association behavior of the light harvesting complex (LHCII) of green plants.


2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Stephan Thaler ◽  
Julija Zavadlav

AbstractIn molecular dynamics (MD), neural network (NN) potentials trained bottom-up on quantum mechanical data have seen tremendous success recently. Top-down approaches that learn NN potentials directly from experimental data have received less attention, typically facing numerical and computational challenges when backpropagating through MD simulations. We present the Differentiable Trajectory Reweighting (DiffTRe) method, which bypasses differentiation through the MD simulation for time-independent observables. Leveraging thermodynamic perturbation theory, we avoid exploding gradients and achieve around 2 orders of magnitude speed-up in gradient computation for top-down learning. We show effectiveness of DiffTRe in learning NN potentials for an atomistic model of diamond and a coarse-grained model of water based on diverse experimental observables including thermodynamic, structural and mechanical properties. Importantly, DiffTRe also generalizes bottom-up structural coarse-graining methods such as iterative Boltzmann inversion to arbitrary potentials. The presented method constitutes an important milestone towards enriching NN potentials with experimental data, particularly when accurate bottom-up data is unavailable.


2021 ◽  
Author(s):  
Yonathan Y Goldtzvik ◽  
D Thirumalai

Conventional kinesin, a motor protein that transports cargo within cells, walks by taking multiple steps towards the plus end of the microtubule (MT). While significant progress has been made in understanding the details of the walking mechanism of kinesin there are many unresolved issues. From a computational perspective, a central challenge is the large size of the system, which limits the scope of time scales accessible in standard computer simulations. Here, we create a general multi-scale coarse-grained model for motors that enables us to simulate the stepping process of motors on polar tracks (actin and MT) with focus on kinesin. Our approach greatly shortens the computation times without a significant loss in detail, thus allowing us to better describe the molecular basis of the stepping kinetics. The small number of parameters, which are determined by fitting to experimental data, allows us to develop an accurate method that may be adopted to simulate stepping in other molecular motors. The model enables us to simulate a large number of steps, which was not possible previously. We show in agreement with experiments that due to the docking of the neck linker (NL) of kinesin, sometimes deemed as the power stroke, the space explored diffusively by the tethered head is severely restricted allowing the step to be in a tens of microseconds. We predict that increasing the interaction strength between the NL and the motor head, achievable by mutations in the NL, decreases the stepping time but reaches a saturation value. Furthermore, the full 3-dimensional dynamics of the cargo are fully resolved in our model, contributing to the predictive power and allowing us to study the important aspects of cargo-motor interactions.


2017 ◽  
Vol 19 (48) ◽  
pp. 32421-32432 ◽  
Author(s):  
Xiaorong Liu ◽  
Jianhan Chen

Efficient coarse-grained (CG) models can be coupled with atomistic force fields to accelerate the sampling of atomistic energy landscapes in the multi-scale enhanced sampling (MSES) framework.


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