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Conservation of Protein Domains Over Different Proteins

Conservation of Protein Domains Over Different Proteins

Protein domains are small structurally independent units that are part of a single amino acid chain. Although these domains are often structurally independent, they may rely on synergistic effects to perform their functions as part of a larger protein. Protein domains may be conserved within the same organism, as well as across different organisms.
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Related Experiment Video

Updated: Jun 25, 2025

A Protocol for Computer-Based Protein Structure and Function Prediction

A Protocol for Computer-Based Protein Structure and Function Prediction

Published on: November 3, 2011

AggreProt: a web server for predicting and engineering aggregation prone regions in proteins.

Joan Planas-Iglesias^1,2, Simeon Borko^1,2, Jan Swiatkowski³

¹Loschmidt Laboratories, Department of Experimental Biology and RECETOX, Faculty of Science, Masaryk University, Brno, Czech Republic.

Nucleic Acids Research

|May 27, 2024

Summary

This summary is machine-generated.

AggreProt is a new webserver that uses deep neural networks to predict aggregation-prone regions in protein sequences. This tool helps improve protein solubility and reduce aggregation for better protein-based technologies.

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Investigating Protein Sequence-structure-dynamics Relationships with Bio3D-web

Investigating Protein Sequence-structure-dynamics Relationships with Bio3D-web

Published on: July 16, 2017

Area of Science:

Biochemistry and structural biology
Computational biology and bioinformatics
Protein engineering and biotechnology

Background:

Recombinant proteins are crucial for industrial biocatalysis and therapeutics.
Designing proteins with high solubility and low aggregation propensity remains a significant challenge.
Identifying aggregation-prone regions (APRs) is vital for understanding protein misfolding and developing protein-based technologies.

Purpose of the Study:

To introduce AggreProt, a user-friendly webserver for predicting APRs in protein sequences using deep neural networks.
To provide a tool that aids in protein engineering for improved solubility and reduced aggregation.
To offer insights into protein aggregation behavior for both research and technological applications.

Main Methods:

Development of a webserver, AggreProt, utilizing an ensemble of deep neural networks.
Training the models on experimentally validated hexapeptides.
Evaluation of AggreProt's performance on two independent benchmark datasets against state-of-the-art algorithms.

Main Results:

AggreProt demonstrates comparable or superior performance to existing algorithms in predicting APRs.
The webserver provides per-residue aggregation profiles, solvent accessibility, and transmembrane propensity.
Interactive sequence and structure viewers facilitate comprehensive analysis within an intuitive interface.

Conclusions:

AggreProt effectively predicts differential aggregation behaviors in proteins.
The tool has the potential to guide protein engineering strategies for enhanced solubility and reduced aggregation.
AggreProt is freely accessible, supporting advancements in protein-based technologies and understanding of misfolding diseases.