Biomolecular interactions trigger structural changes of protein complexes essential for cellular signaling, regulation, and drug action [1–4]. Modern co-folding models can now generate detailed structures of multi-component complexes directly from sequence [5–9]. However, recent evaluations reveal that they frequently...
Machine learning models for protein-ligand interactions commonly rely on atomic coordinates, which are constructed by fitting molecular models to experimental electron-density maps. However, most models represent each complex as a single fitted structure, which can omit information about conformational heterogeneity, p...
Seonghyun Park, Shuan Chen, Jun Jin Choong et al.· 0 citations
Single-cell RNA-seq representation learning is fundamentally label-free: cell identities, states, and contexts are not fixed training targets, so what constitutes signal or nuisance is analysis-dependent. A single representation must therefore preserve biological identity and state, remain robust to nuisance context, a...
Yunhak Oh, Yoonho Lee, Junseok Lee et al.· 0 citations
SoupFold is introduced, which improves co-folding predictions by learning simple mappings between the representation spaces of co-folding models, and achieves state-of-the-art performance on both protein-protein and protein-ligand structure prediction.
Hyosoon Jang, Taewon Kim, Sungsoo Ahn· 0 citations
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