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Updated: Jul 3, 2025

Demonstration of the Sequence Alignment to Predict Across Species Susceptibility Tool for Rapid Assessment of Protein Conservation

Demonstration of the Sequence Alignment to Predict Across Species Susceptibility Tool for Rapid Assessment of Protein Conservation

Published on: February 10, 2023

AlignScape, displaying sequence similarity using self-organizing maps.

Isaac Filella-Merce^1,2, Vincent Mallet², Eric Durand³

¹Life Sciences Department, Electronic and Atomic Protein Modeling Group (EAPM), Barcelona Supercomputing Center (BSC), Barcelona, Spain.

Frontiers in Bioinformatics

|February 12, 2024

Summary

This summary is machine-generated.

AlignScape is a new computational biology method that uses self-organizing maps to efficiently analyze and visualize complex protein sequence data. It aids in sequence classification, functional inference, and predicting coevolving partners.

Keywords:

human GPCRs human kinome protein sequence analysis protein sequence visualization self-organizing maps (SOM)sequence similarity landscape type VI secretion system (T6SS)

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Last Updated: Jul 3, 2025

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A Protocol for Computer-Based Protein Structure and Function Prediction

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Published on: November 3, 2011

Area of Science:

Computational Biology
Bioinformatics
Genomics

Background:

Analyzing large protein sequence datasets is challenging.
Traditional methods like phylogenetic trees and similarity networks have limitations.
Efficient visualization and analysis are crucial for understanding protein families.

Purpose of the Study:

To introduce AlignScape, a novel methodology for analyzing protein sequence data.
To provide efficient tools for sequence classification, functional inference, and identifying coevolving partners.
To demonstrate the application of AlignScape on large protein families.

Main Methods:

Development of AlignScape, a methodology based on self-organizing maps.
Application of AlignScape to human kinases, GPCRs, and bacterial T6SS proteins.
Utilizing GPU implementation for efficient analysis of large multiple sequence alignments (MSAs).

Main Results:

AlignScape generates a similarity landscape map and a tree representation of MSAs.
These representations facilitate sequence display, clustering, classification, and identification of functional trends.
The method efficiently analyzes large MSAs in minutes.
AlignScape analysis predicted coevolving partners within the T6SS complex.

Conclusions:

AlignScape offers an efficient and effective approach for analyzing complex protein sequence data.
The methodology aids in understanding protein family relationships and functions.
AlignScape has potential applications in predicting protein interactions and guiding further research.