De Novo Computational Design of VHH Nanobodies Against LGR5
Xu, C.; Li, Y.; Nguyen, T.; Zhou, Y.; Cong, L.; Lee, T.-H.; Lang, Y.; Shek, R.; Yi, L.; Greisen, P.
Show abstract
VHH discovery traditionally relies on animal immunization or large-scale library screening, methods that are slow, costly, and often ineffective for challenging targets such as GPCRs. We present a fully de novo computational pipeline for epitope-directed VHH design, integrating generative backbone modeling, deep learning-based sequence optimization, and iterative experimental feedback. Using LGR5 as a model, we progressed from in silico design to functional binders without structural templates. Across three design-test-learn cycles, millions of candidates were reduced to epitope-specific binders with nanomolar affinity and high thermal stability (melting temperature [Tm] > 65 {degrees}C). Cryogenic electron microscopy (cryo-EM) confirmed atomic-level agreement (RMSD {approx} 2.2 [A]). This structure-validated approach accelerates timelines, reduces cost, and is broadly applicable to GPCRs and other membrane proteins, enabling "on-demand" therapeutic antibody generation.
Matching journals
The top 6 journals account for 50% of the predicted probability mass.
Similar papers in this journal
- AlphaBind, a Domain-Specific Model to Predict and Optimize Antibody-Antigen Binding Affinity 96%
- Tuning antibody stability and function by rational designs of framework mutations 95%
- Beyond Sequence Similarity: ML-Powered Identification of pHLA Off-Targets for TCR-Mimic Antibodies Using High Throughput Binding Kinetics 95%
Similar papers in this journal
Similar papers in this journal
- intDesc-AbMut: Describing and understanding how antibody mutations impact their environmental interactions 94%
- Small-molecule modulators of TRMT2A decrease PolyQ aggregation and PolyQ-induced cell death 94%
- Towards mechanistic models of mutational effects: Deep Learning on Alzheimer's Aβ peptide 93%
Similar papers in this journal
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.