标题： PTM-Psi 在云端 显示英文标题

标题： PTM-Psi on the Cloud

摘要： 我们开发了一种先进的计算架构，利用Azure量子元素平台上的异步、松耦合工作流来加速蛋白质结构和相互作用的翻译后修饰（PTM-Psi）模拟。 云架构利用固有的任务并行性来提高模拟吞吐量，并动态分配计算资源以优化效率。 在此，我们无缝集成新兴的云计算资产，进一步扩展PTM-Psi模拟的范围和能力，超越当前的限制。 通过将现有的PTM-Psi工作流使用已建立的软件进行重构，包括Alphafold2、NWChem、GROMACS和基于Python的分析工具，形成一个云原生库，我们优化了资源分配，以满足每个工作流的需求，从而扩大PTM-Psi模拟的规模。 我们采用了一种“工作流流程”的方法，其中云架构支持对一种典型的蛋白质巨复合物上的硫醇PTMs的组合爆炸进行计算研究，这种蛋白质巨复合物对于蓝藻中依赖光的糖生产卡尔文-本森循环至关重要。 借助云服务的优势，我们通过PTM-Psi在云上将硫醇PTM分析的管道转换为高通量。 PTM-Psi在云上降低了操作复杂性，并降低了红氧化蛋白质组学质谱专家进行结构建模的数据解释门槛。 显示英文摘要

摘要： We developed an advanced computational architecture to accelerate Post-Translational Modifications on protein structures and interactions (PTM-Psi) simulations utilizing asynchronous, loosely coupled workflows on the Azure Quantum Elements platform. The cloud architecture harnessed inherent task parallelism to enhance simulation throughput and dynamically allocate computational resources to optimize efficiency. Here, we seamlessly integrate emerging cloud computing assets that further expand the scope and capability of PTM-Psi simulations beyond current limitations. By refactoring the existing PTM-Psi workflow using well-established software, including Alphafold2, NWChem, GROMACS, and Python-based analysis tools into a cloud-native library, we optimized resource allocation tailored to each workflow's needs for scaling up the PTM-Psi simulation. We employed a ``flow-of-workflow'' approach in which the cloud architecture supports the computational investigation of a combinatorial explosion of thiol PTMs on an exemplary protein megacomplex critical to the Calvin-Benson cycle of light-dependent sugar production in cyanobacteria. With PTM-Psi on the cloud, we transformed the pipeline for the thiol PTM analysis to achieve high throughput by leveraging the strengths of the cloud service. PTM-Psi on the cloud reduces operational complexity and lowers entry barriers to data interpretation with structural modeling for a redox proteomics mass spectrometry specialist.