Customised fragments libraries for protein structure prediction based on structural class annotations.
BACKGROUND
Since experimental techniques are time and cost consuming, in silico protein structure prediction is essential to produce conformations of protein targets. When homologous structures are not available, fragment-based protein structure prediction has become the approach of choice. However, it still has many issues including poor performance when targets' lengths are above 100 residues, excessive running times and sub-optimal energy functions. Taking advantage of the reliable performance of structural class prediction software, we propose to address some of the limitations of fragment-based methods by integrating structural constraints in their fragment selection process.


RESULTS
Using Rosetta, a state-of-the-art fragment-based protein structure prediction package, we evaluated our proposed pipeline on 70 former CASP targets containing up to 150 amino acids. Using either CATH or SCOP-based structural class annotations, enhancement of structure prediction performance is highly significant in terms of both GDT_TS (at least +2.6, p-values < 0.0005) and RMSD (-0.4, p-values < 0.005). Although CATH and SCOP classifications are different, they perform similarly. Moreover, proteins from all structural classes benefit from the proposed methodology. Further analysis also shows that methods relying on class-based fragments produce conformations which are more relevant to user and converge quicker towards the best model as estimated by GDT_TS (up to 10% in average). This substantiates our hypothesis that usage of structurally relevant templates conducts to not only reducing the size of the conformation space to be explored, but also focusing on a more relevant area.


CONCLUSIONS
Since our methodology produces models the quality of which is up to 7% higher in average than those generated by a standard fragment-based predictor, we believe it should be considered before conducting any fragment-based protein structure prediction. Despite such progress, ab initio prediction remains a challenging task, especially for proteins of average and large sizes. Apart from improving search strategies and energy functions, integration of additional constraints seems a promising route, especially if they can be accurately predicted from sequence alone.
Faculty of Science, Engineering and Computing, Kingston University, London, KT1 2EE, UK. k1064285@kingston.ac.uk.
Jad
J
Abbass
Faculty of Science, Engineering and Computing, Kingston University, London, KT1 2EE, UK. j.nebel@kingston.ac.uk.
Jean-Christophe
JC
Nebel
D000465:Algorithms; D003198:Computer Simulation; D030562:Databases, Protein; D006801:Humans; D010446:Peptide Fragments / Q000737:chemistry*; D019151:Peptide Library; D011506:Proteins / Q000737:chemistry* / Q000145:classification*; D012984:Software
D010446:Peptide Fragments; D019151:Peptide Library; D011506:Proteins