Browse
Submit Data
Databases
API
Help

Dataset Information

0 Views

0 Connections

0 Citations

0 Reanalyses

0 Downloads

Omics score: 0

Coarse-Grained Modeling Using Neural Networks Trained on Structural Data.

ABSTRACT: We propose a method of bottom-up coarse-graining, in which interactions within a coarse-grained model are determined by an artificial neural network trained on structural data obtained from multiple atomistic simulations. The method uses ideas of the inverse Monte Carlo approach, relating changes in the neural network weights with changes in average structural properties, such as radial distribution functions. As a proof of concept, we demonstrate the method on a system interacting by a Lennard-Jones potential modeled by a simple linear network and a single-site coarse-grained model of methanol-water solutions. In the latter case, we implement a nonlinear neural network with intermediate layers trained by atomistic simulations carried out at different methanol concentrations. We show that such a network acts as a transferable potential at the coarse-grained resolution for a wide range of methanol concentrations, including those not included in the training set.

SUBMITTER: Ivanov M

PROVIDER: S-EPMC10569054 | biostudies-literature | 2023 Oct

REPOSITORIES: biostudies-literature

ACCESS DATA

Json Xml

Publications

Coarse-Grained Modeling Using Neural Networks Trained on Structural Data.

Ivanov Mikhail M Posysoev Maksim M Lyubartsev Alexander P AP

Journal of chemical theory and computation 20230915 19

We propose a method of bottom-up coarse-graining, in which interactions within a coarse-grained model are determined by an artificial neural network trained on structural data obtained from multiple atomistic simulations. The method uses ideas of the inverse Monte Carlo approach, relating changes in the neural network weights with changes in average structural properties, such as radial distribution functions. As a proof of concept, we demonstrate the method on a system interacting by a Lennard- ...[more]

PMID: 37712507

Similar Datasets

Coarse-Grained Structural Modeling of Molecular Motors Using Multibody Dynamics.

Project description:Experimental and computational approaches are needed to uncover the mechanisms by which molecular motors convert chemical energy into mechanical work. In this article, we describe methods and software to generate structurally realistic models of molecular motor conformations compatible with experimental data from different sources. Coarse-grained models of molecular structures are constructed by combining groups of atoms into a system of rigid bodies connected by joints. Contacts between rigid bodies enforce excluded volume constraints, and spring potentials model system elasticity. This simplified representation allows the conformations of complex molecular motors to be simulated interactively, providing a tool for hypothesis building and quantitative comparisons between models and experiments. In an example calculation, we have used the software to construct atomically detailed models of the myosin V molecular motor bound to its actin track. The software is available at www.simtk.org.

| S-EPMC2860290 | biostudies-literature

Thermodynamic Transferability in Coarse-Grained Force Fields Using Graph Neural Networks.

Project description:Coarse-graining is a molecular modeling technique in which an atomistic system is represented in a simplified fashion that retains the most significant system features that contribute to a target output while removing the degrees of freedom that are less relevant. This reduction in model complexity allows coarse-grained molecular simulations to reach increased spatial and temporal scales compared with corresponding all-atom models. A core challenge in coarse-graining is to construct a force field that represents the interactions in the new representation in a way that preserves the atomistic-level properties. Many approaches to building coarse-grained force fields have limited transferability between different thermodynamic conditions as a result of averaging over internal fluctuations at a specific thermodynamic state point. Here, we use a graph-convolutional neural network architecture, the Hierarchically Interacting Particle Neural Network with Tensor Sensitivity (HIP-NN-TS), to develop a highly automated training pipeline for coarse-grained force fields, which allows for studying the transferability of coarse-grained models based on the force-matching approach. We show that this approach yields not only highly accurate force fields but also that these force fields are more transferable through a variety of thermodynamic conditions. These results illustrate the potential of machine learning techniques, such as graph neural networks, to improve the construction of transferable coarse-grained force fields.

| S-EPMC11635977 | biostudies-literature

Predicting optical spectra for optoelectronic polymers using coarse-grained models and recurrent neural networks.

Project description:Coarse-grained modeling of conjugated polymers has become an increasingly popular route to investigate the physics of organic optoelectronic materials. While ultraviolet (UV)-vis spectroscopy remains one of the key experimental methods for the interrogation of these materials, a rigorous bridge between simulated coarse-grained structures and spectroscopy has not been established. Here, we address this challenge by developing a method that can predict spectra of conjugated polymers directly from coarse-grained representations while avoiding repetitive procedures such as ad hoc back-mapping from coarse-grained to atomistic representations followed by spectral computation using quantum chemistry. Our approach is based on a generative deep-learning model: the long-short-term memory recurrent neural network (LSTM-RNN). The latter is suggested by the apparent similarity between natural languages and the mathematical structure of perturbative expansions of, in our case, excited-state energies perturbed by conformational fluctuations. We also use this model to explore the level of sensitivity of spectra to the coarse-grained representation back-mapping protocol. Our approach presents a tool uniquely suited for improving postsimulation analysis protocols, as well as, potentially, for including spectral data as input in the refinement of coarse-grained potentials.

| S-EPMC7322026 | biostudies-literature

Structural investigation of MscL gating using experimental data and coarse grained MD simulations.

