让扩散模型在推理时发现蛋白质隐藏构象变化
Unlocking hidden biomolecular conformational landscapes in diffusion models at inference time
- 结合分类器引导、过滤与自由能估计，提升构象采样效率
- 无需先验知识即可捕捉域运动、隐秘口袋柔性等真实变化
- 适用于静态结构预测模型，对生物关键蛋白有高实用价值
蛋白质等功能分子的活性依赖于其在多种结构构象间的动态转换。几十年来，科研人员致力于开发计算方法以预测这些构象分布，这比确定静态折叠结构更难实验验证。本文提出ConforMix，一种推理阶段算法，通过结合分类器引导、过滤与自由能估计，增强扩散模型对构象分布的采样能力。该方法可升级任何用于静态结构预测或构象生成的扩散模型，实现更高效的构象变异性发现，且无需事先了解主要构象自由度。ConforMix与模型预训练改进互补，即使对于能完美再现玻尔兹曼分布的假设模型也有增益。应用在静态结构预测模型上时，ConforMix成功捕捉了域运动、隐秘口袋柔性及转运蛋白循环等结构变化，同时避免了物理上不合理的状态。多个生物学重要蛋白的案例研究证明了该方法的可扩展性、准确性和实用性。
原文摘要 · Abstract (English)
The function of biomolecules such as proteins depends on their ability to interconvert between a wide range of structures or "conformations." Researchers have endeavored for decades to develop computational methods to predict the distribution of conformations, which is far harder to determine experimentally than a static folded structure. We present ConforMix, an inference-time algorithm that enhances sampling of conformational distributions using a combination of classifier guidance, filtering, and free energy estimation. Our approach upgrades diffusion models -- whether trained for static structure prediction or conformational generation -- to enable more efficient discovery of conformational variability without requiring prior knowledge of major degrees of freedom. ConforMix is orthogonal to improvements in model pretraining and would benefit even a hypothetical model that perfectly reproduced the Boltzmann distribution. Remarkably, when applied to a diffusion model trained for static structure prediction, ConforMix captures structural changes including domain motion, cryptic pocket flexibility, and transporter cycling, while avoiding unphysical states. Case studies of biologically critical proteins demonstrate the scalability, accuracy, and utility of this method.
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