Jingjing Guo

This paper introduces RA-GHN, a framework for antibody sequence-structure co-design that captures multi-body interactions and structural constraints, outperforming existing methods.

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

MINT predicts enzyme-cofactor specificity using a multimodal framework with transfer learning, achieving high accuracy in cofactor binding prediction.

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.

This study evaluates AlphaFold3's accuracy in predicting non-canonical cyclic peptides and provides guidelines for its application.

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

TransCodon is a Transformer-based model that predicts optimal synonymous codons by integrating species-specific information, 5'UTRs, and RNA structure, improving translational efficiency in heterologous hosts.

This study introduces cycledesigner, a method combining RFdiffusion, ProteinMPNN, and HighFold to design cyclic peptide binders for specific targets, followed by scoring functions for screening.

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.

NCPepFold predicts structures of non-canonical cyclic peptides with improved accuracy using multigranular representation and cyclization optimization.

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.

This study benchmarks reverse docking pipelines using AlphaFold2 human proteome to predict drug-target interactions and off-target effects, finding that Glide_sfct (PS) performs best.

The paper curates a database of disulfide bonds and their effects on protein thermostability, develops machine learning models to predict beneficial disulfide bonds, and demonstrates the utility of AlphaFold2 for this task.

This paper introduces IGModel, a deep learning framework that predicts protein-ligand docking RMSD and binding strength, achieving state-of-the-art performance on multiple benchmarks.