According to the scientists, traditional modeling methodsare limited in their ability to handle alternative molecular shapes and aresubject to multiple human errors. The process used to generate thesemodels—which involves using mathematical formulas or algorithms that are runsequentially, refining the structural details of the model with each separatealgorithm—is labor-intensive and requires constant error correction.
"With X-ray crystallography, we go through a mathematicalprocess where we shoot X-rays at a protein, and it will bounce off the protein,much like taking a picture," explains Kendall Nettles, a TSRI associateprofessor who led the study. "We make our protein into a crystalline state.When the X-rays hit it, it amplifies the signal in a way that we can getvisible spots on film. The intensity of each spot relates in a mathematicalfashion back to objects that cause the reflection. We get a grouping of spots,and can see where they are located. This may not be that accurate, and it mayhave errors associated with it. X-ray crystallography doesn't tell you whereall of the atoms are in a model. It gives you a rough cloud of where they arelocated. You have to look at it on the computer and try to pick out parts thatare not correct by eye. You generate the first map, then you nudge thingsaround to make it fit better."
By taking a closer look at this process, Nettles and hiscolleagues devised a new process called Extensive Combinatorial Refinement(ExCoR), which combines many of the existing X-ray crystallography formulasinto what they call "an algorithmic stew" to gain a better picture of molecularstructural diversity that is then used to eliminate errors and improve thefinal model. Their process, which they describe in a paper published last monthin the journal Structure, could helpimprove the development of drug candidates that depend to a great degree ondetailed structural analysis to determine how they work against specificdisease targets.
"Our big innovation was the result of a simplistic idea,"says Nettles. "We tried a bunch of people's different approaches in parallel tosee which one works best. It turned out that none of them worked best. If youdo an approach that makes it worse, that feeds the model into the nextapproach, and from there it works even better. We didn't have to write newsoftware or algorithms. All we did was combine everyone's methods in a randomway, and it produced a really big improvement—and did so in such a way that itworked better for us than if we never even looked at the model."
The TSRI team subjected more than 50 molecular structures to256 distinct combinations of algorithms and refinement factors that eventuallytotaled more than 12,000 independent refinement runs. Nettles and hiscolleagues measured the improvement in the models by what is known as the "R-factor,"which measures the similarity between the actual structure of the molecule andthe experimental model.
"Lowering that R-factor is the goal—that's the selectionprocess for finding the best algorithms," Nettles says.
The ExCoR strategy, which can be used to improve bothunrefined and refined crystal structures, revealed complex interactions amongrefinement algorithms. Structural diversity obtained via ExCoR facilitatedautomated error correction and provided an estimate of uncertainty of refined modelparameters.
The major advance of this method is that it will make thingsmore efficient," says Nettles. "A typical drug company might do hundreds ofthese a week, so this could be quite labor-intensive and time-consuming. Thismethod lets the researcher focus less on the process of getting there. It'skind of an art form that gets you to the end product faster."
The TSRI team, which was also comprised of researchers fromGenentech, the Lawrence Berkeley National Laboratory, the University ofCalifornia-Berkeley and the Los Alamos National Laboratory, is now working to"tweak the software package to answer some remaining questions we have," saysNettles.
The study, "Improved Crystallographic Structures usingExtensive Combinatorial Refinement," was published in Structure online ahead of print on Sept. 26. It will also appear inthe journal's Nov. 5 print issue. The study was supported by the U.S. NationalInstitutes of Health and the U.S. Department of Energy.
