Dionne K. Vafeados

Computational design of potent, broadly neutralizing anti-Nipah virus and Hendra virus miniproteins

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

Computational design of de novo integrated domains enables rational control of pathogen effector recognition in plant NLR immune receptors.

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.

Generative design of sequence specific DNA binding proteins

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

Reprogramming CAR T-Cells with designed bioPROTACs

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.

Improved protein binder design using -pairing targeted RFdiffusion

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.

De novo design of high-affinity miniprotein binders targeting Francisella tularensis virulence factor

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.

Computational design of pH-sensitive binders

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

De novo design of phosphotyrosine peptide binders

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

Computational design of sequence-specific DNA-binding proteins

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

Design of Orthogonal Far-Red, Orange and Green Fluorophore-binding Proteins for Multiplex Imaging

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

Diffusing protein binders to intrinsically disordered proteins

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

Design of high specificity binders for peptide-MHC-I complexes

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

De novo design of miniprotein agonists and antagonists targeting G protein-coupled receptors

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

Massively parallel assessment of designed protein solution properties using mass spectrometry and peptide barcoding

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

Programmed mineralization of metal-oxides by de novo designed protein templates

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

Binding profiles for 954 Drosophila and C. elegans transcription factors revealtissue specific regulatory relationships

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

Designed Endocytosis-Triggering Proteins mediate Targeted Degradation and amplify signaling

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