scholarly journals Learning neural network potentials from experimental data via Differentiable Trajectory Reweighting

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Stephan Thaler ◽  
Julija Zavadlav
Keyword(s):  
Neural Network ◽  
Experimental Data ◽  
Coarse Graining ◽  
Md Simulations ◽  
Coarse Grained ◽  
Thermodynamic Perturbation Theory ◽  
Top Down ◽  
Bottom Up ◽  
Coarse Grained Model ◽  
Gradient Computation

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.

scholarly journals Coarse-graining molecular dynamics: stochastic models with non-Gaussian force distributions

2019 ◽  
Vol 80 (1-2) ◽  
pp. 457-479 ◽  
Author(s):  
Radek Erban
Keyword(s):  
Molecular Dynamics ◽  
Linear Models ◽  
Coarse Graining ◽  
Md Simulations ◽  
Coarse Grained ◽  
Spatial And Temporal Scales ◽  
Coarse Grained Model ◽  
Non Gaussian ◽  
Low Dimensional ◽  
Cellular Behaviour

Abstract Incorporating atomistic and molecular information into models of cellular behaviour is challenging because of a vast separation of spatial and temporal scales between processes happening at the atomic and cellular levels. Multiscale or multi-resolution methodologies address this difficulty by using molecular dynamics (MD) and coarse-grained models in different parts of the cell. Their applicability depends on the accuracy and properties of the coarse-grained model which approximates the detailed MD description. A family of stochastic coarse-grained (SCG) models, written as relatively low-dimensional systems of nonlinear stochastic differential equations, is presented. The nonlinear SCG model incorporates the non-Gaussian force distribution which is observed in MD simulations and which cannot be described by linear models. It is shown that the nonlinearities can be chosen in such a way that they do not complicate parametrization of the SCG description by detailed MD simulations. The solution of the SCG model is found in terms of gamma functions.

scholarly journals Assessing the transferability of common top-down and bottom-up coarse-grained molecular models for molecular mixtures

2019 ◽  
Vol 21 (4) ◽  
pp. 1912-1927 ◽  
Author(s):  
Thomas D. Potter ◽  
Jos Tasche ◽  
Mark R. Wilson
Keyword(s):  
Coarse Graining ◽  
Coarse Grained ◽  
Molecular Models ◽  
Top Down ◽  
Bottom Up ◽  
Molecular Mixtures

Assessing the performance of top-down and bottom-up coarse-graining approaches.

THE SIMULATION OF POLYSTYRENE/NANOPARTICLES COMPOSITE MICROSPHERES USING DISSIPATIVE PARTICLE DYNAMICS

2013 ◽  
Vol 12 (02) ◽  
pp. 1250111 ◽  
Author(s):  
HAILONG XU ◽  
QIUYU ZHANG ◽  
HEPENG ZHANG ◽  
BAOLIANG ZHANG ◽  
CHANGJIE YIN
Keyword(s):  
Dissipative Particle Dynamics ◽  
Sodium Dodecyl ◽  
Coarse Graining ◽  
Particle Dynamics ◽  
Coarse Grained ◽  
Polystyrene Nanoparticles ◽  
Coarse Grained Model ◽  
Composite Microspheres ◽  
Polystyrene Matrix ◽  
Materials Studio

Dissipative particle dynamics (DPD) was initially used to simulate the polystyrene/nanoparticle composite microspheres (PNCM) in this paper. The coarse graining model of PNCM was established. And the DPD parameterization of the model was represented in detail. The DPD repulsion parameters were calculated from the cohesive energy density which could be calculated by amorphous modules in Materials Studio. The equilibrium configuration of the simulated PNCM shows that the nanoparticles were actually "modified" with oleic acid and the modified nanoparticles were embedded in the bulk of polystyrene. As sodium dodecyl sulfate (SDS) was located in the interface between water and polystyrene, the hydrophilic head of SDS stretched into water while the hydrophobic tailed into polystyrene. All simulated phenomena were consistent with the experimental results in preparation of polystyrene/nanoparticles composite microspheres. The effect of surface modification of nanoparticles on its dispersion in polystyrene matrix was also studied by adjusting the interaction parameters between the OA and NP beads. The final results indicated that the nanoparticles removed from the core of composite microsphere to the surface with increase of a OA-NP . All the simulated results demonstrated that our coarse–grained model was reasonable.

