Integrating diverse data for structure determination of macromolecular assemblies

Frank Alber, Friedrich Förster, Dmitry Korkin, Maya Topf, Andrej Sali

Research output: Contribution to journalArticleAcademicpeer-review


To understand the cell, we need to determine the macromolecular assembly structures, which may consist of tens to hundreds of components. First, we review the varied experimental data that characterize the assemblies at several levels of resolution. We then describe computational methods for generating the structures using these data. To maximize completeness, resolution, accuracy, precision, and efficiency of the structure determination, a computational approach is required that uses spatial information from a variety of experimental methods. We propose such an approach, defined by its three main components: a hierarchical representation of the assembly, a scoring function consisting of spatial restraints derived from experimental data, and an optimization method that generates structures consistent with the data. This approach is illustrated by determining the configuration of the 456 proteins in the nuclear pore complex (NPC) from baker's yeast. With these tools, we are poised to integrate structural information gathered at multiple levels of the biological hierarchy--from atoms to cells--into a common framework.

Original languageEnglish
Pages (from-to)443-77
Number of pages35
JournalAnnual Review of Biochemistry
Publication statusPublished - 2008
Externally publishedYes


  • Animals
  • Biochemistry
  • Biophysics
  • Humans
  • Macromolecular Substances
  • Magnetic Resonance Spectroscopy
  • Mass Spectrometry
  • Microscopy, Electron
  • Models, Molecular
  • Molecular Conformation
  • Nuclear Pore
  • Reproducibility of Results
  • Saccharomyces cerevisiae
  • Scattering, Radiation
  • X-Rays


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