Integrating Diverse Data for Structure Determination of Macromolecular Assemblies

Author:

Alber Frank12,Förster Friedrich1,Korkin Dmitry13,Topf Maya4,Sali Andrej1

Affiliation:

1. Departments of Biopharmaceutical Sciences and Pharmaceutical Chemistry, and California Institute for Quantitative Biosciences, University of California at San Francisco, California 94158-2330;

2. Present addresses: Molecular and Computational Biology, Department of Biological Sciences, University of Southern California, Los Angeles, California 90089-2910;

3. Informatics Institute and Department of Computer Science, University of Missouri at Columbia, Missouri 65211;

4. School of Crystallography, Birkbeck College, University of London, London WC1E 7HX, United Kingdom;

Abstract

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.

Publisher

Annual Reviews

Subject

Biochemistry

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