AIMC Topic: Macromolecular Substances

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Classification of helical polymers with deep-learning language models.

Journal of structural biology
Many macromolecules in biological systems exist in the form of helical polymers. However, the inherent polymorphism and heterogeneity of samples complicate the reconstruction of helical polymers from cryo-EM images. Currently, available 2D classifica...

CryoRes: Local Resolution Estimation of Cryo-EM Density Maps by Deep Learning.

Journal of molecular biology
Recent progress in cryo-EM research has ignited a revolution in biological macromolecule structure determination. Resolution is an essential parameter for quality assessment of a cryo-EM density map, and it is known that resolution varies in differen...

Smart de novo Macromolecular Structure Modeling from Cryo-EM Maps.

Journal of molecular biology
The study of macromolecular structures has expanded our understanding of the amazing cell machinery and such knowledge has changed how the pharmaceutical industry develops new vaccines in recent years. Traditionally, X-ray crystallography has been th...

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...