Project description:The mechanosensitive channel of large conductance (MscL) has become a model system in which to understand mechanosensation, a process involved in osmoregulation and many other physiological functions. While a high resolution closed state structure is available, details of the open structure and the gating mechanism remain unknown. In this study we combine coarse grained simulations with restraints from EPR and FRET experiments to study the structural changes involved in gating with much greater level of conformational sampling than has previously been possible. We generated a set of plausible open pore structures that agree well with existing open pore structures and gating models. Most interestingly, we found that membrane thinning induces a kink in the upper part of TM1 that causes an outward motion of the periplasmic loop away from the pore centre. This previously unobserved structural change might present a new mechanism of tension sensing and might be related to a functional role in osmoregulation.

| S-EPMC3447979 | biostudies-literature

Probing the Structural Dynamics of the NMDA Receptor Activation by Coarse-Grained Modeling.

Project description:N-Methyl-D-aspartate (NMDA) receptors are glutamate-gated excitatory channels that play essential roles in brain functions. High-resolution structures have been solved for an allosterically inhibited and agonist-bound form of a functional NMDA receptor; however, other key functional states (particularly the active open-channel state) were only resolved at moderate resolutions by cryo-electron microscopy (cryo-EM). To decrypt the mechanism of the NMDA receptor activation, structural modeling is essential to provide presently missing information about structural dynamics. We performed systematic coarse-grained modeling using an elastic network model and related modeling/analysis tools (e.g., normal mode analysis, flexibility and hotspot analysis, cryo-EM flexible fitting, and transition pathway modeling) based on an active-state cryo-EM map. We observed extensive conformational changes that allosterically couple the extracellular regulatory and agonist-binding domains to the pore-forming trans-membrane domain (TMD), and validated these, to our knowledge, new observations against known mutational and functional studies. Our results predict two key modes of collective motions featuring shearing/twisting of the extracellular domains relative to the TMD, reveal subunit-specific flexibility profiles, and identify functional hotspot residues at key domain-domain interfaces. Finally, by examining the conformational transition pathway between the allosterically inhibited form and the active form, we predict a discrete sequence of domain motions, which propagate from the extracellular domains to the TMD. In summary, our results offer rich structural and dynamic information, which is consistent with the literature on structure-function relationships in NMDA receptors, and will guide in-depth studies on the activation dynamics of this important neurotransmitter receptor.

| S-EPMC5479051 | biostudies-literature

COARSE-GRAINED MODELING OF PROTEIN UNFOLDING DYNAMICS.

Project description:We present a new dynamic elastic network model (DENM) that describes the unfolding process of a force-loaded protein. The protein interaction network and its potentials are constructed based on information of its native-state structure obtained from the Protein Data Bank, with network nodes positioned at the Cα coordinates of the protein backbone. Specifically, to mimic the unfolding process, i.e., to simulate the process of overcoming the local energy barrier on the free energy landscape with force loading, the noncovalent protein network bonds (i.e., hydrogen bonds, salt bridges, hydrophobic contacts, etc.) are broken one-by-one with a certain probability, while the strong covalent bonds along the backbone (i.e., peptide bonds, disulfide bonds, etc.) are kept intact. The jumping event from local energy minima (bonds breaking rate) are chosen according to Kramer's theory and the Bell model. Moreover, we exploit the self-similar structure of proteins at different scales to design an effective coarse-graining procedure for DENM with optimal parameter selection. The robustness of DENM is validated by coarse-grained molecular dynamics (MD) simulation against atomistic MD simulation of force-extension processes of the Fibrinogen and Titin Immunoglobulin proteins. We observe that the native structure of the proteins determines the total unfolding dynamics (including large deviations) and not just the fluctuations around the native state.

| S-EPMC4230303 | biostudies-literature

Coarse-grained modeling of large RNA molecules with knowledge-based potentials and structural filters.

