Proteins (3D)
A new method can accurately predict the three-dimensional structures of proteins that were previously unknown.
Proteins are long chains made up of small building blocks called amino acids. These chains fold up in various ways to form the three-dimensional structures that proteins need to be able work properly. Therefore, to understand how a protein works, it is important to determine its structure, but this is very challenging. It is possible to predict the structure of a protein with high accuracy if previous experiments have revealed the structure of a similar protein. However, for almost half of all known families of proteins, there are currently no members whose structures have been solved.
The three-dimensional shape of a protein is determined by interactions between various amino acids. During evolution, the structure and activity of proteins often remain the same across species, even if the amino acid sequences have changed. This is because pairs of amino acids that interact with each other tend to ‘co-evolve’; that is, if one amino acid changes, then the second amino acid also changes in order to accommodate it. By identifying these pairs of co-evolving amino acids, it is possible to work out which amino acids are close to each other in the three-dimensional structure of the protein. This information can be used to generate a structural model of a protein using computational methods.
Now, Sergey Ovchinnikov and colleagues have developed a new method to predict the structures of proteins that combines data on the co-evolution of amino acids (as identified using a method called GREMLIN) with a structural prediction program known as Rosetta. A community-wide experiment called CASP — which tests different methods of protein prediction — showed that, in two cases, this new method works much better than anything previously used to predict the structures of proteins. Ovchinnikov and colleagues then used the new method to make models for proteins belonging to 58 different protein families with currently unknown structures.
These predictions were found to be highly accurate and the protein families each have thousands of members, so Ovchinnikov and colleagues’ findings are expected to be useful to researchers in a wide variety of research areas. A future challenge is to extend these methods to the many protein families that have hundreds rather than thousands of members.
To find out more
Read the eLife research paper on which this story is based: “Large-scale determination of previously unsolved protein structures using evolutionary information” (September 3, 2015).
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