Solving protein structure from sparse serial microcrystal diffraction data at a storage-ring synchrotron source

Ti Yen Lan, Jennifer L. Wierman, Mark W. Tate, Hugh T. Philipp, Jose M. Martin-Garcia, Lan Zhu, David Kissick, Petra Fromme, Robert F. Fischetti, Wei Liu, Veit Elser, Sol M. Gruner

Research output: Contribution to journalArticlepeer-review

10 Scopus citations


In recent years, the success of serial femtosecond crystallography and the paucity of beamtime at X-ray free-electron lasers have motivated the development of serial microcrystallography experiments at storage-ring synchrotron sources. However, especially at storage-ring sources, if a crystal is too small it will have suffered significant radiation damage before diffracting a sufficient number of X-rays into Bragg peaks for peak-indexing software to determine the crystal orientation. As a consequence, the data frames of small crystals often cannot be indexed and are discarded. Introduced here is a method based on the expand-maximize-compress (EMC) algorithm to solve protein structures, specifically from data frames for which indexing methods fail because too few X-rays are diffracted into Bragg peaks. The method is demonstrated on a real serial microcrystallography data set whose signals are too weak to be indexed by conventional methods. In spite of the daunting background scatter from the sample-delivery medium, it was still possible to solve the protein structure at 2.1 Å resolution. The ability of the EMC algorithm to analyze weak data frames will help to reduce sample consumption. It will also allow serial microcrystallography to be performed with crystals that are otherwise too small to be feasibly analyzed at storage-ring sources.

Original languageEnglish (US)
Pages (from-to)548-558
Number of pages11
StatePublished - 2018


  • EMC algorithm
  • Protein microcrystallography
  • Sparse data
  • Storage-ring synchrotron sources
  • X-ray serial microcrystallography

ASJC Scopus subject areas

  • General Chemistry
  • Biochemistry
  • General Materials Science
  • Condensed Matter Physics


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