DOI: 10.1093/bioinformatics/btag789 ISSN: 1367-4811

SSAlign: Fast and Sensitive Protein Structure Search at Scale

Lei Wang, Xuchao Zhang, Yan Wang, Zhidong Xue

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.

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