scholarly journals Predictive Coding Models for Pain Perception

2019 ◽  
Author(s):  
Yuru Song ◽  
Mingchen Yao ◽  
Helen Kemprecos ◽  
Áine Byrne ◽  
Zhengdong Xiao ◽  
...  
Keyword(s):  
Experimental Data ◽  
Chronic Pain ◽  
Pain Perception ◽  
Predictive Coding ◽  
Oscillatory Activity ◽  
Top Down ◽  
Perceptual Inference ◽  
Bottom Up ◽  
Temporal Coordination ◽  
Mechanistic Insight

AbstractPain is a complex, multidimensional experience that involves dynamic interactions between sensory-discriminative and affective-emotional processes. Pain experiences have a high degree of variability depending on their context and prior anticipation. Viewing pain perception as a perceptual inference problem, we use a predictive coding paradigm to characterize both evoked and spontaneous pain. We record the local field potentials (LFPs) from the primary somatosensory cortex (S1) and the anterior cingulate cortex (ACC) of freely behaving rats—two regions known to encode the sensory-discriminative and affective-emotional aspects of pain, respectively. We further propose a framework of predictive coding to investigate the temporal coordination of oscillatory activity between the S1 and ACC. Specifically, we develop a high-level, empirical and phenomenological model to describe the macroscopic dynamics of bottom-up and top-down activity. Supported by recent experimental data, we also develop a mechanistic mean-field model to describe the mesoscopic population neuronal dynamics in the S1 and ACC populations, in both naive and chronic pain-treated animals. Our proposed predictive coding models not only replicate important experimental findings, but also provide new mechanistic insight into the uncertainty of expectation, placebo or nocebo effect, and chronic pain.Author SummaryPain perception in the mammalian brain is encoded through multiple brain circuits. The experience of pain is often associated with brain rhythms or neuronal oscillations at different frequencies. Understanding the temporal coordination of neural oscillatory activity from different brain regions is important for dissecting pain circuit mechanisms and revealing differences between distinct pain conditions. Predictive coding is a general computational framework to understand perceptual inference by integrating bottom-up sensory information and top-down expectation. Supported by experimental data, we propose a predictive coding framework for pain perception, and develop empirical and biologically-constrained computational models to characterize oscillatory dynamics of neuronal populations from two cortical circuits—one for the sensory-discriminative experience and the other for affective-emotional experience, and further characterize their temporal coordination under various pain conditions. Our computational study of biologically-constrained neuronal population model reveals important mechanistic insight on pain perception, placebo analgesia, and chronic pain.

scholarly journals Conformational and functional characterization of artificially conjugated non-canonical ubiquitin dimers

2019 ◽  
Vol 9 (1) ◽  
Author(s):  
Tobias Schneider ◽  
Andrej Berg ◽  
Zeynel Ulusoy ◽  
Martin Gamerdinger ◽  
Christine Peter ◽  
...  
Keyword(s):  
Experimental Data ◽  
Functional Characterization ◽  
Isotopic Labeling ◽  
Md Simulations ◽  
Conformational Space ◽  
Coarse Grained ◽  
Covalent Attachment ◽  
Binding Studies ◽  
Polyubiquitin Chains ◽  
Polypeptide Chains

AbstractUbiquitylation is an eminent posttranslational modification referring to the covalent attachment of single ubiquitin molecules or polyubiquitin chains to a target protein dictating the fate of such labeled polypeptide chains. Here, we have biochemically produced artificially Lys11-, and Lys27-, and Lys63-linked ubiquitin dimers based on click-chemistry generating milligram quantities in high purity. We show that the artificial linkage used for the conjugation of two ubiquitin moieties represents a fully reliable surrogate of the natural isopeptide bond by acquiring highly resolved nuclear magnetic resonance (NMR) spectroscopic data including ligand binding studies. Extensive coarse grained and atomistic molecular dynamics (MD) simulations allow to extract structures representing the ensemble of domain-domain conformations used to verify the experimental data. Advantageously, this methodology does not require individual isotopic labeling of both ubiquitin moieties as NMR data have been acquired on the isotopically labeled proximal moiety and complementary MD simulations have been used to fully interpret the experimental data in terms of domain-domain conformation. This combined approach intertwining NMR spectroscopy with MD simulations makes it possible to describe the conformational space non-canonically Lys11-, and Lys27-linked ubiquitin dimers occupy in a solution averaged ensemble by taking atomically resolved information representing all residues in ubiquitin dimers into account.

scholarly journals Structural Investigation of MscL Gating Using Experimental Data and Coarse Grained MD Simulations

