Dingeldein Lars, Silva-Sánchez David, Evans Luke, D'Imprima Edoardo, Grigorieff Nikolaus, Covino Roberto, Cossio Pilar
Institute of Physics, Faculty of Physics, Goethe University Frankfurt, Frankfurt am Main 60438, Germany.
Frankfurt Institute for Advanced Studies, Frankfurt am Main 60438, Germany.
Proc Natl Acad Sci U S A. 2025 Jun 10;122(23):e2420158122. doi: 10.1073/pnas.2420158122. Epub 2025 Jun 4.
Characterizing the conformational ensemble of biomolecular systems is key to understand their functions. Cryoelectron microscopy (cryo-EM) captures two-dimensional snapshots of biomolecular ensembles, giving in principle access to thermodynamics. However, these images are very noisy and show projections of the molecule in unknown orientations, making it very difficult to identify the biomolecule's conformation in each individual image. Here, we introduce cryo-EM simulation-based inference (cryoSBI) to infer the conformations of biomolecules and the uncertainties associated with the inference from individual cryo-EM images. CryoSBI builds on simulation-based inference, a merger of physics-based simulations and probabilistic deep learning, allowing us to use Bayesian inference even when likelihoods are too expensive to calculate. We begin with an ensemble of conformations, templates from experiments, and molecular modeling, serving as structural hypotheses. We train a neural network approximating the Bayesian posterior using simulated images from these templates and then use it to accurately infer the conformation of the biomolecule from each experimental image. Training is only done once on simulations, and after that, it takes just a few milliseconds to make inference on an image, making cryoSBI suitable for arbitrarily large datasets and direct analysis on micrographs. CryoSBI eliminates the need to estimate particle pose and imaging parameters, significantly enhancing the computational speed compared to explicit likelihood methods. Importantly, we obtain interpretable machine learning models by integrating physics-based approaches with deep neural networks, ensuring that our results are transparent and reliable. We illustrate and benchmark cryoSBI on synthetic data and showcase its promise on experimental single-particle cryo-EM data.
表征生物分子系统的构象集合是理解其功能的关键。冷冻电子显微镜（cryo-EM）捕捉生物分子集合的二维快照，原则上可以获取热力学信息。然而，这些图像噪声很大，且显示的是分子在未知方向上的投影，使得在每张单独的图像中识别生物分子的构象非常困难。在这里，我们引入基于冷冻电子显微镜模拟的推理（cryoSBI）来推断生物分子的构象以及与从单个冷冻电子显微镜图像推理相关的不确定性。CryoSBI建立在基于模拟的推理之上，这是一种基于物理的模拟与概率深度学习的融合，使我们即使在似然性计算成本过高时也能使用贝叶斯推理。我们从一个构象集合开始，这些构象来自实验模板和分子建模，作为结构假设。我们使用这些模板的模拟图像训练一个近似贝叶斯后验的神经网络，然后用它从每张实验图像中准确推断生物分子的构象。训练只在模拟上进行一次，之后，对一张图像进行推理只需几毫秒，这使得CryoSBI适用于任意大的数据集，并能直接对显微照片进行分析。CryoSBI无需估计粒子姿态和成像参数，与显式似然方法相比，显著提高了计算速度。重要的是，我们通过将基于物理的方法与深度神经网络相结合，获得了可解释的机器学习模型，确保我们的结果是透明且可靠的。我们在合成数据上对CryoSBI进行了说明和基准测试，并展示了其在实验单粒子冷冻电子显微镜数据上的前景。
