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Updated: Aug 13, 2025

Mass Spectrometry-Guided Genome Mining as a Tool to Uncover Novel Natural Products

Mass Spectrometry-Guided Genome Mining as a Tool to Uncover Novel Natural Products

Published on: March 12, 2020

Convolutional networks for supervised mining of molecular patterns within cellular context.

Irene de Teresa-Trueba^1,2, Sara K Goetz^1,3, Alexander Mattausch^1,4

¹Structural and Computational Biology Unit, European Molecular Biology Laboratory, Heidelberg, Germany.

|January 23, 2023

Summary

This summary is machine-generated.

DeePiCt, a deep learning framework, accurately identifies macromolecular complexes in cryo-electron tomograms. This tool aids in understanding cellular structures and ribosome populations, even in challenging low-density scenarios.

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Area of Science:

Structural biology
Cell biology
Biophysics

Background:

Cryo-electron tomography (cryo-ET) provides high-resolution structural insights into cellular components.
Accurate segmentation and localization of macromolecular complexes are crucial for understanding cellular architecture and function.

Purpose of the Study:

To introduce DeePiCt, an open-source deep learning framework for supervised segmentation and macromolecular complex localization in cryo-ET.
To benchmark DeePiCt's performance against state-of-the-art methods using annotated experimental data.

Main Methods:

Development of DeePiCt, a deep learning framework utilizing supervised segmentation.
Comprehensive annotation of 20 cryo-ET tomograms from Schizosaccharomyces pombe.
Benchmarking DeePiCt against existing approaches on annotated datasets.

Main Results:

DeePiCt demonstrates superior performance in identifying low-abundance and low-density macromolecular complexes.
Analysis of cellular ribosome subpopulations and their association with mitochondria and endoplasmic reticulum.
High-quality predictions achieved on unseen datasets from different species within minutes.

Conclusions:

DeePiCt offers a powerful and efficient tool for macromolecular complex localization in cryo-ET.
The framework facilitates the study of cellular composition and organization.
Availability of annotated data and pre-trained networks accelerates research in structural cell biology.