PDF(Pubmed)
Abstract：
Molecules are essential building blocks of life and their different conformations (i.e., shapes) crucially determine the functional role that they play in living organisms. Cryogenic Electron Microscopy (cryo-EM) allows for acquisition of large image datasets of individual molecules. Recent advances in computational cryo-EM have made it possible to learn latent variable models of conformation landscapes. However, interpreting these latent spaces remains a challenge as their individual dimensions are often arbitrary. The key message of our work is that this interpretation challenge can be viewed as an Independent Component Analysis (ICA) problem where we seek models that have the property of identifiability. That means, they have an essentially unique solution, representing a conformational latent space that separates the different degrees of freedom a molecule is equipped with in nature. Thus, we aim to advance the computational field of cryo-EM beyond visualizations as we connect it with the theoretical framework of (nonlinear) ICA and discuss the need for identifiable models, improved metrics, and benchmarks. Moving forward, we propose future directions for enhancing the disentanglement of latent spaces in cryo-EM, refining evaluation metrics and exploring techniques that leverage physics-based decoders of biomolecular systems. Moreover, we discuss how future technological developments in time-resolved single particle imaging may enable the application of nonlinear ICA models that can discover the true conformation changes of molecules in nature. The pursuit of interpretable conformational latent spaces will empower researchers to unravel complex biological processes and facilitate targeted interventions. This has significant implications for drug discovery and structural biology more broadly. More generally, latent variable models are deployed widely across many scientific disciplines. Thus, the argument we present in this work has much broader applications in AI for science if we want to move from impressive nonlinear neural network models to mathematically grounded methods that can help us learn something new about nature.
摘要：
分子是生命及其不同构象的基本组成部分(即，形状)至关重要地决定了它们在生物体中发挥的功能作用。低温电子显微镜(cryo-EM)允许获取单个分子的大图像数据集。计算低温EM的最新进展使学习构象景观的潜在变量模型成为可能。然而,解释这些潜在空间仍然是一个挑战，因为它们的个体维度通常是任意的。我们工作的关键信息是，可以将这种解释挑战视为独立成分分析（ICA）问题，在该问题中，我们寻求具有可识别性的模型。这意味着,他们有一个本质上独特的解决方案，代表构象潜在空间，该空间将分子在自然界中配备的不同自由度分开。因此，我们的目标是推进cryo-EM的计算领域超越可视化，因为我们将其与（非线性）ICA的理论框架联系起来，并讨论对可识别模型的需求，改进的指标，和基准。往前走,我们提出了增强cryo-EM潜在空间解纠缠的未来方向，完善评估指标，探索利用基于物理的生物分子系统解码器的技术。此外，我们讨论了时间分辨单粒子成像的未来技术发展如何实现非线性ICA模型的应用，该模型可以发现自然界分子的真实构象变化。对可解释的构象潜在空间的追求将使研究人员能够解开复杂的生物过程并促进有针对性的干预。这对更广泛的药物发现和结构生物学具有重要意义。更一般地说,潜在变量模型被广泛部署在许多科学学科中。因此，如果我们想从令人印象深刻的非线性神经网络模型转向数学基础方法，可以帮助我们学习有关自然的新知识，那么我们在这项工作中提出的论点在AI科学中具有更广泛的应用。
