{"title":"AutoPD: an integrated meta-pipeline for high-throughput X-ray crystallography data processing and structure determination","authors":"Xin Zhang, Haikai Sun, Yu Hu, Zengru Li, Zhi Geng, Zengqiang Gao, Quan Hao, Fazhi Qi, Wei Ding","doi":"10.1107/S1600576725003218","DOIUrl":"https://doi.org/10.1107/S1600576725003218","url":null,"abstract":"<p>The advent of hybrid pixel array detectors and fully automated data acquisition workflows has revolutionized synchrotron light sources, enabling high-throughput collection of diffraction data from biological macromolecular crystals. However, these advancements have also created an urgent need for efficient and fully automated data processing pipelines. To address this challenge, we introduce <i>AutoPD</i>, an open-source high-throughput meta-pipeline for automated data processing and structure determination. Developed for the biological macromolecular crystallography beamline at the High Energy Photon Source in Beijing, <i>AutoPD</i> is also accessible to other academic and synchrotron users. By integrating cutting-edge parallel computing strategies, <i>AlphaFold</i>-assisted molecular replacement, a direct-method-based dual-space-iteration approach for model building, and an adaptive decision-making strategy that dynamically selects the optimal modeling pathway based on data quality and intermediate results, <i>AutoPD</i> streamlines the process from raw diffraction data and sequence files to high-precision structural models. When benchmarked against 186 recently deposited X-ray diffraction datasets from the Protein Data Bank, <i>AutoPD</i> successfully determined structures for 92% of cases, achieving map–model correlation values of at least 0.5 between density-modified electron density maps and the generated models. These results highlight the robustness and efficiency of <i>AutoPD</i> in addressing the challenges of modern structural biology, setting a new standard for automated structure determination.</p>","PeriodicalId":48737,"journal":{"name":"Journal of Applied Crystallography","volume":"58 3","pages":"746-758"},"PeriodicalIF":5.2,"publicationDate":"2025-05-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144207113","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"材料科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Absolute intensity normalization of powder neutron scattering data","authors":"Joseph A. M. Paddison","doi":"10.1107/S1600576725003632","DOIUrl":"https://doi.org/10.1107/S1600576725003632","url":null,"abstract":"<p>An important property of neutron diffraction data is that they can be normalized in absolute intensity units. In practice, however, such normalization is seldom performed, since it can be time consuming and subject to systematic uncertainties. Here, a straightforward approach is presented for absolute intensity normalization of neutron diffraction data from polycrystalline samples. This approach uses the intensity scale factor obtained from a Rietveld refinement to normalize the data to the nuclear Bragg profile of the sample. Factors to convert the Rietveld scale factor into an absolute normalization factor are tabulated for constant-wavelength and time-of-flight data refined using the popular programs <i>FullProf</i> and <i>GSAS-II</i>. An example of the application of this method to experimental data is presented. Advantages, disadvantages and extensions of this approach to spectroscopic data are discussed.</p>","PeriodicalId":48737,"journal":{"name":"Journal of Applied Crystallography","volume":"58 3","pages":"1022-1026"},"PeriodicalIF":5.2,"publicationDate":"2025-05-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144207108","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"材料科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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