Nagoya University

PDZ-binding motifs (PBMs) are short C-terminal sequences that determine which PDZ domains a protein binds. Designing PBMs by affinity has generally required a dedicated model per domain and made off-target control hard. This work encodes PDZ and PBM sequences into one 128-dimensional unit hypersphere, predicts pKd from their cosine similarity, and writes single-target, target-minus-off-target and dual-target-mean objectives as directions on that sphere; in four-position redesign for DLG4-PDZ3, 84 of the top 100 candidates fell in the top 1% of measured fitness in a published mutant library, versus 64 for the comparison method.

PDZ-binding motifs (PBMs) are short C-terminal sequences that determine which PDZ domains a protein binds. Designing PBMs by affinity has generally required a dedicated model per domain and made off-target control hard. This work encodes PDZ and PBM sequences into one 128-dimensional unit hypersphere, predicts pKd from their cosine similarity, and writes single-target, target-minus-off-target and dual-target-mean objectives as directions on that sphere; in four-position redesign for DLG4-PDZ3, 84 of the top 100 candidates fell in the top 1% of measured fitness in a published mutant library, versus 64 for the comparison method.

Collider analyses have trained a separate model per task; EVENET instead represents each collision as an unordered particle cloud and pretrains one backbone on 500 million simulated events with self-supervised plus physics-informed objectives, then adapts it through interchangeable task heads. In an anti-isolated ϒ→μ+μ− search on CMS Open Data, the fully pretrained variant reaches a median local significance of 7.6σ after calibration, above the published 6.4σ, and across a heavy-resonance mass grid its maximum SIC averages about 50% above training from scratch.

Collider analyses have trained a separate model per task; EVENET instead represents each collision as an unordered particle cloud and pretrains one backbone on 500 million simulated events with self-supervised plus physics-informed objectives, then adapts it through interchangeable task heads. In an anti-isolated ϒ→μ+μ− search on CMS Open Data, the fully pretrained variant reaches a median local significance of 7.6σ after calibration, above the published 6.4σ, and across a heavy-resonance mass grid its maximum SIC averages about 50% above training from scratch.