Leveraging evolutionary information is central to modern protein structure prediction. As protein structure is more conserved than sequence throughout evolution, searching for distant protein homologs using structural information is a highly appealing strategy. However, until recently, performing high-throughput searches for distant structural homologs with low sequence identity was not feasible without access to large databases of structures. The Embedded Alphabet (TEA), a novel 20-letter 1D structural alphabet, addresses this challenge by enabling fast and sensitive detection of distant homologs without requiring prior structural information. In this work, we investigate whether structure-aware multiple sequence alignments (MSAs) generated with TEA can improve protein structure prediction by enhancing the evolutionary signal captured in the alignments. Structure-aware MSAs were constructed using Search with TEA against Many (STEAM), a tool built on the Foldseek framework and adapted for the TEA alphabet. We evaluated these MSAs on two datasets: the CASP13 dataset, containing query proteins with shallow MSAs, and viral proteins from BFVD, previously characterized by poor-quality MSAs using the default ColabFold DB. To evaluate the impact on structure prediction, we ran AlphaFold2 (AF2) in custom MSA mode using both STEAM-generated structure-aware MSAs and MMseqs2-generated MSAs. We present an evaluation of STEAM-generated MSAs and their effect on AF2 prediction quality across both datasets, with a particular focus on challenging low-homology and viral targets, where sequence-based searches alone may be insufficient.

Co-authors: Celia Ulrich, Lorenzo Pantolini, Janani Durairaj