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Multiple protein structure alignment at scale with FoldMason.

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Title: Multiple protein structure alignment at scale with FoldMason.
Authors: Gilchrist, Cameron L. M. (AUTHOR), Mirdita, Milot (AUTHOR), Steinegger, Martin (AUTHOR)
Source: Science. 1/29/2026, Vol. 391 Issue 6784, p485-488. 4p.
Subjects: Protein structure, Phylogeny, Prediction models, Structural bioinformatics, Open source software
Abstract: Protein structure is conserved beyond sequence, making multiple structural alignment (MSTA) essential for analyzing distantly related proteins. Computational prediction methods have vastly extended our repository of available protein structures, requiring fast and accurate MSTA methods. We introduce FoldMason, a progressive MSTA method that leverages the pairwise structural aligners Foldseek and TM-align for the multiple alignment of hundreds of thousands of protein structures, matching or exceeding the alignment quality of state-of-the-art MSTA methods while being two orders of magnitude faster. Using Flaviviridae glycoproteins, we demonstrate that FoldMason's MSTAs support phylogenetic analysis beyond the "twilight zone." FoldMason computes confidence scores, offers interactive visualizations, and provides essential speed and accuracy for large-scale protein structure analysis in the era of accurate structure prediction. FoldMason is free, open-source software. Editor's summary: Protein structures can preserve information about evolutionary relationships that are no longer possible to detect by sequence alone. However, aligning by structure is computationally intensive, and with the advent of large-scale structure prediction, there is a need for tools that can efficiently align vast numbers of structures. Gilchrist et al. present FoldMason, a tool designed for this purpose that is based on parallelized progressive alignment. This approach permits large-scale phylogenetic analyses for protein families that have conserved structure but limited sequence conservation. —Michael A. Funk [ABSTRACT FROM AUTHOR]
Copyright of Science is the property of American Association for the Advancement of Science and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database: Psychology and Behavioral Sciences Collection
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  Data: Multiple protein structure alignment at scale with FoldMason.
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  Data: <searchLink fieldCode="JN" term="%22Science%22">Science</searchLink>. 1/29/2026, Vol. 391 Issue 6784, p485-488. 4p.
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  Data: <searchLink fieldCode="DE" term="%22Protein+structure%22">Protein structure</searchLink><br /><searchLink fieldCode="DE" term="%22Phylogeny%22">Phylogeny</searchLink><br /><searchLink fieldCode="DE" term="%22Prediction+models%22">Prediction models</searchLink><br /><searchLink fieldCode="DE" term="%22Structural+bioinformatics%22">Structural bioinformatics</searchLink><br /><searchLink fieldCode="DE" term="%22Open+source+software%22">Open source software</searchLink>
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  Label: Abstract
  Group: Ab
  Data: Protein structure is conserved beyond sequence, making multiple structural alignment (MSTA) essential for analyzing distantly related proteins. Computational prediction methods have vastly extended our repository of available protein structures, requiring fast and accurate MSTA methods. We introduce FoldMason, a progressive MSTA method that leverages the pairwise structural aligners Foldseek and TM-align for the multiple alignment of hundreds of thousands of protein structures, matching or exceeding the alignment quality of state-of-the-art MSTA methods while being two orders of magnitude faster. Using Flaviviridae glycoproteins, we demonstrate that FoldMason's MSTAs support phylogenetic analysis beyond the "twilight zone." FoldMason computes confidence scores, offers interactive visualizations, and provides essential speed and accuracy for large-scale protein structure analysis in the era of accurate structure prediction. FoldMason is free, open-source software. Editor's summary: Protein structures can preserve information about evolutionary relationships that are no longer possible to detect by sequence alone. However, aligning by structure is computationally intensive, and with the advent of large-scale structure prediction, there is a need for tools that can efficiently align vast numbers of structures. Gilchrist et al. present FoldMason, a tool designed for this purpose that is based on parallelized progressive alignment. This approach permits large-scale phylogenetic analyses for protein families that have conserved structure but limited sequence conservation. —Michael A. Funk [ABSTRACT FROM AUTHOR]
– Name: AbstractSuppliedCopyright
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  Data: <i>Copyright of Science is the property of American Association for the Advancement of Science and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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        Value: 10.1126/science.ads6733
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      – Code: eng
        Text: English
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        PageCount: 4
        StartPage: 485
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      – SubjectFull: Protein structure
        Type: general
      – SubjectFull: Phylogeny
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      – SubjectFull: Prediction models
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      – SubjectFull: Structural bioinformatics
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      – SubjectFull: Open source software
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      – TitleFull: Multiple protein structure alignment at scale with FoldMason.
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              M: 01
              Text: 1/29/2026
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              Y: 2026
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