Distance-AF Multimer

Constraint-guided protein complex structure modeling with distributed multi-GPU optimization

Distance-AF Multimer (Feb 2024 – Sept 2025)

Role: Lead Developer

A distributed multi-GPU optimization pipeline for protein-complex structure prediction that integrates user-supplied distance constraints with AlphaFold-Multimer, achieving up to ~60Å global-RMSD improvement on the hardest targets.

Key Achievements

  • Built a distributed multi-GPU optimization pipeline (PyTorch) for protein-complex structure prediction on 50k+ atom graphs
  • Adapted the lab’s Distance-AF method to complexes as a per-target, test-time optimization that fits user-supplied distance constraints with no retraining — robust to noisy and partial constraints
  • Designed a coarse-to-fine, two-stage optimization that escapes local minima from AlphaFold-Multimer’s poor initialization, eliminating flying residues and steric clashes
  • Benchmarked across three datasets (27 hard multimeric targets, 8 peptide complexes, 3 large assemblies with 10+ chains); outperformed Chai-1, Protenix, and AlphaLink2 on Win/Tie/Loss

Publications

  • Ling, K. et al. “Peptide–protein docking: from physics-based models to generative intelligence.” Chemical Communications, 2026
  • Ling, K. et al. “Distance-AF Multimer: A Multi-Step Approach for Protein Complex Structure Modeling with User-Defined Distance Constraints.” (In preparation)

Technologies

  • PyTorch, Distributed multi-GPU optimization
  • AlphaFold2-Multimer integration
  • Test-time optimization with experimental constraints

Conducted at Kihara Lab, Purdue University