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Protein Families

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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...

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Updated: Mar 1, 2026

Creating and Applying a Reference to Facilitate the Discussion and Classification of Proteins in a Diverse Group

Creating and Applying a Reference to Facilitate the Discussion and Classification of Proteins in a Diverse Group

Published on: August 16, 2017

PFASUM: a substitution matrix from Pfam structural alignments.

Frank Keul¹, Martin Hess², Michael Goesele³

¹Computational Biology and Simulation, Department of Biology, Technische Universität Darmstadt, Schnittspahnstraße 2, Darmstadt, 64287, Germany.

BMC Bioinformatics

|June 7, 2017

Summary

This summary is machine-generated.

New PFASUM substitution matrices improve protein homology detection and multiple sequence alignment (MSA) quality. Derived from expert data, PFASUM outperforms conventional matrices in 93% of homology search tests and enhances MSA construction.

Keywords:

Homologous sequence search PFASUM Sequence alignment Substitution matrix

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Area of Science:

Molecular Bioinformatics
Computational Biology
Evolutionary Biology

Background:

Homologous protein sequence detection and multiple sequence alignment (MSA) are critical bioinformatics tasks.
Traditional substitution matrices, often based on outdated data, may limit performance.
The rapid expansion of sequence data necessitates updated matrices for improved accuracy.

Purpose of the Study:

Introduce the novel PFASUM substitution matrix series.
Evaluate PFASUM's performance in homology searching and MSA construction.
Compare PFASUM against established matrices like BLOSUM and PAM.

Main Methods:

Developed PFASUM matrices using a novel algorithm on Pfam seed MSAs.
Tested homology search performance against ASTRAL databases.
Assessed MSA construction quality using up-to-date MSA benchmarks with the MUSCLE aligner.

Main Results:

PFASUM matrices significantly improved homology search results compared to conventional matrices.
PFASUM outperformed standard matrices in 93% of homology search tests.
PFASUM matrices enhanced MSA quality, with at least 60% of MSAs reconstructed at equal or higher quality.

Conclusions:

PFASUM matrices, derived from curated MSAs, represent an advancement in bioinformatics tools.
PFASUM offers improved homology detection and MSA quality over conventional matrices.
PFASUM, particularly PFASUM60, is recommended for homology search and MSA construction tasks.