scholarly journals Structure and Dynamics of Supercooled Liquid Ge 2 Sb 2 Te 5 from Machine‐Learning‐Driven Simulations

2020 ◽  
pp. 2000403
Author(s):  
Yu-Xing Zhou ◽  
Han-Yi Zhang ◽  
Volker L. Deringer ◽  
Wei Zhang
Keyword(s):  
Machine Learning ◽  
Supercooled Liquid ◽  
Structure And Dynamics

scholarly journals Disconnecting structure and dynamics in glassy thin films

2017 ◽  
Vol 114 (40) ◽  
pp. 10601-10605 ◽  
Author(s):  
Daniel M. Sussman ◽  
Samuel S. Schoenholz ◽  
Ekin D. Cubuk ◽  
Andrea J. Liu
Keyword(s):  
Thin Film ◽  
Machine Learning ◽  
Thin Films ◽  
Predictive Models ◽  
Local Structure ◽  
The Other ◽  
Microscopic Structure ◽  
Glassy Dynamics ◽  
Structure And Dynamics ◽  
Machine Learning Methods

Nanometrically thin glassy films depart strikingly from the behavior of their bulk counterparts. We investigate whether the dynamical differences between a bulk and thin film polymeric glass former can be understood by differences in local microscopic structure. Machine learning methods have shown that local structure can serve as the foundation for successful, predictive models of particle rearrangement dynamics in bulk systems. By contrast, in thin glassy films, we find that particles at the center of the film and those near the surface are structurally indistinguishable despite exhibiting very different dynamics. Next, we show that structure-independent processes, already present in bulk systems and demonstrably different from simple facilitated dynamics, are crucial for understanding glassy dynamics in thin films. Our analysis suggests a picture of glassy dynamics in which two dynamical processes coexist, with relative strengths that depend on the distance from an interface. One of these processes depends on local structure and is unchanged throughout most of the film, while the other is purely Arrhenius, does not depend on local structure, and is strongly enhanced near the free surface of a film.

scholarly journals Machine Learning for Prioritization of Thermostabilizing Mutations for G-protein Coupled Receptors

2019 ◽  
Author(s):  
S. Muk ◽  
S. Ghosh ◽  
S. Achuthan ◽  
X. Chen ◽  
X. Yao ◽  
...  
Keyword(s):  
Machine Learning ◽  
Membrane Proteins ◽  
G Protein ◽  
Three Dimensional ◽  
G Protein Coupled Receptors ◽  
Complement Factor ◽  
Computational Framework ◽  
Alanine Scanning ◽  
Structure And Dynamics ◽  
G Protein Coupled

AbstractAlthough the three-dimensional structures of G-protein-coupled receptors (GPCRs), the largest superfamily of drug targets, have enabled structure-based drug design, there are no structures available for 87% of GPCRs. This is due to the stiff challenge in purifying the inherently flexible GPCRs. Identifying thermostabilized mutant GPCRs via systematic alanine scanning mutations has been a successful strategy in stabilizing GPCRs, but it remains a daunting task for each GPCR. We developed a computational method that combines sequence, structure and dynamics based molecular properties of GPCRs that recapitulate GPCR stability, with four different machine learning methods to predict thermostable mutations ahead of experiments. This method has been trained on thermostability data for 1231 mutants, the largest publicly available dataset. A blind prediction for thermostable mutations of the Complement factor C5a Receptor retrieved 36% of the thermostable mutants in the top 50 prioritized mutants compared to 3% in the first 50 attempts using systematic alanine scanning.Statement Of SignifiganceG-protein-coupled receptors (GPCRs), the largest superfamily of membrane proteins play a vital role in cellular physiology and are targets to blockbuster drugs. Hence it is imperative to solve the three dimensional structures of GPCRs in various conformational states with different types of ligands bound. To reduce the experimental burden in identifying thermostable GPCR mutants, we report a computational framework using machine learning algorithms trained on thermostability data for 1231 mutants and features calculated from analysis of GPCR sequences, structure and dynamics to predict thermostable mutations ahead of experiments. This work represents a significant advancement in the development, validation and testing of a computational framework that can be extended to other class A GPCRs and helical membrane proteins.

scholarly journals Variation along liquid isomorphs of the driving force for crystallization

2017 ◽  
Vol 2 (3) ◽  
Author(s):  
Ulf Pedersen ◽  
Karolina Adrjanowicz ◽  
Kristine Niss ◽  
Nicholas Bailey
Keyword(s):  
Experimental Data ◽  
Relaxation Time ◽  
Driving Force ◽  
Supercooled Liquid ◽  
Scaling Exponent ◽  
Dimethyl Phthalate ◽  
Molecular Liquids ◽  
Lennard Jones ◽  
Structure And Dynamics ◽  
Increasing Function

We investigate the variation of the driving force for crystallization of a supercooled liquid along isomorphs, curves along which structure and dynamics are invariant. The variation is weak, and can be predicted accurately for the Lennard-Jones fluid using a recently developed formalism and data at a reference temperature. More general analysis allows interpretation of experimental data for molecular liquids such as dimethyl phthalate and indomethacin, and suggests that the isomorph scaling exponent \gammaγ in these cases is an increasing function of density, although this cannot be seen in measurements of viscosity or relaxation time.

Mind wandering as data augmentation: How mental travel supports abstraction

2020 ◽  
Vol 43 ◽  
Author(s):  
Myrthe Faber
Keyword(s):  
Machine Learning ◽  
Data Augmentation ◽  
Mental Content ◽  
Mind Wandering ◽  
Theoretical Framework ◽  
Important Addition

Abstract Gilead et al. state that abstraction supports mental travel, and that mental travel critically relies on abstraction. I propose an important addition to this theoretical framework, namely that mental travel might also support abstraction. Specifically, I argue that spontaneous mental travel (mind wandering), much like data augmentation in machine learning, provides variability in mental content and context necessary for abstraction.

Nucleosome dynamics

2006 ◽  
Vol 73 ◽  
pp. 109-119 ◽  
Author(s):  
Chris Stockdale ◽  
Michael Bruno ◽  
Helder Ferreira ◽  
Elisa Garcia-Wilson ◽  
Nicola Wiechens ◽  
...  
Keyword(s):  
High Resolution ◽  
Chromatin Structure ◽  
Current Knowledge ◽  
Dynamic Properties ◽  
Structure And Dynamics ◽  
Nucleosome Dynamics ◽  
Sharp Focus ◽  
Recent Advances ◽  
Insight Into ◽  
Chromatin Structure And Dynamics

In the 30 years since the discovery of the nucleosome, our picture of it has come into sharp focus. The recent high-resolution structures have provided a wealth of insight into the function of the nucleosome, but they are inherently static. Our current knowledge of how nucleosomes can be reconfigured dynamically is at a much earlier stage. Here, recent advances in the understanding of chromatin structure and dynamics are highlighted. The ways in which different modes of nucleosome reconfiguration are likely to influence each other are discussed, and some of the factors likely to regulate the dynamic properties of nucleosomes are considered.

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