Sen Cao

HighFold-Lasso predicts lasso peptide structures using topology-guided methods, achieving perfect topology success and low RMSD on a test set of 52 structures.

High-PepBinder generates target-specific peptides using a pLM-guided diffusion model and a comprehensive dataset, producing peptides with favorable binding properties and sequence diversity.

HighFold3 improves cyclic peptide structure prediction, especially for those with unnatural amino acids, outperforming existing models.

The paper introduces PPIKB, a database and platform for protein-peptide interactions, compiled from literature and patents with manual validation, supporting drug discovery and systems biology research.

HighFold2 predicts 3D structures of cyclic peptides with unnatural amino acids, achieving a median RMSD of 1.891 Å for monomers and complexes.

MeDCycFold accelerates cyclic peptide structure prediction using a deep learning-based Rosetta distillation model, achieving 49x speedup while maintaining Rosetta-level accuracy.