Pan-microalgal dark proteome mapping via interpretable deep learning and synthetic chimeras

Microalgal genomes contain a vast "dark proteome"-sequences lacking detectable homology that evade conventional classification tools. We developed LA⁴SR (language modeling with AI for algal amino acid sequence representation), a framework using transformer- and state-space models to classify translated ORFeomes across ten algal phyla. Training on ∼77 million sequences, LA⁴SR achieves near-complete recall, accelerates classification by ∼10,701× relative to BLASTP⁺, and generalizes robustly to unseen sequences using less than 2% of available data. Models trained on synthetic, chimeric (terminal information [TI]-free) sequences maintained high accuracy, demonstrating that internal sequence features alone can drive robust classification. Inference speed and scalability were further enhanced under TI-free settings, supporting rapid annotation of large proteomic datasets. Custom explainability tools revealed interpretable amino acid patterns linked to evolutionary and biophysical features. Designed for accessibility across disciplines, LA⁴SR integrates biological context and computational innovation in parallel, enabling both biologists and data scientists to interrogate the microbial dark proteome.