Structural alphabets approach performance of structural alignment in remote homology detection

MotivationRemote homology detection (RHD) is central to fold recognition and protein function annotation. While structural alignments provide a gold standard, they are computationally expensive. Encoding protein structures as sequences over structural alphabets offers a scalable alternative, but the relative performance of simple secondary-structure alphabets versus higher-resolution representations remains unclear. ResultsWe systematically compare 20-letter (3Di), 8-letter (Q8), and 3-letter (Q3) structural alphabets across three large-scale fold recognition benchmarks of increasing difficulty, using both advanced and basic sequence alignment algorithms. All three alphabets perform close to structural alignment gold standards and substantially outperform sequence-based methods. Remarkably, the minimal Q3 alphabet, distinguishing only helices, strands, and loops, achieves robust performance. We further demonstrate the practical utility of this finding in a protein function annotation task for a newly sequenced genome. Data AvailabilityBenchmark data are freely available at https://doi.org/10.6084/m9.figshare.c.8208161. Contactmichael.schroeder@tu-dresden.de Supplementary InformationSupplementary data are available online at the journal website.