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Protein Families

Protein Families

Protein families are groups of homologous proteins; that is, they have similarities in amino acid sequences and three-dimensional structures. Protein families usually occur because of gene duplication, where an additional copy of a gene is inserted into the genome of an organism. Mutations that change the amino acids but still allow the protein to be properly synthesized, will lead to new protein family members. If these new proteins contain similar amino acids in key...

Protein Families

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Protein Networks

Protein Networks

An organism can have thousands of different proteins, and these proteins must cooperate to ensure the health of an organism. Proteins bind to other proteins and form complexes to carry out their functions. Many proteins interact with multiple other proteins creating a complex network of protein interactions.
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Gene Families

Gene Families

Gene families consist of groups of genes proposed to have originated from a common ancestor. Typically these arise through events in which a gene or genes are mistakenly duplicated during cell division. Unlike their parent genes (which are subject to selection pressure to maintain function), these gene copies do not need to preserve their sequences and may evolve at a relatively faster rate.
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CRISPR Gene Editing Tool for MicroRNA Cluster Network Analysis

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PFstats: A Network-Based Open Tool for Protein Family Analysis.

Néli J Fonseca-Júnior¹, Marcelo Q L Afonso¹, Lucas C Oliveira¹

¹Departamento de Bioquimica e Imunologia, Instituto de Ciências Biologicas (ICB), Universidade Federal de Minas Gerais (UFMG) , Belo Horizonte, Brazil .

Journal of Computational Biology : a Journal of Computational Molecular Cell Biology

|February 27, 2018

Summary

PFstats software analyzes protein alignments to find key residues and functional groups. It aids in understanding protein structure, function, and biological significance through advanced computational methods.

Keywords:

correlation analysis decomposition of residue correlation networks functional annotation protein families.

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Area of Science:

Bioinformatics
Computational Biology
Structural Biology

Background:

Protein multiple sequence alignments (MSAs) are crucial for understanding protein evolution and function.
Identifying structurally and functionally important residues within MSAs remains a challenge.
Existing tools may lack comprehensive analysis of conservation and coevolutionary networks.

Purpose of the Study:

To introduce PFstats, a novel software for extracting biological insights from protein MSAs.
To enable the identification of critical residue groups and potential functional subclasses.
To provide tools for assessing the biological significance of identified patterns.

Main Methods:

Utilizes positional conservation analysis to pinpoint conserved amino acid sites.
Employs residue coevolution network analysis to detect interacting residues.
Integrates automatic UniProtKB queries for residue annotation.
Incorporates alignment filtering and weighting for enhanced significance.

Main Results:

Successfully identifies groups of structurally and functionally important residues.
Discovers probable functional subclasses within protein families.
Facilitates the biological interpretation of conservation and coevolutionary data.
Offers diverse data visualization methods for intuitive exploration.

Conclusions:

PFstats provides a powerful, integrated approach for analyzing protein MSAs.
The software enhances the discovery of key residues and functional insights.
PFstats is a valuable tool for researchers in structural and functional genomics.