Riddles in Protein Molecular Structure Solved with New Application

The lead authors were Dr. Frank DiMaio of the University of Washington (UW; Seattle, USA) and Dr. Thomas C. Terwilliger of Los Alamos [US] National Laboratory (NM, USA). The senior author was Dr. David Baker, from the UW department of biochemistry.

A protein's molecular structure shapes its functions. In biomedical and health research, for example, scientists are interested in the molecular structure of specific proteins for many reasons, a few of which are (1) to design drugs that selectively target, at the molecular level, specific biochemical reactions in the body; (2) to understand abnormal human proteins in disease, and how these abnormal proteins cause malfunctions; (3) to learn the shape and function of virus particles and how they act to cause infections; (4) to see how the chains of amino acids, decoded from the DNA in genes, fold and twist into normally or abnormally shaped protein molecules; (5) to design new proteins not found in the nature; (6) to find ways to replace malfunctioning molecular parts of proteins that are vital to health; (7) lastly, to devise nanoscale tools.

"The important new method described…in Nature highlights the value of computational modeling in helping scientists to determine the structures and functions of molecules that are difficult to study using current techniques,” said Dr. Peter Preusch, who oversees Dr. Baker's research grant and other structural biology grants at the US National Institutes of Health (NIH; Bethesda, MD, USA). "Expanding the repertoire of known protein structures--a key goal of the NIH Protein Structure Initiative, which helped fund the research--will be of great benefit to scientists striving to design new therapeutic agents to treat disease.”

The methods devised by the group overcome some of the limitations of X-ray crystallography in determining the molecular structure of a protein. X-ray crystallography obtains data about the positions of atoms, chemical bonds, the density of electrons and other arrangements within a protein molecule.

The information is gleaned by striking protein crystals with X-ray beams, which bounce off in several directions. Measuring the angles and intensities of these diffracted beams enables scientists to generate a three-dimensional (3D) image of electron density. However, information about the molecular structure can be lost in taking the measurements, due to restraints posed by physics.

Scientists attempt to avoid this problem by comparing the crystallography results to previously solved protein structures that resemble the unknown structure. The technique to "fill in the blanks” is called molecular replacement. Molecular replacement has its own limitations in understanding the electron density maps produced by X-ray crystallography, according to the authors of the paper. Techniques such as automatic chain tracing frequently follow the comparative model more closely than the actual structure of the protein under question. These errors lead to failure to obtain an accurate configuration of the molecule.

The researchers demonstrated that this limitation could be considerably reduced by combining computer algorithms for protein structure modeling with those for determining structure via X-ray crystallography.

Several years ago, University of Washington researchers and their colleagues developed a structure prediction method called Rosetta. This program takes a chain of amino acids and searches for the lowest energy conformation possible from folding, twisting, and packing the chain into a 3D molecule.

The researchers discovered that even very poor electron density maps from molecular replacement solutions could be useful. These maps could guide Rosetta structural prediction searches that are based on energy optimization. By taking these energy-optimized predicted models, and searching for consistency with the electron density data contained in the X-ray crystallography, new maps were generated. The new maps were then subjected to automatic chain tracing to generate 3D models of the protein molecular structure. The models were checked with an advanced monitoring technique to see if any are successful.

To evaluate the performance of their new integrated method, the researchers looked at 13 sets of X-ray crystallography data on molecules whose structures could not be solved by expert crystallographers. These structures remained unsolved even after the application of an extensive array of other approaches. The new integrated method was able to yield high-resolution structures for eight of these 13 highly challenging models.

"The results show that structural prediction methods such as Rosetta can be even more powerful when combined with X-ray crystallography data,” the researchers noted. They added that the integrated approach likely outperforms others because it provides physical chemistry and protein structural information that can guide the massive sampling of candidate configurations. These data eliminate most conformations that are not physically possible.

These procedures, the authors noted, required significant computation, as up to several thousand Rosetta model predictions are generated for each structure. The researchers have developed automated procedures that potentially could taper down the possibilities and lessen the number of times a model is rebuilt to make corrections. This automation could reduce computing time.

Through Dr. Baker's laboratory, many members of the general public contribute their unused home computer time to help in the effort to obtain structural models of proteins that are biologically and medically significant. The scientific discovery game is called "Fold It,” which can be accessed online (please see Related Links below).

Related Links:
University of Washington
Los Alamos US National Laboratory
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