SSAlign: Fast and Sensitive Protein Structure Search at Scale

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ID: 334143
2026
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Abstract
MOTIVATION: Predicted protein structure resources now contain hundreds of millions of models, turning structural comparison from a pairwise alignment task into a database-scale retrieval problem. Exhaustive structural aligners are computationally impractical at this scale, whereas fast structural-alphabet searches may discard remote or low-complexity homologs during prefiltering. Database-scale search therefore requires methods that combine rapid retrieval with sensitivity to global structural similarity. RESULTS: We present SSAlign, a protein structure search method that integrates SaProt sequence-structure embeddings, entropy-reduced dense retrieval, an SS-score predictor and two-stage alignment refinement. In benchmarks on the Swiss-Prot, SCOPe40 and AFDB50 datasets, SSAlign showed competitive alignment quality and returned more TM-align-supported hits than Foldseek in recall-oriented evaluations. On AFDB50, 1,000 queries against 53.7 million structures completed in 3,150 s on CPUs and 2,290 s with GPU prefiltering, corresponding to 103-fold and 142-fold speedups over Foldseek, respectively. On SCOPe40, SSAlign increased the area under the curve (AUC) by 20.2% at family level and 33.3% at superfamily level. In simple-fold antimicrobial peptide case studies, SSAlign retrieved candidates that were not returned by Foldseek under the tested settings, indicating its utility as a fast candidate-generation tool for proteome-scale structural search. AVAILABILITY AND IMPLEMENTATION: The source code for SSAlign is publicly available at https://github.com/ISYSLAB-HUST/SSAlign. The software version and test data used in this study are archived on Zenodo at https://doi.org/10.5281/zenodo.23432919. Datasets and prebuilt indices can be downloaded from http://bioinfo.isyslab.info/ssalign/download/section/ssalign/. SUPPLEMENTARY INFORMATION: Supplementary data are available online.
Reference Key
openalex_W7223675166 Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Lei Wang, Xuchao Zhang, Yan Wang, Zhidong Xue
Journal BMC Bioinformatics
Year 2026
DOI
10.1093/bioinformatics/btag789
URL
https://doi.org/10.1093/bioinformatics/btag789
Keywords Keywords not found

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