Hidden molecular structures in proteins revealed

Improved X-ray diffraction software uses statistical methods
December 23, 2014

A membrane protein called cysZ, imaged in three dimensions with Phenix software, using data that could not previously be analyzed (credit: Los Alamos National Laboratory)

Los Alamos National Lab scientists have developed statistical methods that allow for creating three-dimensional pictures of previously hidden molecular structures in proteins.

To view the proteins, researchers produce billions of copies, dissolve them in water, and grow crystals of the protein, then shine a beam of X-rays and measure the brightness of each of the thousands of diffracted X-ray spots that are produced.

Then researchers use the powerful Phenix software, developed by scientists at Los Alamos, Lawrence Berkeley National Laboratory, Duke and Cambridge universities, to analyze the diffraction spots and produce a three-dimensional picture of a single protein machine that tells the researchers how the protein machine is put together, allowing for pharmaceutical companies and researchers to see the detailed inner workings of complex protein molecules.

A 3D advance

Recently Los Alamos scientists worked with their colleagues at LBNL and Cambridge University to make it even easier to visualize a molecular machine. In a report in the journal Nature Methods this month, Los Alamos scientists and their team show that they can obtain three-dimensional pictures of molecular machines using X-ray diffraction spots that could not previously be analyzed.

Some molecular machines contain a few metal atoms or other atoms that diffract X-rays differently than the carbon, oxygen, nitrogen, and hydrogen atoms that make up most of the atoms in a protein. The Phenix software finds those metal atoms first, and then uses their locations to find all the other atoms. For most molecular machines, however, metal atoms have to be incorporated into the machine artificially to make this all work.

The major new development to which Los Alamos scientists have contributed was showing that powerful statistical methods could be applied to find metal atoms even if they do not scatter X-rays very differently than all the other atoms. Even metal atoms such as sulfur that are naturally part of almost all proteins can be found and used to generate a three-dimensional picture of a protein.

X-ray crystal structure of Cascade (credit: Ryan N. Jackson et al./Science)

Molecular machines that have recently been seen in three-dimensional detail include a huge molecular machine called Cascade that was reported in the journal Science this summer. The Cascade machine is present in bacteria and can recognize DNA that comes from viruses that infect the bacteria.

The Cascade machine is made up of 11 proteins and an RNA molecule and looks like a seahorse, with the RNA molecule winding through the whole “body” of the seahorse. If a foreign piece of DNA in the bacterial cell is complementary to part of the RNA molecule, another specialized machine can come by and chop up the foreign DNA, saving the bacterium from infection.

Los Alamos and Cambridge University scientists who developed the Phenix software were part of the team that visualized this protein machine for the first time.

The Phenix software has been used to determine the three-dimensional shapes of over 15,000 different protein machines and has been cited by over 5000 scientific publications.

Abstract of Macromolecular X-ray structure determination using weak, single-wavelength anomalous data

We describe a likelihood-based method for determining the substructure of anomalously scattering atoms in macromolecular crystals that allows successful structure determination by single-wavelength anomalous diffraction (SAD) X-ray analysis with weak anomalous signal. With the use of partial models and electron density maps in searches for anomalously scattering atoms, testing of alternative values of parameters and parallelized automated model-building, this method has the potential to extend the applicability of the SAD method in challenging cases.