ProtAlign: Contrastive learning paradigm for Sequence and structure alignment

Protein language models produce rich per-residue representations, but the sequence embeddings obtained by mean-pooling them are never trained to place related proteins near one another, so the geometry that retrieval, clustering and nearest-neighbor transfer depend on is never optimized. We present ProtSent, a contrastive post-training framework that supervises a protein language model with three types of biological relation at once, Pfam family co-membership, AlphaFold DB structural clusters and STRING interactions, and with deep mutational scanning fitness ordering as an optional fourth. It needs no structure at training or inference, trains on corpora filtered at 40% sequence identity against the benchmark test sets they most resemble, and is applied to ESM-2 (35M and 150M parameters) and ESM-C (300M). Post-training makes structural family membership recoverable from the frozen embedding without a trained readout: on SCOPe-40, the adjusted Rand index of the 35M model rises from 0.054 (near chance) to 0.507, and family retrieval gains +0.185, +0.190 and +0.424 Recall@1 over the three frozen backbones, with paired bootstrap intervals excluding zero. On the CATH midnight-zone benchmark, homologous-superfamily accuracy rises from 40.7% to 56.7% at 35M and from 43.3% to 62.7% at 150M, more than the 12 points a CATH-supervised model gains over its own base. Against a maximally sensitive HMMER search, alignment keeps the best single hit at both ESM-2 scales, but the embedding leads at ranking depth (MAP 0.705 against 0.607 at 150M) and returns a ranking for every query, including those where alignment finds none. Under a trained linear probe, however, the same embeddings fall below the untuned backbone, as do structure-distilled embeddings against theirs, so the gain in neighborhood structure comes at a cost in linear-probe accuracy. Code and models: https://github.com/oriel9p/ProtSent

We envision a general-purpose protein foundation model as a unified, interactive model of sequence, structure, and function, capable of understanding, generation, and cross-modal translation while retaining the instruction-following and reasoning capabilities of modern LLMs. Toward this vision, we introduce Trion-1, the first native, unified, multimodal diffusion language model (dLLM) for proteins. Trion-1 is built on two key design choices: (1) a unified discrete token space that jointly represents sequence, structure, and natural language; (2) a dLLM backbone that replaces strict left-to-right factorization with bidirectional or semi-bidirectional token interactions, enabling global residue communication and long-range contact modeling that are naturally suited to structure modeling. This formulation unifies six core capabilities: folding, inverse folding, sequence understanding, structure understanding, sequence design, and structure design, while retaining the native language capabilities of general-purpose LLMs. On established benchmarks, Trion-1 achieves broadly competitive performance. In folding, Trion-1 achieves a TM-score above 0.8 with a single sample under strict homology control, matching or exceeding the existing tri-modal model using 200 samples. For sequence understanding, Trion-1 performs on par with top-tier LLMs such as Claude Opus 4.7 (62.37%). These results support well-aligned sequence-structure-language representations and strong cross-modal generative capabilities.

Protein homology search underlies function annotation, structure prediction, and evolutionary analysis, but remains challenging in the "twilight zone," where global sequence similarity is weak and classical alignment methods lose sensitivity. Protein language models provide context-aware representations that could improve alignment sensitivity in this regime. However, prior protein embedding-based retrieval pipelines often pool these representations into a single vector, potentially obscuring local motifs, domains, or conserved residues that reveal remote homology. We introduce ProtoCol, a model which represents proteins as sets of residue embeddings and uses ColBERT-style late interaction to test whether residue-level comparison improves homolog retrieval. ProtoCol encodes proteins independently, keeps candidate representations pre-computable, and scores candidates with MaxSim over residue embeddings. On SCOPe superfamily and Pfam clan benchmarks, ProtoCol outperforms sequence-composition, alignment-based, pooled PLM, and trained single-vector baselines, supporting late interaction as an effective retrieval layer for remote homology search.