2012 ◽  
Vol 8 (9) ◽  
pp. e1002683 ◽  
Author(s):  
Evelyne Deplazes ◽  
Martti Louhivuori ◽  
Dylan Jayatilaka ◽  
Siewert J. Marrink ◽  
Ben Corry
Keyword(s):  
Experimental Data ◽  
Structural Investigation ◽  
Md Simulations ◽  
Coarse Grained

scholarly journals Quantitative Prediction of pH-Controlled Ionization of Oligopeptides

2020 ◽  
Author(s):  
Raju Lunkad ◽  
Anastasiia Murmiliuk ◽  
Pascal Hebbeker ◽  
Milan Boublík ◽  
Zdeněk Tošner ◽  
...  
Keyword(s):  
Electrostatic Interactions ◽  
Weak Acid ◽  
Coarse Grained ◽  
Quantitative Prediction ◽  
Bottom Up ◽  
Coarse Grained Model ◽  
Flexible Polymer ◽  
Acid And Base ◽  
Base Side ◽  
Capillary Zone

Weak ampholytes are ubiquitous in nature and commonly found in artificial pH-responsive systems. However, our limited understanding of their ionisation response and the lack of predictive capabilities hinder the bottom-up design of such systems. Here, we used a coarse-grained model of a flexible polymer with weakly ionisable monomer units to quantitatively analyse the ionisation behaviour of two oligopeptides. Differences in ionisation response between oligopeptides and monomeric amino acids showed that electrostatic interactions between weak acid and base side chains play a key role in oligopeptide ionisation, as predicted by our model. Moreover, by comparing our simulations with experimental results from potentiometric titration, capillary zone electrophoresis and NMR, we demonstrated that our model reliably predicts the ionisation response and electrophoretic mobilities of various peptide sequences. Ultimately, our model is the first step towards using predictive bottom-up design of responsive ampholytes to tailor their properties as a function of charge and pH.<br>

scholarly journals The Bipolar Logic of Feedforward and Feedback Circuits

2021 ◽  
Author(s):  
Arturo Tozzi
Keyword(s):  
Visual Recognition ◽  
Coarse Grained ◽  
William Of Ockham ◽  
Top Down ◽  
Object Persistence ◽  
Bottom Up ◽  
Bipolar Mode ◽  
External Sources ◽  
Drug Resistant Epilepsy ◽  
Work Up

Instead of the conventional 0 and 1 values, bipolar reasoning uses -1, 0, +1 to describe double-sided judgements in which neutral elements are halfway between positive and negative evaluations (e.g., &ldquo;uncertain&rdquo; lies between &ldquo;impossible&rdquo; and &ldquo;totally sure&rdquo;). We discuss the state-of-the-art in bipolar logics and recall two medieval forerunners, i.e., William of Ockham and Nicholas of Autrecourt, who embodied a bipolar mode of thought that is eminently modern. Starting from the trivial observation that &ldquo;once a wheat sheaf is sealed and tied up, the packed down straws display the same orientation&rdquo;, we work up a new theory of the bipolar nature of networks, suggesting that orthodromic (i.e., feedforward, bottom-up) projections might be functionally coupled with antidromic (i.e., feedback, top-down) projections via the mathematical apparatus of presheaves/globular sets. When an entrained oscillation such as a neuronal spike propagates from A to B, changes in B might lead to changes in A, providing unexpected antidromic effects. Our account points towards the methodological feasibility of novel neural networks in which message feedback is guaranteed by backpropagation mechanisms endowed in the same feedforward circuits. Bottom-up/top-down transmission at various coarse-grained network levels provides fresh insights in far-flung scientific fields such as object persistence, memory reinforcement, visual recognition, Bayesian inferential circuits and multidimensional activity of the brain. Implying that axonal stimulation by external sources might backpropagate and modify neuronal electric oscillations, our theory also suggests testable previsions concerning the optimal location of transcranial magnetic stimulation&rsquo;s coils in patients affected by drug-resistant epilepsy.

Implicit-solvent coarse-grained modeling for polymer solutions via Mori-Zwanzig formalism

Soft Matter ◽  
2019 ◽  
Vol 15 (38) ◽  
pp. 7567-7582 ◽  
Author(s):  
Shu Wang ◽  
Zhen Li ◽  
Wenxiao Pan
Keyword(s):  
Polymer Solutions ◽  
Dynamic Properties ◽  
Coarse Graining ◽  
Coarse Grained ◽  
Implicit Solvent ◽  
Bottom Up ◽  
System P

We present a bottom-up coarse-graining (CG) method to establish implicit-solvent CG modeling for polymers in solution, which conserves the dynamic properties of the reference microscopic system.

Sign in / Sign up

Export Citation Format

Share Document