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Updated: Jan 25, 2026

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

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

Published on: July 16, 2017

MAFFT-DASH: integrated protein sequence and structural alignment.

John Rozewicki^1,2, Songling Li^1,2, Karlou Mar Amada²

¹Department of Genome Informatics, Genome Information Research Center, Research Institute for Microbial Diseases, Osaka University, 3-1 Yamadaoka, Suita 565-0871, Japan.

Nucleic Acids Research

|May 8, 2019

Summary

This summary is machine-generated.

We developed MAFFT-DASH, integrating structural alignments with MAFFT multiple sequence alignment (MSA). This tool improves MSA accuracy for proteins with weak similarity, aiding in functional annotation and homology modeling.

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Area of Science:

Bioinformatics
Computational Biology
Structural Biology

Background:

Multiple sequence alignment (MSA) is crucial for understanding protein evolution and function.
Existing MSA tools often struggle with sequences exhibiting low similarity.
Structural information can enhance the accuracy of sequence alignments.

Purpose of the Study:

To integrate structural alignments into the MAFFT multiple sequence alignment tool.
To create a comprehensive database of protein structural alignments for querying.
To improve the accuracy of MSA for evolutionarily distant proteins.

Main Methods:

Development of the Database of Aligned Structural Homologs (DASH) containing structural alignments for all Protein Data Bank (PDB) proteins.
Integration of DASH with the MAFFT multiple sequence alignment (MSA) tool via a simple flag.
Benchmarking MAFFT-DASH against standard MAFFT using diverse datasets (BAliBase, HomFam, OXFam, Mattbench, SISYPHUS).

Main Results:

MAFFT-DASH demonstrated a 10-20% improvement in Sum-of-Pairs (SP) scores compared to standard MAFFT for MSAs with weak similarity.
Further accuracy gains were observed when MAFFT alignments were supplemented with homologous sequences from DASH.
The DASH database is accessible via a web interface and a REST-like API.

Conclusions:

MAFFT-DASH offers a significant improvement for multiple sequence alignment, particularly for challenging cases with low sequence identity.
The integrated approach enhances the reliability of protein sequence alignments.
DASH has potential applications in functional annotation, remote homology detection, and homology modeling template library construction.