439: Coembedding Sequence and Structure: CLSS Maps the Protein Universe

Longo LM et al., PNAS - This episode summarizes a PNAS study introducing CLSS, a contrastive two-tower protein language model that coembeds domain sequences, structures, and subsequences into a shared 32-dimensional latent space. Trained self-supervised on one million ECOD domains, CLSS aligns sequence and structure modalities, yields compact embeddings that recapitulate ECOD and CATH hierarchies, outperforms several state-of-the-art PLMs on ProteinShake classification tasks, and powers an interactive viewer for exploring protein space. Key terms: contrastive learning, protein sequence, protein structure, protein domains, protein embeddings. Study Highlights:The authors developed CLSS, a contrastive two-tower model that coembeds full domain sequences, structures, and sampled subsequences into a single latent space. CLSS embeddings recapitulate expert ECOD and CATH hierarchical labels despite never using those labels during training. A subsequence-trained variant (CLSS-sub) meaningfully embeds fragments, and CLSS outperforms comparison PLMs on downstream ProteinShake classification benchmarks. Visualizations of CLSS maps reveal a strong partitioning of domains by cofactor binding and other functional annotations. Conclusion:CLSS demonstrates that sequence and structure can be jointly organized into a compact, informative embedding space that captures domain hierarchy, subsequence reuse, and functional preferences; these embeddings enable efficient downstream classification, visualization, and potential applications in database search, alignment, protein design, and evolutionary analysis. Music:Enjoy the music based on this article at the end of the episode. Article title:Contrastive learning unites sequence and structure in a global representation of protein space First author:Longo LM Journal:PNAS DOI:10.1073/pnas.2532702123 Reference:Longo LM, Yanai G, Axel G, Kolodny R, Ben-Tal N. Contrastive learning unites sequence and structure in a global representation of protein space. PNAS. 2026;123(32):e2532702123. doi:10.1073/pnas.2532702123. License:This episode is based on an open-access article published under the Creative Commons Attribution 4.0 International License (CC BY 4.0) – https://creativecommons.org/licenses/by/4.0/ Support:Base by Base is independent and ad-free — no sponsors, no paywall. If an episode was worth your time, chip in and keep the papers audited and the original songs coming:❤️ Support monthly: https://buy.stripe.com/cNifZhclVebvagk2JDgEg01☕ One-time donation: https://donate.stripe.com/7sY4gz71B2sN3RWac5gEg00 More at basebybase.com On PaperCast Base by Base you'll discover the latest in genomics, functional genomics, structural genomics, and proteomics. Episode link: https://basebybase.com/episodes/clss-contrastive-sequence-structure-protein-space QC:This episode was checked against the original article PDF and publication metadata for the episode release published on 2026-08-11. QC Scope:- article metadata and core scientific claims from the narration- excludes analogies, intro/outro, and music- transcript coverage: Audited transcript portions describing CLSS concept and motivation, two-tower architecture, CLSS-sub subsequences, training on ECOD domains, evaluation on ProteinShake, TSNE visualizations, cofactor and zinc-binding patterns, and limitations.- transcript topics: CLSS concept and motivation; Two-tower architecture: sequence tower (ESM2) and frozen structure encoder (ESM3); CLSS-sub subsequences (20-60 residues); Self-supervised training on ~1 million ECOD domains; Evaluation on ProteinShake and ECOD/CATH hierarchies; t-SNE visualizations showing coembedding of sequence and structure QC Summary:- factual score: 10/10- metadata score: 10/10- supported c...