标题： FlowBack-Adjoint：物理感知且能量引导的全原子蛋白质回溯条件流匹配 显示英文标题

标题： FlowBack-Adjoint: Physics-Aware and Energy-Guided Conditional Flow-Matching for All-Atom Protein Backmapping

摘要： 粗粒度（CG）蛋白质分子模型可以显著增加蛋白质分子动力学模拟的时间和长度尺度，但从CG模拟轨迹中恢复准确的全原子（AA）系综对于揭示折叠和对接的分子机制以及计算需要原子细节的物理性质至关重要。最近报道的深度生成模型FlowBack使用流匹配架构将AA细节恢复到蛋白质C-α轨迹，并在生成AA结构系综方面表现出最先进的性能。然而，训练仅在结构数据上进行，训练过程中缺乏对原子间能量或力的任何意识，导致错误键长、原子碰撞和高能结构的比例较小。在这项工作中，我们引入了FlowBack-Adjoint作为轻量级增强，通过一次物理感知的后训练过程升级预训练的FlowBack模型。辅助贡献引入了键长和Lennard-Jones相互作用的物理意识，并通过伴随匹配将分子力学力场能量的梯度纳入以引导FlowBack-Adjoint向量场产生低能构型。与FlowBack相比，在基准测试中，FlowBack-Adjoint将单点能量降低了中位数约78 kcal/mol.残基，将键长误差减少了超过92%，消除了超过98%的分子碰撞，保持了AA构型系综的优秀多样性，并产生了能够初始化稳定全原子分子动力学模拟的构型，而无需进行能量松弛。我们提出FlowBack-Adjoint作为一种准确且高效的物理感知深度生成模型，用于从C-α轨迹进行AA回映射。 显示英文摘要

摘要： Coarse-grained (CG) molecular models of proteins can substantially increase the time and length scales accessible to molecular dynamics simulations of proteins, but recovery of accurate all-atom (AA) ensembles from CG simulation trajectories can be essential for exposing molecular mechanisms of folding and docking and for calculation of physical properties requiring atomistic detail. The recently reported deep generative model FlowBack restores AA detail to protein C-alpha traces using a flow-matching architecture and demonstrates state-of-the-art performance in generation of AA structural ensembles. Training, however, is performed exclusively on structural data and the absence of any awareness of interatomic energies or forces within training results in small fractions of incorrect bond lengths, atomic clashes, and otherwise high-energy structures. In this work, we introduce FlowBack-Adjoint as a lightweight enhancement that upgrades the pre-trained FlowBack model through a one-time, physics-aware post-training pass. Auxiliary contributions to the flow introduce physical awareness of bond lengths and Lennard-Jones interactions and gradients of a molecular mechanics force field energy are incorporated via adjoint matching to steer the FlowBack-Adjoint vector field to produce lower-energy configurations. In benchmark tests against FlowBack, FlowBack-Adjoint lowers single-point energies by a median of ~78 kcal/mol.residue, reduces errors in bond lengths by >92%, eliminates >98% of molecular clashes, maintains excellent diversity of the AA configurational ensemble, and produces configurations capable of initializing stable all-atom molecular dynamics simulations without requiring energy relaxation. We propose FlowBack-Adjoint as an accurate and efficient physics-aware deep generative model for AA backmapping from C-alpha traces.