Modeling GroEL

By Nancy Eaton

Professor Thomas E. Ferrin of the University of California at San Francisco discusses a 3D model of GroEL created with UCSF Chimera, a highly extensible, interactive molecular visualization and analysis system developed in Dr. Ferrin’s research laboratary.

What kind of structure is this, and why is this image valuable for scientific study?

This is a three-dimensional model of a “chaperonin” protein complex called GroEL. Chaperonins are protein complexes that assist the folding of other proteins into functional three-dimensional entities. The data used to create the model shown here was determined using both cryo-electron microscopy (EM) and X-ray crystallography techniques.

First, GroEL protein was isolated and crystallized, and an atomic model of the protein created. Since the GroEL complex shown in the image contains 14 copies of the monomeric crystal structure, the resulting detailed three-dimensional atomic model was then docked into the an electron microscope density map in order to create the overall model of the biologically active protein complex. Molecular complexes are of keen scientific interest in biology today, and hence the ability to visualize these complex entities is critical our understanding of their structure and function.

What does the colour-coding in this image represent?

Colour-coding, together with how parts of the model are rendered, helps us differentiate the various components of the GroEL complex. For example, each adjacent copy of the individual GroEL proteins is coloured differently to distinguish one copy from another. Twelve of the 14 copies of the protein are rendered as semi-transparent surfaces, the 13th copy is rendered as a “ribbon” in order to more clearly depict the secondary structural elements (helices and “beta sheets”) of the protein, and in the 14th copy, the individual atoms comprising the protein crystal structure (colour-coded by atom type and exclusive of hydrogen atoms) are rendered as spheres. Lastly, there’s a yellow mesh that represents a particular contour level within the electron microscope density map. The mesh allows you to see how well the individual copies of the monomeric crystal structure fit the EM density data.

What can you tell about this structure by looking at this image?

I can immediately see how the individual GroEL proteins are arranged to form two rings that comprise the biologically active complex. And although you can’t see it in the static image shown here, and especially when viewed as such a small image, the underlying EM and crystal data provide critical information about how the GroEL complex binds another molecule, adenosine triphosphate (ATP), and thus derives the energy necessary to drive the protein folding process.

How do scientists create images like this?

By using visualization software to analyze and create models based on experimental data. To create the model illustrated here, it was necessary to employ both electron microscopy and X-ray crystallography because neither technology alone can provide all the needed data. X-ray crystallography is a great technique for resolving detailed atomic-level information — the three-dimensional positions of all of the non-hydrogen atoms in the monomeric crystal structure of GroEL protein. But the same technique can’t currently be used for determining the structure of large complexes.

Cryo-electron microscopy, on the other hand, shows the relative orientation of one protein to another in the complex, but doesn’t provide enough resolution to determine the locations of individual atoms. Hence, the advantage of combining the two techniques — you take the detailed atomic level structures and then position them relative to one another using the electron microscope density map. So it’s combining the two experimental techniques that lead to our ability to create the type of model shown here.

Are there other ways scientists can communicate this same kind of data?

What’s shown here is a static image. But the real key to understanding the complicated spatial relationships found within biological complexes is not through viewing static images, but rather interactive visualizations. By relying on real-time three-dimensional computer graphics hardware, together with specialized scientific visualization software, researchers can build models that can be manipulated interactively using standard input devices such as a mouse or 3D joystick. It’s akin to holding the model in your hand, manipulating it, and viewing it from any desired angle. But more than this, you can control what parts of the model you see, how you see it (that is, how the model is rendered graphically), and much more. By using special stereo glasses, you can even view it all in stereo. And with a stereoscopic video projector and stereo glasses such as we have in my lab at UCSF, a group of people can visualize models like this and together comprehend and discuss the details of the underlying structures and their functions. It’s a fantastic way for scientists and students to learn about complex biological systems.

Of course, it goes without saying that the underlying data is numerical, so another obvious way to disseminate the data is via a large spreadsheet or XML-formatted data file. But this method is totally ineffective for anything other than simple data transfer. The old adage about a picture — in this case, an interactive 3D picture — being worth 100,000 words couldn’t be truer in structural biology.

Tell us a little bit about the visualization software you use.

UCSF Chimera is a highly extensible, interactive molecular visualization and analysis system that we developed in my research laboratory. It can read molecular structures and associated data in a large number of formats, display the structures in a variety of representations, and generate high-quality images and animations suitable for publication and presentation. In addition, Chimera provides tools to show density maps and analyze microscopy data; utilize symmetry information for the display of higher-order structures; display multiple sequence alignments, with crosstalk between the sequences and structures; and enable analysis of molecular dynamics trajectories and docking results.

Are there any special challenges in creating images of crystal structures versus other biological structures? How do you choose to best represent an image like this in order to communicate its importance most effectively?

The beauty of an interactive and feature-rich application like Chimera is that you can quickly try different approaches to visualizing your data, in this case complex biological structure data. What’s best for answering one scientific question is not necessarily best for another question. For example, if you’re interested in the overall organization of proteins within a complex, the image shown here is quite effective. But if you’re interested in why a particular protein within the complex forms multimeric units, then you’ll want to focus in on the protein-protein interfaces, fit the crystal structure of the protein, if available, to the electron density map as we did here, and try to determine just what amino acid residues within the protein are responsible for the underlying hydrogen binding. So it’s scientific curiosity and inquiry that drives the visualization of the data.

What advances in scientific visualization do you envision happening in the near future?

The technology is constantly evolving, so we’ll soon be able to get, for example, higher resolution electron microscopic data. Even now we can resolve some features on the surface of proteins in EM density maps, and hence can recognize secondary structural elements such as helices that lie near the protein’s surface.

And because computer performance is continuing to improve, we’ll soon have more analytical capabilities, meaning additional ways of looking at, filtering, and analyzing data, together with better ways of creating illustrative images, including animations. Through these new capabilities, we may soon be able to explore such things as dynamic processes in biological complexes.

For example, one of the most important yet least understood biological complex is the ribosome. It’s composed of a large number of proteins, there’s not a lot of symmetry associated with it, and it works dynamically, like a small engine running at an extremely small scale. Without the dynamic understanding, you’d miss the critical molecular interactions. And it’s the detail of these interactions that require analysis if we’re going to understand how the ribosome assembles proteins based on the genetic code.

Understanding complex biological systems is both fascinating and challenging, with great hope and potential for improving the world we live in and eliminating the scourge of disease. Key to our understanding of these systems will be the development of new scientific methods and the creation of models that can be interactively visualized and manipulated.