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GhostFold: Accurate protein structure prediction using structure-constrained synthetic coevolutionary signals.

Nitesh Mishra1,2, Bryan Briney1,2,3,4,5

  • 1Department of Immunology and Microbiology, The Scripps Research Institute, La Jolla, CA 92037 USA.

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|November 24, 2025
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Summary

GhostFold generates synthetic multiple sequence alignments (pseudoMSAs) from single sequences, enabling accurate protein structure prediction for proteins lacking homologs. This method bypasses traditional database searches, offering a computationally efficient solution.

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Area of Science:

  • Computational structural biology
  • Deep learning in protein folding
  • Bioinformatics

Background:

  • Protein structure prediction accuracy relies heavily on multiple sequence alignments (MSAs).
  • Proteins with few or no homologs present a significant challenge for accurate structure prediction.
  • Existing methods struggle with data scarcity for orphan proteins and hypervariable regions.

Purpose of the Study:

  • To develop a novel method, GhostFold, for generating structure-constrained synthetic MSAs (pseudoMSAs) from single amino acid sequences.
  • To overcome the limitations of traditional homology searches for protein structure prediction.
  • To enable accurate structure prediction for proteins with limited or no identifiable homologs.

Main Methods:

  • Leveraging the ProstT5 protein language model and the 3Di structural alphabet.
  • Projecting a query sequence into a tokenized structural representation.
  • Iterative back-translation to generate diverse, fold-consistent synthetic sequences (pseudoMSAs).

Main Results:

  • GhostFold successfully generates pseudoMSAs that enable high-accuracy structure prediction for challenging targets.
  • Performance matches or exceeds existing MSA-based and language model-based predictors.
  • The method is computationally lightweight and independent of large sequence databases.
  • A decoupling of confidence metrics (pLDDT) from prediction accuracy was observed with pseudoMSAs.

Conclusions:

  • Structure-guided synthetic MSAs can functionally replace evolutionary data in protein structure prediction.
  • GhostFold offers a scalable and generalizable solution to a central limitation in computational structural biology.
  • This work shifts towards intelligent sequence synthesis for encoding structural priors in deep learning models.