scholarly journals Data-Driven Reaction Coordinate Discovery in Overdamped and non-Conservative Systems: Application to Optical Matter Structural Isomerization

2020 ◽  
Author(s):  
Shiqi Chen ◽  
Curtis Peterson ◽  
John Parker ◽  
Stuart A. Rice ◽  
Andrew Ferguson ◽  
...  
Keyword(s):  
Normal Mode Analysis ◽  
Principal Component ◽  
Reaction Coordinate ◽  
Data Driven ◽  
Mode Analysis ◽  
Reference Structure ◽  
Structural Transitions ◽  
Linear Discriminant ◽  
Conservative Systems ◽  
Structural Isomerization

Abstract Powerful technique as it is to study the collective fluctuations and structural transitions in under-damped conservative systems, normal mode analysis cannot be applied to the optical matter (OM) system, an overdamped system subject to non-conservative forces, which consists of (nano-)particle constituents in solution that can self-organize into ordered arrays bound by electrodynamic interactions. We propose a data-driven approach based on principal component analysis (PCA) of deviations from a reference structure to determine the soft collective modes of non-conservative overdamped systems, such as OM structures, and harmonic linear discriminant analysis (HLDA) of time trajectories to estimate the reaction coordinate for structural transitions, which is demonstrated for structural isomerization of a six-particle OM system. The reaction coordinate we discover is in accord with committor analysis and the identified mechanism is in agreement with experiment. The PCA-HLDA approach to data-driven discovery of reaction coordinates aids in the understanding of non-conservative and overdamped systems.

scholarly journals Approximating deformation fields for the analysis of continuous heterogeneity of biological macromolecules by 3D Zernike polynomials

IUCrJ ◽  
2021 ◽  
Vol 8 (6) ◽  
Author(s):  
David Herreros ◽  
Roy R. Lederman ◽  
James Krieger ◽  
Amaya Jiménez-Moreno ◽  
Marta Martínez ◽  
...  
Keyword(s):  
Electron Microscopy ◽  
Structural Characteristics ◽  
Normal Mode Analysis ◽  
Principal Component ◽  
Current Data ◽  
Mode Analysis ◽  
Biological Macromolecules ◽  
Mathematical Basis ◽  
Common Framework ◽  
Low Dimensional

Structural biology has evolved greatly due to the advances introduced in fields like electron microscopy. This image-capturing technique, combined with improved algorithms and current data processing software, allows the recovery of different conformational states of a macromolecule, opening new possibilities for the study of its flexibility and dynamic events. However, the ensemble analysis of these different conformations, and in particular their placement into a common variable space in which the differences and similarities can be easily recognized, is not an easy matter. To simplify the analysis of continuous heterogeneity data, this work proposes a new automatic algorithm that relies on a mathematical basis defined over the sphere to estimate the deformation fields describing conformational transitions among different structures. Thanks to the approximation of these deformation fields, it is possible to describe the forces acting on the molecules due to the presence of different motions. It is also possible to represent and compare several structures in a low-dimensional mapping, which summarizes the structural characteristics of different states. All these analyses are integrated into a common framework, providing the user with the ability to combine them seamlessly. In addition, this new approach is a significant step forward compared with principal component analysis and normal mode analysis of cryo-electron microscopy maps, avoiding the need to select components or modes and producing localized analysis.

