Hierarchical Discrete Representations for Coarse-to-Fine Protein Conformation Generation

Area of Science:

• Structural Biology
• Machine Learning
• Computational Chemistry

Background:

• Generating diverse and physically plausible protein conformations is essential for understanding protein dynamics, intrinsically disordered proteins (IDPs), and drug discovery.
• Current generative models in continuous coordinate space struggle with structural validity and handling long-range dependencies, often forcing a trade-off between diversity and accuracy.

Purpose of the Study:

• To develop a novel approach for protein conformation generation that overcomes the limitations of existing continuous-space models.
• To generate diverse and structurally valid protein ensembles using hierarchical discrete representations.

Main Methods:

• Proposed a novel approach utilizing hierarchical discrete representations learned through vector quantization (VQ-VAE).
• Discretized residue-level structural contexts into multi-level codebooks.
• Employed a coarse-to-fine generation strategy, starting with a global scaffold and progressively refining local geometry.

Main Results:

• The proposed method achieved superior performance compared to state-of-the-art models like ESMDiff on benchmark datasets (BPTI MD trajectories, conformational-changing pairs).
• Successfully mitigated the trade-off between conformational diversity and structural validity.
• Generated realistic protein ensembles with interpretable and scalable geometric representations.

Conclusions:

• The hierarchical discrete representation approach offers a powerful new paradigm for flexible biomolecular modeling.
• This method enhances the generation of accurate and diverse protein conformations, advancing structural biology and drug discovery applications.