Cross-state representation learning enables prospective discovery of dynamic protein allosteric sites
Year: 2026
Type: preprint
Abstract: Allosteric regulation offers multifunctional induction in therapeutic interventions, yet conformational plasticity hinders computational identification of allosteric sites. Machine learning models rely on static holo (ligand-bound) structures, generalizing poorly to apo or substrate-bound states where binding pockets are transient or cryptic. We present AsBench2, a cross-state site-recognition benchmark, and AllositeUltra, a deep learning framework integrating multimodal protein representations (combining structure-aware language models with physical dynamics) and residue-guided candidate supplementation, effectively decoupling site detection from static pocket geometry. AllositeUltra demonstrates superior generalization, achieving Top-3 site recovery rates of 65.2% and 70.6% on apo and substrate-bound structures, respectively, representing relative improvements of 36.4% and approximately 50% over the best-performing baselines. Prospective application to MAGI2 identifies a previously unrecognized interdomain allosteric pocket, validated at atomic resolution through small-molecule screening and X-ray crystallography. Proteome-wide application yields a high-confidence atlas of targetable allosteric sites, bridging the gap between structural representation learning and prospective AI-driven drug discovery.
Institutions: Shanghai Jiao Tong University, Chinese Academy of Sciences
Language: English
Subfield: Drug Discovery
Domain: Life Sciences
Open Access status: green
OpenAlex ID: W7225754671
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