Protein functional landscapes, dynamics, allostery: a tortuous path towards a universal theoretical framework

2010 ◽  
Vol 43 (3) ◽  
pp. 295-332 ◽  
Author(s):  
Pavel I. Zhuravlev ◽  
Garegin A. Papoian
Keyword(s):  
Free Energy ◽  
Common Ground ◽  
Normal Mode Analysis ◽  
Energy Landscape ◽  
Principal Component ◽  
Dynamics Simulation ◽  
Mode Analysis ◽  
General Description ◽  
Topographical Map ◽  
Energy Surfaces

AbstractEnergy landscape theories have provided a common ground for understanding the protein folding problem, which once seemed to be overwhelmingly complicated. At the same time, the native state was found to be an ensemble of interconverting states with frustration playing a more important role compared to the folding problem. The landscape of the folded protein – the native landscape – is glassier than the folding landscape; hence, a general description analogous to the folding theories is difficult to achieve. On the other hand, the native basin phase volume is much smaller, allowing a protein to fully sample its native energy landscape on the biological timescales. Current computational resources may also be used to perform this sampling for smaller proteins, to build a ‘topographical map’ of the native landscape that can be used for subsequent analysis. Several major approaches to representing this topographical map are highlighted in this review, including the construction of kinetic networks, hierarchical trees and free energy surfaces with subsequent structural and kinetic analyses. In this review, we extensively discuss the important question of choosing proper collective coordinates characterizing functional motions. In many cases, the substates on the native energy landscape, which represent different functional states, can be used to obtain variables that are well suited for building free energy surfaces and analyzing the protein's functional dynamics. Normal mode analysis can provide such variables in cases where functional motions are dictated by the molecule's architecture. Principal component analysis is a more expensive way of inferring the essential variables from the protein's motions, one that requires a long molecular dynamics simulation. Finally, the two popular models for the allosteric switching mechanism, ‘preexisting equilibrium’ and ‘induced fit’, are interpreted within the energy landscape paradigm as extreme points of a continuum of transition mechanisms. Some experimental evidence illustrating each of these two models, as well as intermediate mechanisms, is presented and discussed.

A Data-Driven Fault Diagnosis Method for Railway Turnouts

2019 ◽  
Vol 2673 (4) ◽  
pp. 448-457 ◽  
Author(s):  
Dongxiu Ou ◽  
Rui Xue ◽  
Ke Cui
Keyword(s):  
Fault Diagnosis ◽  
Imbalanced Data ◽  
Principal Component ◽  
Real Data ◽  
Feature Reduction ◽  
Data Driven ◽  
Support Vector ◽  
Linear Discriminant ◽  
Railway Line ◽  
Diagnosis Method

Turnout systems on railways are crucial for safety protection and improvements in efficiency. The statistics show that the most common faults in railway system are turnout system faults. Therefore, many railway systems have adopted the microcomputer monitoring system (MMS) to monitor their health and performance in real time. However, in practice, existing turnout fault diagnosis methods depend largely on human experience. In this paper, we propose a data-driven fault diagnosis method that monitors data from point machines collected using MMS. First, based on a derivative method, data features are extracted by segmenting the original sample. Then, we apply two methods for feature reduction: principal component analysis (PCA) and linear discriminant analysis (LDA). The results show that LDA gave a better performance in the cases studied. A problem that cannot be overlooked is that the imbalanced quantity of rare fault samples and abundant normal samples will reduce the accuracy of classic fault diagnosis models. To deal with this problem of imbalanced data, we propose a modified support vector machine (SVM) method. Finally, an experiment using real data collected from the Guangzhou Railway Line is presented, which demonstrates that our method is reliable and feasible in fault diagnosis. It can further assist engineers to perform timely repairs and maintenance work in the future.

scholarly journals Coarse-Grained Protein Dynamics Studies Using Elastic Network Models

2018 ◽  
Vol 19 (12) ◽  
pp. 3899 ◽  
Author(s):  
Yuichi Togashi ◽  
Holger Flechsig
Keyword(s):  
Normal Mode Analysis ◽  
Protein Structures ◽  
Computational Cost ◽  
Network Models ◽  
Coarse Grained ◽  
Mode Analysis ◽  
Nonlinear Phenomena ◽  
Reference Structure ◽  
Elastic Network ◽  
Elastic Network Models

