Clara T. Schoeder

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.

This paper benchmarks deep learning-based peptide design methods for GPCRs, finding that validation fails to distinguish valid from invalid designs, highlighting scoring challenges.

FrustraMPNN rapidly predicts single-residue frustration in proteins at proteome scale with high accuracy, enabling new insights into protein function and dynamics.

This study develops a dual-chimeric virus to safely study antiviral resistance and cross-resistance using spike and protease inhibitors.

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 study compares self-supervised ML methods with biophysical approaches in protein design, finding ML improves sampling but not scoring of high-fitness variants.

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

The paper uses Rosetta comparative modeling to generate structural models of ΔF508 CFTR and its variants, revealing how the mutation destabilizes the protein and how drugs like VX-809 stabilize it.

The paper predicts animal species susceptibility to SARS-CoV-2 by analyzing ACE2 receptor sequences and structures to identify key residues that determine infection risk.