AI 中文总结
PhiFold通过将蛋白质结构生成与平衡动力学协方差建模统一，分解为局部柔性、集体运动和参与度三部分，实现功能感知的动态蛋白质设计。
AI 中文摘要
蛋白质设计正从结构正确性迈向功能感知设计，然而现有生成模型通常将动力学视为结构生成后通过模拟或预测估计的下游属性。将分子动力学轨迹作为生成目标也不可取，因为随机、路径依赖的轨迹过度指定了潜在的平衡系综。我们提出PhiFold，一个联合生成蛋白质主链及其二阶动力学（以残基位移协方差表示）的框架。PhiFold不预测二次规模的完整协方差，而是将动力学分解为三个可解释的组成部分：局部柔性、低秩集体运动表示和残基级集体参与度。这些组件被组装成具有精确边际一致性的正定协方差矩阵，产生平衡动力学的紧凑且物理约束的表示。在生成的蛋白质中，PhiFold提高了局部波动和长程残基耦合的恢复能力，同时在主导集体运动子空间上保持竞争力。它进一步实现了残基柔性的双向控制，同时保持主链可设计性。通过将结构生成与平衡动力学的显式表示统一起来，PhiFold为不仅根据蛋白质外观，还根据其运动方式设计蛋白质奠定了基础。
英文摘要
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.
