Protein structure prediction
||Protein structure prediction|
||Nanjiang Shu <email@example.com>|
||2014-03-03 – 2015-04-01|
||10203 10601 30199|
The structure of a protein is essential for understanding its function. Today, next generation sequencing techniques have identified millions of protein sequences. To study all of these experimentally would be impossible, thus the only way to obtain structural information of these proteins will be through computational methods. Such protein structure prediction methods include many different techniques, extending from a detailed prediction of the structural change of a single amino acid substation, over to the detection of remote homology between proteins, to the prediction of protein structure de novo.
The development of improved methods for protein structure prediction methods and the use of such methods has been an active research field for decades.
The Elofsson group and Wallner group have been involved in the development of state of art methods for protein structure predictions for some time. Some of the methods they developed have had a significant impact in the field, as can be seen from the number of citations received. In particular the Pcons (Lundström et al., 2001) method have continuously proven to be one of the best methods at the bi-annual CASP assessment in protein structure predictions (Wallner & Elofsson, 2007; Larsson et al., 2009. In addition ProQ (Wallner & Elofsson, 2003, 2006; Ray et al., 2012) have proven to be the best methods to evaluate the quality of an individual protein models. Finally the Elofsson group have for more than a decade been leading the development of prediction servers for membrane proteins both of the α-helical and β-sheet classes (Viklund & Elofsson, 2004, 2008; Hayat & Elofsson 2012).
I have recently been employed by BILS (Bioinformatics Services to Swedish Life Science) to develop a web-service infrastructure with a focus on protein structure prediction. As a part of this goal we need to test and optimize various protein structure prediction methods, including the ones developed in the group of Arne Elofsson and Björn Wallner. The benchmarking is a time-consuming but necessary step to be able to provide the best possible solution for the scientific community.