AIMC Topic: Macromolecular Substances

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Deep learning to decompose macromolecules into independent Markovian domains.

Nature communications
The increasing interest in modeling the dynamics of ever larger proteins has revealed a fundamental problem with models that describe the molecular system as being in a global configuration state. This notion limits our ability to gather sufficient s...

New opportunities in integrative structural modeling.

Current opinion in structural biology
Integrative structural modeling enables structure determination of macromolecules and their complexes by integrating data from multiple sources. It has been successfully used to characterize macromolecular structures when a single structural biology ...

Volumetric macromolecule identification in cryo-electron tomograms using capsule networks.

BMC bioinformatics
BACKGROUND: Despite recent advances in cellular cryo-electron tomography (CET), developing automated tools for macromolecule identification in submolecular resolution remains challenging due to the lack of annotated data and high structural complexit...

Prot2Prot: a deep learning model for rapid, photorealistic macromolecular visualization.

Journal of computer-aided molecular design
Molecular visualization is a cornerstone of structural biology, providing insights into the form and function of biomolecules that are difficult to achieve any other way. Scientific analysis, publication, education, and outreach often benefit from ph...

Precise measurement of nanoscopic septin ring structures with deep learning-assisted quantitative superresolution microscopy.

Molecular biology of the cell
The combination of image analysis and superresolution microscopy methods allows for unprecedented insight into the organization of macromolecular assemblies in cells. Advances in deep learning (DL)-based object recognition enable the automated proces...

Accurate Sampling of Macromolecular Conformations Using Adaptive Deep Learning and Coarse-Grained Representation.

Journal of chemical information and modeling
Conformational sampling of protein structures is essential for understanding biochemical functions and for predicting thermodynamic properties such as free energies. Where previous approaches rely on sequential sampling procedures, recent development...

Observing Noncovalent Interactions in Experimental Electron Density for Macromolecular Systems: A Novel Perspective for Protein-Ligand Interaction Research.

Journal of chemical information and modeling
We report for the first time the use of experimental electron density (ED) in the Protein Data Bank for modeling of noncovalent interactions (NCIs) for protein-ligand complexes. Our methodology is based on reduced electron density gradient (RDG) theo...

Bayesian deep learning-based H-MRS of the brain: Metabolite quantification with uncertainty estimation using Monte Carlo dropout.

Magnetic resonance in medicine
PURPOSE: To develop a Bayesian convolutional neural network (BCNN) with Monte Carlo dropout sampling for metabolite quantification with simultaneous uncertainty estimation in deep learning-based proton MRS of the brain.

Macromolecules Structural Classification With a 3D Dilated Dense Network in Cryo-Electron Tomography.

IEEE/ACM transactions on computational biology and bioinformatics
Cryo-electron tomography, combined with subtomogram averaging (STA), can reveal three-dimensional (3D) macromolecule structures in the near-native state from cells and other biological samples. In STA, to get a high-resolution 3D view of macromolecul...