Xinting Li

The paper designs and validates protein readers that specifically detect disease-related modifications in Tau and Abeta in Alzheimer's disease models and human samples.

This study designs synthetic activators for programmable gene expression by engineering coactivator-binding proteins and demonstrates their enhanced transcriptional activation capabilities.

This paper designs de novo metalloproteases using RoseTTAFold Diffusion 2, achieving high activity and specificity for cleaving various substrates.

This paper develops a computational method to design glycoproteins with controlled N-linked glycan composition by encoding glycan location and structure into amino acid sequences.

This paper describes a scalable recombinant pipeline for producing disulphide-stapled cyclic peptides, achieving high yields and enabling high-throughput screening.

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

This study designs allosterically controlled enzymes using denoising diffusion models, showing that activity can be activated or repressed by designed effectors.

This paper introduces scalable experimental and computational workflows to accelerate protein design by improving throughput, reducing costs, and enabling rapid validation of thousands of protein designs.

The paper designs de novo proteins that form autocatalytic isopeptide bonds, enabling rigid covalent protein assemblies through intramolecular and intermolecular crosslinking.

This paper designs a chemically induced dimerization system using a de novo macrocycle and protein homodimer to control cellular processes.

This study designs Ras isoform-specific binders using deep learning to target the C-termini of Ras proteins, enabling specific inhibition and study of their roles in biology and disease.

This paper designs soluble, miniaturized MHC proteins that enable stable expression in E. coli and retain peptide and TCR binding specificity.

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

This paper designs beta barrels and transmembrane nanopores using deep learning and parametric methods, achieving high success rates with experimental validation.

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 designs proteins that bind intrinsically disordered regions with high affinity and specificity across diverse targets.

The paper designs de novo proteins called NeoNectins that specifically bind integrin α5β1, enhancing cell attachment and tissue integration in biomaterials.

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

This paper introduces WRAPs, designed proteins that solubilize membrane proteins without detergents, enabling structural studies and vaccine development.

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 peptide traps that bind amyloidogenic segments in {beta}-strand and {beta}-hairpin conformations, effectively blocking fibril assembly for A{beta}42.

This paper introduces RFAA and RFdiffusionAA, models that can predict and design complex biomolecular assemblies including proteins, nucleic acids, small molecules, and covalent modifications.