Improving prediction accuracy in chimeric proteins with windowed multiple sequence alignment

A key step in protein structure prediction involves the detection of co-evolving pairs of residues, a signal for spatial proximity. This information is gleaned from multiple sequence alignment and underscores Alphafold's structure prediction for almost every known protein. A simple means to create proteins beyond those found in nature, is by unnaturally fusing together two known proteins or protein parts. Here we demonstrate that structured peptides are predicted with significantly reduced accuracy when added to the terminal ends of scaffold proteins. Appending the multiple sequence alignment for the individual peptide tags to that of the scaffold protein often restores prediction accuracy. This work suggests that this windowed multiple sequence alignment approach can be a useful tool for predicting the structure of fused, chimeric proteins.

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Publication Details

Journal
Computational and Structural Biotechnology Journal
Published
2025-01-01
DOI
https://doi.org/10.1016/j.csbj.2025.07.039
Citations
2
Primary Topic
Machine Learning in Bioinformatics
Type
article
Field-Weighted Citation Impact
1.61

Funders

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article

Improving prediction accuracy in chimeric proteins with windowed multiple sequence alignment

Alex Bronstein, Ailie Marx, Sanketh Vedula
2 citations
Computational and Structural Biotechnology Journal
Machine Learning in Bioinformatics
1.61
article

Improving prediction accuracy in chimeric proteins with windowed multiple sequence alignment

Alex Bronstein, Ailie Marx, Sanketh Vedula
article en
2 citations

Abstract

A key step in protein structure prediction involves the detection of co-evolving pairs of residues, a signal for spatial proximity. This information is gleaned from multiple sequence alignment and underscores Alphafold's structure prediction for almost every known protein. A simple means to create proteins beyond those found in nature, is by unnaturally fusing together two known proteins or protein parts. Here we demonstrate that structured peptides are predicted with significantly reduced accuracy when added to the terminal ends of scaffold proteins. Appending the multiple sequence alignment for the individual peptide tags to that of the scaffold protein often restores prediction accuracy. This work suggests that this windowed multiple sequence alignment approach can be a useful tool for predicting the structure of fused, chimeric proteins.

Computational and Structural Biotechnology JournalVol. 27
Institute of Science and Technology Austria (AT), Technion – Israel Institute of Technology (IL), Migal - Galilee Technology Center (IL), Tel Hai Academic College (IL)
Leona M. and Harry B. Helmsley Charitable Trust
Openalex Percentile: Top 16%
Machine Learning in Bioinformatics
1.61
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