PhiFold generates protein shapes and how they move together
PhiFold: Towards Dynamic Protein Design with Physics-Structured Covariance Modeling
Artificial Intelligence
Summary
Designing proteins with the right shape is important, but knowing how they move is even more crucial for their function. The authors introduce PhiFold, a method that creates protein backbones while also modeling their natural movements through a special kind of matrix called a covariance matrix. Instead of predicting complex data directly, PhiFold breaks it down into simpler, understandable parts to represent local flexibility and collective motions. This method helps design proteins not only by their structures but also by their dynamics, potentially improving how proteins work.
What this means in practice
- •For protein engineering teams: Create proteins with controlled flexibility and movement properties to improve function beyond static shape design.
- •For computational drug designers: Generate protein structures accounting for dynamic behaviors that affect drug binding and efficacy.
Authors
Yutian Liu, Mujie Lin, LanqianZhang, Meng Fan, Chang Liu, ZhiweiNie, Siwei Ma
Abstract
Protein design is moving beyond structural correctness toward function-aware design, yet existing generative models typically treat dynamics as a downstream property estimated through simulation or prediction after structure generation. Using MD trajectories as a generative target is also undesirable because stochastic, path-dependent trajectories over-specify the underlying equilibrium ensemble. We introduce PhiFold, a framework for jointly generating protein backbones and their second-order dynamics, represented by residue-displacement covariance. Rather than predicting the quadratically sized full covariance, PhiFold decomposes dynamics into three interpretable components: local flexibility, a low-rank collective-motion representation, and residue-wise collective participation. These components are assembled into a positive-definite covariance matrix with exact marginal consistency, yielding a compact and physically constrained representation of equilibrium dynamics. Across generated proteins, PhiFold improves recovery of local fluctuations and long-range residue coupling while remaining competitive on dominant collective-motion subspaces. It further enables bidirectional control of residue flexibility while preserving backbone designability. By unifying structure generation with an explicit representation of equilibrium dynamics, PhiFold lays a foundation for designing proteins not only by how they look, but also by how they move.