Elastic networks have been used as simple models of proteins to study their slow structural dynamics. They consist of point-like particles connected by linear Hookean springs and hence are convenient for linear normal mode analysis around a given reference structure. Furthermore, dynamic simulations using these models can provide new insights. As the computational cost associated with these models is considerably lower compared to that of all-atom models, they are also convenient for comparative studies between multiple protein structures. In this review, we introduce examples of coarse-grained molecular dynamics studies using elastic network models and their derivatives, focusing on the nonlinear phenomena, and discuss their applicability to large-scale macromolecular assemblies.

scholarly journals Collective Mode Mining from Molecular Dynamics Simulations: A Comparative Approach

2018 ◽  
Vol 15 (03) ◽  
pp. 1850108 ◽  
Author(s):  
Vito Dario Camiola ◽  
Valentina Tozzini
Keyword(s):  
Molecular Dynamics ◽  
Molecular Dynamics Simulations ◽  
Normal Mode Analysis ◽  
Principal Component ◽  
Mode Analysis ◽  
Comparative Approach ◽  
Atomic Systems ◽  
Equilibrium Dynamics ◽  
Definition Of ◽  
Dynamics Simulations

The evaluation of collective modes is fundamental in the analysis of molecular dynamics simulations. Several methods are available to extract that information, i.e., normal mode analysis, principal component and spectral analysis of trajectories, basically differing by the quantity considered as the nodal one (frequency, amplitude, or pattern of displacement) and leading to the definition of different kinds of collective excitations and physical spectral observables. The different views converge in the harmonic regime and/or for homo-atomic systems. However, for anharmonic and out of equilibrium dynamics, different quantities bring different information, and only their comparison can give a complete view of the system behavior. To allow such a comparative analysis, we review and compare the different approaches, applying them in diverse combinations to two examples of physical relevance: graphene and fullerene C[Formula: see text].

scholarly journals Analysis of changes of cavity volumes in predefined directions of protein motions and cavity flexibility

2021 ◽  
Author(s):  
German P. Barletta ◽  
Matias Barletta ◽  
Tadeo E. Saldaño ◽  
Sebastian Alberti
Keyword(s):  
Conformational Changes ◽  
Normal Mode Analysis ◽  
Growth Factor Receptor ◽  
Principal Component ◽  
Md Simulations ◽  
Mode Analysis ◽  
It Use ◽  
Nmr Structures ◽  
Structural Distortions ◽  
Protein Fluctuations

Dynamics of protein cavities associated with protein fluctuations and conformational plasticity is essential for their biological function. NMR ensembles, Molecular Dynamics (MD) simulations combined with Principal Component Analysis (PCA), and Normal Mode Analysis (NMA) provide appropriate frameworks to explore functionally relevant protein dynamics and cavity changes relationships. Within this context, we have developed ANA (Analysis of Null Areas), an efficient method to calculate cavity volumes. ANA is based on a combination of algorithms that guarantees its robustness against numerical differentiations. This is a unique feature with respect to other methods. Herein, we test ANA as a biophysical and bioinformatic method to analyze different structural and dynamics properties of cavities. In order to address this task, we have developed an updated and improved version of ANA that expands it use to quantify changes in cavity features, like volume and flexibility, due to protein structural distortions performed on predefined biologically relevant directions, e.g, directions of largest contribution to protein fluctuations (PCA modes) obtained by MD simulations or ensembles of NMR structures, collective NMA modes or any other direction of motion associated with specific conformational changes. A web page has been developed and its facilities are explained in detail, making the software available to all users. Firstly, we show that ANA can be useful to explore gradual changes of cavity volume and flexibility associated with protein ligand binding. Secondly, we perform a comparison study of the extent of variability between protein backbone structural distortions, and changes in cavity volumes and flexibilities evaluated for an ensemble of NMR active and inactive conformers of the epidermal growth factor receptor (EGFR) structures. Finally, we compare changes in size and flexibility between sets of NMR structures for different homologous chains of dynein.

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