Zicong Zhang

PathFold: Predicting the Entire Protein Folding Pathway from Protein Sequence Alone

PathFold predicts protein folding pathways from sequence using a deep learning framework combining AlphaFold and diffusion models, showing agreement with experimental data.

Prot-LAMBDA: Explicit Distance Learning Enhances Structural Reasoning in Protein Language Models

Prot-LAMBDA improves protein structure prediction by explicitly incorporating spatial relationships, achieving better performance than ESM2-3B in contact prediction and 3D structure prediction.

Accurate Macromolecular Complex Modeling for Cryo-EM with CryoZeta

CryoZeta improves cryo-EM structure modeling by integrating density features with sequence data using a diffusion-based deep learning model, achieving higher accuracy than existing methods.

Distance-AF: Modifying Predicted Protein Structure Models by Alphafold2 with User-Specified Distance Constraints

Distance-AF improves AlphaFold2 predictions by incorporating user-specified distance constraints to refine protein structures and reduce RMSD.

Improved Peptide Docking with Privileged Knowledge Distillation using Deep Learning

DistPepFold improves protein-peptide complex docking using a privileged knowledge distillation approach based on AlphaFold-Multimer, outperforming AFM on two datasets.

AFM-RL: Large Protein Complex Docking Using AlphaFold-Multimer and Reinforcement Learning

AFM-RL predicts structures of large protein complexes with six to twenty chains using AlphaFold-Multimer and reinforcement learning, showing improved performance over existing methods.