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Subcellular Fractionation

Subcellular Fractionation

The homogenate obtained after cell lysis contains various membrane-bound organelles that can be further separated into pure fractions by subcellular fractionation. These isolates are used to study specific cellular components, analyze localized protein activity, and are even employed in diagnostics. Fractionation is typically achieved using centrifugation methods, the most common being density-gradient and differential centrifugation.
Differential Centrifugation
Differential centrifugation is...

Conserved Binding Sites

Conserved Binding Sites

Many proteins’ biological role depends on their interactions with their ligands, small molecules that bind to specific locations on the protein known as ligand-binding sites. Ligand-binding sites are often conserved among homologous proteins as these sites are critical for protein function.
Binding sites are often located in large pockets, and if their location on a protein’s surface is unknown, it can be predicted using various approaches. The energetic method computationally analyses the...

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Related Experiment Video

Updated: Jun 24, 2026

Identification and Classification of Position-specific GABAA Receptor Subunit Missense Variants for Their Role In Hippocampal Pyramidal Neurons

Identification and Classification of Position-specific GABA_A Receptor Subunit Missense Variants for Their Role In Hippocampal Pyramidal Neurons

Published on: June 6, 2025

Supervised ensembles of prediction methods for subcellular localization.

Johannes Assfalg¹, Jing Gong, Hans-Peter Kriegel

¹Institute for Informatics, Ludwig-Maximilians-Universität München, Oettingenstrasse 67, 80538 Munich, Germany. assfalg@dbs.ifi.lmu.de

Journal of Bioinformatics and Computational Biology

|April 3, 2009

Summary

This summary is machine-generated.

This study introduces novel ensemble methods for predicting protein subcellular localization, significantly improving accuracy over existing automated approaches. These enhanced protein localization prediction tools offer better biological insights.

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Area of Science:

Bioinformatics
Computational Biology
Molecular Biology

Background:

Numerous automated methods for predicting protein subcellular localization have emerged.
These methods employ diverse principles and machine learning techniques.
Evaluating existing methods reveals their theoretical properties and limitations.

Purpose of the Study:

To develop improved automated prediction methods for protein subcellular localization.
To leverage experimental evaluations and theoretical properties of existing approaches.
To create novel ensemble-based prediction systems.

Main Methods:

Experimental evaluation of various protein localization prediction methods.
Analysis of theoretical properties of different prediction algorithms.
Development of ensemble methods by combining existing approaches.

Main Results:

Ensemble-based prediction methods were developed.
These ensembles demonstrated substantial performance improvements.
The proposed methods outperformed individual base methods in accuracy.

Conclusions:

Combining existing prediction methods into ensembles enhances accuracy.
Ensemble approaches offer a robust strategy for protein subcellular localization prediction.
The developed methods provide a valuable tool for biological research.