PhaseFlow

PhaseFlow is a multimodal generative model for liquid-liquid phase separation (LLPS). It connects full-length protein understanding, peptide-scale phase diagrams, and mutation-level optimization in one workflow.

The core peptide module jointly models amino acid sequences and 4x4 PSSI phase diagrams. A Transfusion-style Transformer combines autoregressive sequence modeling with Conditional Optimal Transport Flow Matching over continuous phase values, enabling both sequence-to-phase prediction and phase-to-sequence design. A full-length GNN-Transformer branch then brings in long-range sequence, structural, disorder, and peptide-derived local phase evidence for protein-scale LLPS prediction and droplet-promoting region scanning.

What It Enables

• Predicting full 4x4 LLPS phase diagrams from peptide sequences.
• Generating novel peptide sequences conditioned on target phase behavior.
• Predicting full-length protein LLPS propensity.
• Scanning proteins for droplet-promoting regions.
• Scoring mutation effects and steering in-silico directed evolution toward desired phase profiles.

My Role

• Quantitative analysis and mechanistic insight. Independently developed a quantitative framework linking concentration and illumination to phase-separation intensity from large-scale wet-lab data, identifying key regulatory variables and formulating experimentally testable mechanistic hypotheses.
• Machine learning task formulation. Devised a clever formulation of the machine learning task based on insights from large-scale wet-lab data, driving PhaseFlow to state-of-the-art performance.