Project description:Understanding the function of complex RNA molecules depends critically on understanding their structure. However, creating three-dimensional (3D) structural models of RNA remains a significant challenge. We present a protocol (the nucleic acid simulation tool [NAST]) for RNA modeling that uses an RNA-specific knowledge-based potential in a coarse-grained molecular dynamics engine to generate plausible 3D structures. We demonstrate NAST's capabilities by using only secondary structure and tertiary contact predictions to generate, cluster, and rank structures. Representative structures in the best ranking clusters averaged 8.0 +/- 0.3 A and 16.3 +/- 1.0 A RMSD for the yeast phenylalanine tRNA and the P4-P6 domain of the Tetrahymena thermophila group I intron, respectively. The coarse-grained resolution allows us to model large molecules such as the 158-residue P4-P6 or the 388-residue T. thermophila group I intron. One advantage of NAST is the ability to rank clusters of structurally similar decoys based on their compatibility with experimental data. We successfully used ideal small-angle X-ray scattering data and both ideal and experimental solvent accessibility data to select the best cluster of structures for both tRNA and P4-P6. Finally, we used NAST to build in missing loops in the crystal structures of the Azoarcus and Twort ribozymes, and to incorporate crystallographic data into the Michel-Westhof model of the T. thermophila group I intron, creating an integrated model of the entire molecule. Our software package is freely available at https://simtk.org/home/nast.

| S-EPMC2648710 | biostudies-literature

Advances in coarse-grained modeling of macromolecular complexes.

Project description:Recent progress in coarse-grained (CG) molecular modeling and simulation has facilitated an influx of computational studies on biological macromolecules and their complexes. Given the large separation of length-scales and time-scales that dictate macromolecular biophysics, CG modeling and simulation are well-suited to bridge the microscopic and mesoscopic or macroscopic details observed from all-atom molecular simulations and experiments, respectively. In this review, we first summarize recent innovations in the development of CG models, which broadly include structure-based, knowledge-based, and dynamics-based approaches. We then discuss recent applications of different classes of CG models to explore various macromolecular complexes. Finally, we conclude with an outlook for the future in this ever-growing field of biomolecular modeling.

| S-EPMC6296860 | biostudies-literature

Graph neural network based coarse-grained mapping prediction.

Project description:The selection of coarse-grained (CG) mapping operators is a critical step for CG molecular dynamics (MD) simulation. It is still an open question about what is optimal for this choice and there is a need for theory. The current state-of-the art method is mapping operators manually selected by experts. In this work, we demonstrate an automated approach by viewing this problem as supervised learning where we seek to reproduce the mapping operators produced by experts. We present a graph neural network based CG mapping predictor called Deep Supervised Graph Partitioning Model (DSGPM) that treats mapping operators as a graph segmentation problem. DSGPM is trained on a novel dataset, Human-annotated Mappings (HAM), consisting of 1180 molecules with expert annotated mapping operators. HAM can be used to facilitate further research in this area. Our model uses a novel metric learning objective to produce high-quality atomic features that are used in spectral clustering. The results show that the DSGPM outperforms state-of-the-art methods in the field of graph segmentation. Finally, we find that predicted CG mapping operators indeed result in good CG MD models when used in simulation.

| S-EPMC8161155 | biostudies-literature

Investigating the structural dynamics of the PIEZO1 channel activation and inactivation by coarse-grained modeling.

Project description:The PIEZO channels, a family of mechanosensitive channels in vertebrates, feature a fast activation by mechanical stimuli (eg, membrane tension) followed by a slower inactivation. Although a medium-resolution structure of the trimeric form of PIEZO1 was solved by cryo-electron microscopy (cryo-EM), key structural changes responsible for the channel activation and inactivation are still unknown. Toward decrypting the structural mechanism of the PIEZO1 activation and inactivation, we performed systematic coarse-grained modeling using an elastic network model and related modeling/analysis tools (ie, normal mode analysis, flexibility and hotspot analysis, correlation analysis, and cryo-EM-based hybrid modeling and flexible fitting). We identified four key motional modes that may drive the tension-induced activation and inactivation, with fast and slow relaxation time, respectively. These modes allosterically couple the lateral and vertical motions of the peripheral domains to the opening and closing of the intra-cellular vestibule, enabling external mechanical forces to trigger, and regulate the activation/inactivation transitions. We also calculated domain-specific flexibility profiles, and predicted hotspot residues at key domain-domain interfaces and hinges. Our results offer unprecedented structural and dynamic information, which is consistent with the literature on mutational and functional studies of the PIEZO channels, and will guide future studies of this important family of mechanosensitive channels.

| S-EPMC5758363 | biostudies-literature

OmicsDI is part of the ELIXIR infrastructure

OmicsDI is an Elixir interoperability service. Learn more ›

Tweets

OmicsDI Databases

PRIDE
PeptideAtlas
MassIVE
JPOST Repository
Physiome Model Repository

EGA
EVA
ENA
LINCS
PAXDB
Cell Collective

MetaboLights
Metabolomics Workbench
MetabolomeExpress
GNPS
BioModels
FAIRDOMHub

ArrayExpress
dbGaP
ExpressionAtlas
GEO
NODE

Information

Databases
Help
API
Contact us
Code on GitHub
Terms of use
Submit Data