Chengyun Zhang

HighFold-Lasso: Topology-Guided Prediction of Lasso Peptide Structures Using AlphaFold3

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: A pLM-Guided Latent Diffusion Framework for Affinity-Aware Target-Specific Peptide Design

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.

AlphaFold3 for Non-canonical Cyclic Peptide Modeling: Hierarchical Benchmarking Reveals Accuracy and Practical Guidelines

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

Cycledesigner Leveraging RFdiffusion and HighFold to Design Cyclic Peptide Binders for Specific Targets

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.

PPIKB: A Comprehensive Knowledge Base and Analysis Platform for Protein Peptide Interactions Based on Literature and Patents

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.

HighPlay：Cyclic Peptide Sequence Design Based on Reinforcement Learning and Protein Structure Prediction

HighPlay uses reinforcement learning and protein structure prediction to design cyclic peptides with good binding affinity, validated through molecular dynamics simulation and experimental testing.

NCPepFold: Accurate Prediction of Non-canonical Cyclic Peptide Structures via Cyclization Optimization with Multigranular Representation

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

PepMSND: Integrating Multi-level Feature Engineering and Comprehensive Databases to Enhance in vivo/in vitro Peptide Blood Stability Prediction

The paper develops a deep learning model called PepMSND to predict peptide blood stability using multi-level feature engineering and a comprehensive database of 635 samples, achieving high accuracy and AUC.

GAPS: Geometric Attention-based Networks for Peptide Binding Sites Identification by the Transfer Learning Approach

GAPS uses geometric attention mechanisms and transfer learning to identify protein-peptide binding sites, achieving state-of-the-art performance.

Highfold: accurately predicting cyclic peptide monomers and complexes with AlphaFold

Highfold predicts cyclic peptides and their complexes using AlphaFold, improving accuracy by incorporating structural information.

Multi_CycGT: a DL-based multimodal model for membrane permeability prediction of cyclic peptides

This paper introduces Multi_CycGT, a deep learning model for predicting cyclic peptide membrane permeability, achieving high accuracy and AUC on a benchmark dataset.