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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...

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Updated: Feb 23, 2026

An Integrated Approach for Microprotein Identification and Sequence Analysis

An Integrated Approach for Microprotein Identification and Sequence Analysis

Published on: July 12, 2022

The SubCons webserver: A user friendly web interface for state-of-the-art subcellular localization prediction.

M Salvatore^1,2, N Shu^1,2,3, A Elofsson^1,2

¹Science for Life Laboratory, Stockholm University, 171 21, Solna, Sweden.

Protein Science : a Publication of the Protein Society

|September 14, 2017

Summary

This summary is machine-generated.

SubCons is a new tool that predicts protein subcellular localization using a Random Forest classifier. It offers a user-friendly web interface and API for rapid, proteome-wide analysis of protein localization.

Keywords:

machine learning sequence analysis subcellular localization

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Last Updated: Feb 23, 2026

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Area of Science:

Bioinformatics
Computational Biology
Proteomics

Background:

Accurate prediction of protein subcellular localization is crucial for understanding protein function and cellular processes.
Existing methods may lack efficiency or user-friendliness for large-scale analyses.

Purpose of the Study:

To introduce SubCons, a web server and API for predicting protein subcellular localization.
To provide a user-friendly platform for both individual and proteome-wide localization predictions.

Main Methods:

SubCons integrates predictions from four independent localization predictors.
A Random Forest classifier is employed for final localization prediction.
A web interface and WSDL API are implemented for accessibility.

Main Results:

The SubCons server enables rapid prediction of subcellular localization for single proteins.
It supports batch processing of protein sets via file upload or API, facilitating proteome-wide studies.
Pre-calculated predictions for several eukaryotic organisms are available for download.

Conclusions:

SubCons provides an efficient and accessible tool for protein subcellular localization prediction.
Its capabilities are suitable for large-scale proteomic analyses.
Performance benchmarks against other tools like LocTree3 are presented.