Protein Structure Prediction Using Rosetta

Abstract

Publisher Summary This chapter elaborates protein structure prediction using Rosetta. Double-blind assessments of protein structure prediction methods have indicated that the Rosetta algorithm is perhaps the most successful current method for de novo protein structure prediction. In the Rosetta method, short fragments of known proteins are assembled by a Monte Carlo strategy to yield native-like protein conformations. Using only sequence information, successful Rosetta predictions yield models with typical accuracies of 3–6 A˚ Cα root mean square deviation (RMSD) from the experimentally determined structures for contiguous segments of 60 or more residues. For each structure prediction, many short simulations starting from different random seeds are carried out to generate an ensemble of decoy structures that have both favorable local interactions and protein-like global properties. This set is then clustered by structural similarity to identify the broadest free energy minima. The effectiveness of conformation modification operators for energy function optimization is also described in this chapter.