ESM3 is a multimodal protein language model that ingests a protein through several channels at once, including amino-acid sequence, three-dimensional structure, secondary structure (SS8), solvent accessibility (SASA), and discrete functional annotations, summing their embeddings into a single residual stream.
Abstract
Protein language models learn general-purpose representations from large collections of protein sequences and structures, and have advanced the prediction of protein structure and function. ESM3 is a multimodal protein language model that ingests a protein through several channels at once, including amino-acid sequence, three-dimensional structure, secondary structure (SS8), solvent accessibility (SASA), and discrete functional annotations, summing their embeddings into a single residual stream. Little is known about whether these modalities occupy separate subspaces and the depth at which they fuse. The present analysis examines ESM3 (esm3-sm-open-v1; 1.4 billion parameters; 48 transformer layers) once per modality in isolation and applies representational-similarity analysis across all 48 layers. The four physical modalities (sequence, structure, SS8, SASA) begin in distinct subspaces, remain maximally separated through roughly the first half of layers, and then fuse into a shared low-dimensional subspace between layers 25 and 35. The fusion is ordered. The structure-derived modalities (structure, SS8, SASA) are mutually aligned from the input, whereas sequence joins last, after layer 28. The functional-annotation modality never fuses; instead, it remains representationally orthogonal to the physical modalities at every layer, and this orthogonality holds whether the annotation is supplied as whole-protein or per-residue, suggesting that it is content-driven rather than a tokenization arti-fact. The fusion is a learned property, absent in a randomly initialized model of the same architecture, holds at the residue level below the mean-pool, and reorganizes variance, converting between-condition variance into within-condition variance while the stream never approaches isotropy. Fusion depth is independent of protein length but is delayed by structural disorder. The phenomenon is universal across diverse organisms. Across 5,555 proteins from 12 organisms spanning eukaryota, bacteria, and archaea, every superkingdom (and every individual organism) reaches peak modality fusion at the same network depth (layer 35).
Over eight diverse protein foundational models trained on 550,120 SwissProt proteins with AlphaFold structures, enriched embeddings improved zero-shot remote homology retrieval, increasing Precision@10 and MRR by up to 0.13 and 0.11, respectively.
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