Phillip Schlegel

Reward-guided inference-time search over the Proteína-Complexa sequence-structure codesign model yields validated binders across 127 targets, kinases and a free carbohydrate, with codesigned sequences outperforming ProteinMPNN redesign.

This paper introduces an AI-based framework for designing responsive protein oligomers that assemble in response to chemical or phosphorylation triggers, validated through experimental testing.

The paper designs and experimentally validates mini-protein binders targeting receptor-binding domains of Nipah, Langya, and Measles viruses using computational tools and limited screening.

This paper introduces HyperMPNN, a deep learning model trained on hyperthermophile proteins to design highly thermostable proteins, achieving stability up to 95°C.