Cross-state representation learning enables prospective discovery of dynamic protein allosteric sites
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…
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