Dionne K. Vafeados

This paper computationally designs thermostable miniproteins that neutralize Nipah and Hendra viruses with subnanomolar affinity and potent in vitro antiviral activity.

This paper designs de novo integrated domains using AI tools to enable plant NLR receptors to recognize non-cognate pathogen effectors, demonstrating functional immune responses and structural fidelity.

This paper designs sequence-specific DNA binding proteins using deep learning, achieving a 100-fold improvement in success rates over previous methods.

This paper designs a protein family that binds and senses small molecules using deep learning and physics-based methods, achieving nanomolar sensitivity for cortisol.

This paper demonstrates that bioPROTACs can reprogram CAR T-cells by targeting DNMT3A, offering a tunable alternative to gene editing for enhancing T-cell function.

Conditioning RFdiffusion on edge beta-strand pairing yields specific binders to polar targets, with in silico success above 9.2% versus 0.98% for hotspot-only conditioning.

This paper designs antibodies de novo with atomic precision using RFdiffusion and yeast display, validated by cryo-EM and biophysical methods.

The paper designs high-affinity miniprotein binders targeting Flpp3, a virulence factor of Francisella tularensis, using physics-based and deep-learning methods, achieving sub-nanomolar affinities.

This paper introduces computational methods to design pH-sensitive binders that dissociate under acidic conditions, demonstrating their application in targeting various receptors and proteins.

This paper introduces RFD2-MI, a deep learning framework for designing phosphotyrosine peptide binders, showing high specificity and affinity for phosphorylated targets.

This paper designs sequence-specific DNA-binding proteins computationally and validates them experimentally, achieving high specificity and affinity for target sequences.

The paper designs orthogonal fluorophore-binding proteins using deep learning for multiplex imaging, demonstrating high specificity and utility in labeling and monitoring subcellular processes.

The paper develops RFdiffusion to generate protein binders for IDPs/IDRs, achieving nanomolar affinity and functional applications in amylin and IL2RG.

The paper designs high specificity binders for peptide-MHC-I complexes using deep learning, achieving specific targeting of pMHCs and enabling T-cell activation.

This paper presents computational methods for designing GPCR-binding miniproteins and validates them through high-throughput screening, producing agonists and antagonists for multiple GPCRs.

The paper develops a method to assess solution properties of designed proteins using mass spectrometry and peptide barcoding, enabling analysis of large protein libraries.

The paper designs nanomolar binders for diverse small molecules using a pseudocycle-based shape complementarity method, enabling direct conversion into sensing systems.

The paper designs proteins that template mineralization of metal-oxides, showing they can guide hematite and ZnO growth under specific conditions.

The paper presents a catalog of transcription factor binding sites in Drosophila and C. elegans, revealing tissue-specific regulatory relationships using machine learning.

The paper designs EndoTags to trigger endocytosis and degradation of target proteins, enabling tissue-specific targeting and therapeutic applications.