Rational design of potent phosphopeptide binders to endocrine Snk PBD domain by integrating machine learning optimization, molecular dynamics simulation, binding energetics rescoring, and in vitro affinity assay.

Human Snk is an evolutionarily conserved serine/threonine kinase essential for the maintenance of endocrine stability. The protein consists of a N-terminal catalytic domain and a C-terminal polo-box domain (PBD) that determines subcellular localization and substrate specificity. Here, an integrated strategy is described to explore the vast structural diversity space of Snk PBD-binding phosphopeptides at a molecular level using machine learning modeling, annealing optimization, dynamics simulation, and energetics rescoring, focusing on the recognition specificity and motif preference of the Snk PBD domain. We further performed a systematic rational design of potent phosphopeptide ligands for the domain based on the harvested knowledge, from which a few potent binders were also confirmed by fluorescence-based assays. A phosphopeptide PP17 was designed as a good binder with affinity improvement by 6.7-fold relative to the control PP0, while the other three designed phosphopeptides PP7, PP13, and PP15 exhibit a comparable potency with PP0. In addition, a basic recognition motif that divides potent Snk PBD-binding sequences into four residue blocks was defined, namely [Χ