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Protein Organization

Protein Organization

Proteins are polymers of amino acid residues. They are versatile and responsible for different cellular functions, including DNA replication, molecular transport, catalysis, and structural support. Proteins have a hierarchical structure comprising at least three levels of organization: primary, secondary, and tertiary structure. Some large proteins have a quaternary structure where individual protein subunits are linked together.
The primary structure of a protein is its amino acid sequence....

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Updated: Jun 28, 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

Large-scale structure-informed multiple sequence alignment of proteins with SIMSApiper.

Charlotte Crauwels^1,2,3, Sophie-Luise Heidig^1,3,4, Adrián Díaz^1,2,3

¹Interuniversity Institute of Bioinformatics in Brussels, ULB-VUB, Brussels, 1050, Belgium.

Bioinformatics (Oxford, England)

|April 22, 2024

Summary

This summary is machine-generated.

SIMSApiper is a novel Nextflow pipeline for creating reliable, structure-informed multiple sequence alignments (MSAs) of thousands of protein sequences. It significantly speeds up alignment by using structural information and parallelization, reducing gaps with conserved secondary structures.

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Last Updated: Jun 28, 2025

A Protocol for Computer-Based Protein Structure and Function Prediction

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Published on: November 3, 2011

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Published on: July 16, 2017

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Published on: February 11, 2022

Area of Science:

Bioinformatics
Computational Biology
Structural Biology

Background:

Multiple sequence alignment (MSA) is crucial for understanding protein function and evolution.
Existing structure-based alignment methods can be computationally intensive and slow for large datasets.
Integrating structural information can improve MSA accuracy and reliability.

Purpose of the Study:

To develop a fast and reliable pipeline for structure-informed multiple sequence alignment.
To enable the alignment of thousands of protein sequences efficiently.
To reduce the number of gaps in MSAs by leveraging structural data.

Main Methods:

Developed SIMSApiper, a Nextflow pipeline utilizing Python3 and Bash.
Incorporated user-provided or automatically retrieved structural information.
Implemented parallelization strategies based on sequence identity subsets.
Utilized conserved secondary structure elements to minimize gaps.

Main Results:

SIMSApiper generates reliable, structure-informed MSAs.
The pipeline significantly outperforms standard structure-based alignment methods in speed.
Achieved substantial speed-up through parallelization techniques.
Reduced the number of gaps in alignments by effectively using secondary structure information.

Conclusions:

SIMSApiper offers a highly efficient and accurate solution for large-scale protein sequence alignment.
The pipeline's ability to integrate structural data enhances MSA quality.
Its speed and reliability make it a valuable tool for bioinformatics research.