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Improving prediction accuracy in chimeric proteins with windowed multiple sequence alignment
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AI
DOI：10.1016/j.csbj.2025.07.039.png)
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Abstract
En 中文
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.
Keywords:
Protein structure prediction
AlphaFold
Multiple-sequence alignment
Journal
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4.1
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686
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