Excitation Energy Transfer in a Photosynthetic System with more than 100 Million Atoms

超过 1 亿个原子的光合作用系统中的激发能量转移

• 批准号: 466761712
• 负责人: Professor Dr. Ulrich Kleinekathöfer
• 金额: --
• 依托单位: Biodesign Institute
• 依托单位国家: 德国
• 项目类别: Research Grants
• 资助国家: 德国
• 项目状态: 未结题
• 来源: https://gepris.dfg.de/gepris/projekt/466761712?language=en
• 关键词: Excitation; Energy; Transfer; Photosynthetic; System

Light-harvesting protein-pigment complexes of plants, bacteria and algae are key players in the conversion of sunlight into stable forms of chemical energy during the process of photosynthesis. Chlorophyll, bacteriochlorophyll and bilin molecules are the main pigments present in those complexes which absorb solar light and pass it on to other pigments. Aim of those complexes is to transport the excitation energy to reaction centers where the charge separation takes place for further processing.The objective of this project is to enable the simulation of these excitation energy transport processes at an atomistic level in a full photosynthetic chromatophore vesicle from a purple bacterium with more than 130 million atoms. While the in-silico construction of this chromatophore has been a research topic by itself, we rely on this model and aim at carrying out quantum mechanics/molecular mechanics (QM/MM) simulations on this entire cell organelle along a molecular dynamics trajectory. Until now, it was only possible to carry out very preliminary QM/MM simulation studies on this extremely large model system due to the sheer size of the model with more than 2.000 pigments. If we were to use traditional simulation techniques from the field, accurately determining the excitation energies and their transport over a longer time period would be computationally unfeasible, even on large high performance computing installations.Therefore, we propose to enrich multi-scale QM/MM simulations, which are a state-of-the-art technology from computational biophysics, by multi-fidelity machine learning (ML) models. QM/MM combines the accuracy of QM calculations with the speed of MM simulations. Similarly, multi-fidelity ML models incorporate information from many, fast to compute low accuracy quantum chemical calculations with a few very accurate quantum chemical simulations. In our novel multi-scale multi-fidelity approach, the highly accurate but fast to obtain multi-fidelity ML models will replace the QM excitation energy calculations for the pigment molecules, which make up the vast majority of the computational runtime in the QM/MM simulation. Thereby, we expect to overcome the currently existing computational barrier.To actually carry out the full scale excitation energy transport simulation, fundamental advances have to be made in methods in computational biophysics and machine learning. A joint novel methodology of QM/MM simulation using multi-fidelity ML will be the first outcome of this project. The second outcome will be the complete simulation that will ultimately allow us to answer fundamental questions in biophysics such as: Do excitation energies depend on the local neighborhood of the pigment-protein complex? Is an energy funnel present which drives the excitation energies towards certain parts of the chromatophore? What are the relevant transfer times in the chromatophore between various parts of the system?

植物、细菌和藻类的捕光蛋白质-色素复合体是在光合作用过程中将阳光转化为稳定形式的化学能的关键参与者。叶绿素、细菌叶绿素和胆碱分子是存在于这些复合体中的主要色素，这些复合体吸收太阳光并将其传递给其他色素。这些络合物的目的是将激发能量传输到发生电荷分离的反应中心进行进一步处理。本项目的目标是能够在原子水平上模拟来自一种拥有超过1.3亿个原子的紫色细菌的全光合生色团囊泡中的这些激发能量传输过程。虽然这个发色团的电子结构本身就是一个研究课题，但我们依靠这个模型，沿着分子动力学轨迹对整个细胞细胞器进行量子力学/分子力学(QM/MM)模拟。到目前为止，由于模型的大小超过2.000种颜料，所以只能对这个非常大的模型系统进行非常初步的QM/MM模拟研究。如果我们使用传统的现场模拟技术，即使在大型高性能计算设备上，准确地确定激发能量及其在较长时间段内的传输在计算上也是不可行的，因此，我们建议用多保真机器学习(ML)模型来丰富计算生物物理学中的最新技术--多尺度QM/MM模拟。QM/MM结合了QM计算的准确性和MM模拟的速度。同样，多保真ML模型结合了来自多个、快速计算低精度量子化学计算和一些非常精确的量子化学模拟的信息。在我们的新的多尺度多保真方法中，高精度但快速获得多保真ML模型的方法将取代在QM/MM模拟中占据绝大部分计算时间的颜料分子的QM激发能计算。因此，我们希望克服目前存在的计算障碍。要真正进行全面的激发能量输运模拟，必须在计算生物物理和机器学习的方法方面取得根本性进展。一种基于多保真ML的QM/MM联合模拟方法将是该项目的第一个成果。第二个结果将是完整的模拟，最终将允许我们回答生物物理学中的基本问题，例如：激发能量是否依赖于色素-蛋白质复合体的局部邻域？是否存在一个能量漏斗，将激发能量驱动到着色团的某些部分？系统各部分之间在发色团中的相关转移时间是多少？