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Nuclear Localization Signals and Import

Nuclear Localization Signals and Import

Proteins targeted to the nucleus carry short stretches of amino acid sequences called the nuclear localization signal or NLS. Classical nuclear localization signals are of two types: monopartite and bipartite NLS. Monopartite classical NLS (cNLS) consists of a single cluster of 4-8 amino acids. Bipartite cNLS consists of two clusters of 2-3 amino acids and a 9-12 residue long proline-rich linker bridging the two clusters. Signal clusters are rich in positively charged amino acids such as...

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Updated: Sep 25, 2025

Multi-color Localization Microscopy of Single Membrane Proteins in Organelles of Live Mammalian Cells

Multi-color Localization Microscopy of Single Membrane Proteins in Organelles of Live Mammalian Cells

Published on: June 30, 2018

DeepLoc 2.0: multi-label subcellular localization prediction using protein language models.

Vineet Thumuluri¹, José Juan Almagro Armenteros^2,3, Alexander Rosenberg Johansen^4,3

¹Indian Institute of Technology Madras, Chennai 600036, India.

Nucleic Acids Research

|April 30, 2022

Summary

This summary is machine-generated.

DeepLoc 2.0 enhances protein subcellular localization prediction using a novel protein language model. This update improves accuracy and interpretability, aiding proteomics research.

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Deep Proteome Profiling by Isobaric Labeling, Extensive Liquid Chromatography, Mass Spectrometry, and Software-assisted Quantification

Deep Proteome Profiling by Isobaric Labeling, Extensive Liquid Chromatography, Mass Spectrometry, and Software-assisted Quantification

Published on: November 15, 2017

Area of Science:

Proteomics
Bioinformatics
Computational Biology

Background:

Protein subcellular localization is crucial for understanding protein function and cellular processes.
Accurate prediction of protein localization aids in proteomics research and drug discovery.
Existing tools often lack multi-localization prediction capabilities and interpretability.

Purpose of the Study:

To introduce DeepLoc 2.0, an improved tool for predicting protein subcellular localization.
To enhance prediction performance and interpretability compared to previous versions.
To enable multi-localization prediction for eukaryotic and human proteins.

Main Methods:

Curated eukaryotic and human multi-location protein datasets with homology partitioning.
Utilized a pre-trained protein language model for sequence-based predictions.
Incorporated attention mechanisms for interpretability and prediction of sorting signals.

Main Results:

Achieved state-of-the-art performance in DeepLoc 2.0.
Demonstrated improved accuracy using sequence input over protein profiles.
Provided attention outputs correlating with sorting signal positions.
Successfully predicted nine types of protein sorting signals with high accuracy.

Conclusions:

DeepLoc 2.0 offers a significant advancement in predicting protein subcellular localization.
The tool provides enhanced performance, interpretability, and multi-localization capabilities.
The integration of protein language models and attention mechanisms represents a novel approach in the field.