Machine learning platform for determining experimental lipid phase behaviour from small angle X-ray scattering patterns by pre-training on synthetic data
Machine learning platform for determining experimental lipid phase behaviour from small angle X-ray scattering patterns by pre-training on synthetic data
复制标题
机器学习平台，用于通过对合成数据进行预训练，从小角度 X 射线散射模式确定实验脂质相行为
DOI：
10.1039/d1dd00025j
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发表时间：
2022
期刊：
影响因子：
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通讯作者：
Abdel Aty H
中科院分区：
文献类型：
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作者：
Abdel Aty H
脂质膜在广泛的生物和生物技术系统中至关重要;它们从蛋白质活性的调节到药物摄取和递送发挥作用。了解脂质的结构，相互作用，自组装和相行为对于发展生物膜介导过程的分子理解，建立生物技术膜应用开发的工程方法至关重要。小角X射线散射（SAXS）是用于分析自组装脂质系统结构的事实上的方法。然而，由此产生的衍射图案非常难以自动分配，研究人员通常花费大量时间从光束线设施分析非原位图案，从而降低了实验能力和优化。此外，研究项目通常集中在特定的脂质成分上，因此将从一种可以快速优化一系列感兴趣的样品的方法中受益匪浅。我们提出了一个可通用的机器学习管道，能够根据原始的实验SAXS光谱对脂质相进行分类，准确度>99%，推理时间<60 ms，从而实现高通量现场分析。我们实现了这一目标，通过应用程序的合成数据生成系统，能够建立合成SAXS图案的基础物理决定相行为，我们还提出了一个扩展我们的系统，以合成生成共存相谱与已知的组成比。在此合成数据上预训练我们的机器学习模型，并对实验样本进行微调，使模型能够实现最先进的快速脂质相分类，使研究人员能够在需要时在现场调整他们的实验，从而大大加快高通量脂质研究。
Lipid membranes are vital in a wide range of biological and biotechnical systems; they undepin functions from modulation of protein activity to drug uptake and delivery. Understanding the structure, interactions, self-assembly and phase behaviour of lipids is critical to developing a molecular undertanding of biological membrane mediated processes, establishing engineering approaches to biotechnical membrane application development. Small Angle X-ray Scattering (SAXS) is the de facto method used to analyse the structure of self-assembled lipid systems. The resultant diffraction patterns are however extremely difficult to assign automatically with researchers spending considerable time often analysing patterns ex situ from a beamline facility, reducing experimental capacity and optimisation. Furthermore, research projects will often focus on particular lipid compositions and thus would benefit significantly from a method which can be rapidly optimised for a range of samples of interest. We present a generalisable machine learning pipeline that is able to classify lipid phases based on their raw, experimental SAXS spectra, with >99% accuracy and an inference time of <60 ms, enabling high throughput on-site analysis. We achieved this through application of a synthetic data generation system, capable of building synthetic SAXS patterns from the underlying physics which dictate phase behaviour, and we also propose an extension of our system to synthetically generate co-existence phase spectra with known composition ratios. Pre-training our machine learning model on this synthetic data, and fine-tuning on experimental samples empowers the model in achieving state-of-the-art, rapid lipid phase classification, allowing researchers to be able to adapt their experiments on site if needed and hence massively accelerate high throughput lipid research.