The Proteome Folding Project: proteome-scale prediction of structure and function

• Published in: Genome research Vol. 21; no. 11; pp. 1981 - 1994
• Main Authors: Drew, Kevin, Winters, Patrick, Butterfoss, Glenn L, Berstis, Viktors, Uplinger, Keith, Armstrong, Jonathan, Riffle, Michael, Schweighofer, Erik, Bovermann, Bill, Goodlett, David R, Davis, Trisha N, Shasha, Dennis, Malmström, Lars, Bonneau, Richard
• Format: Journal Article
• Language: English
• Published: United States Cold Spring Harbor Laboratory Press 01.11.2011
• Subjects: Animals; Arabidopsis; Chorismate Mutase - chemistry; Deinococcus - metabolism; Deinococcus - radiation effects; Drosophila Proteins - chemistry; Escherichia coli; Genome; Glucosyltransferases - chemistry; Humans; Mice; Molecular Sequence Annotation; Nuclear Proteins - chemistry; Nuclear Proteins - classification; Oryza sativa; Plasmodium vivax - metabolism; Protein Conformation; Protein Folding; Protein Structure, Tertiary; Proteome - chemistry; Protozoan Proteins - chemistry; Quality Control; Reproducibility of Results; Resource; Transglutaminases - chemistry; User-Computer Interface
• ISSN: 1088-9051; 1549-5469
• DOI: 10.1101/gr.121475.111

The incompleteness of proteome structure and function annotation is a critical problem for biologists and, in particular, severely limits interpretation of high-throughput and next-generation experiments. We have developed a proteome annotation pipeline based on structure prediction, where function and structure annotations are generated using an integration of sequence comparison, fold recognition, and grid-computing-enabled de novo structure prediction. We predict protein domain boundaries and three-dimensional (3D) structures for protein domains from 94 genomes (including human, Arabidopsis, rice, mouse, fly, yeast, Escherichia coli, and worm). De novo structure predictions were distributed on a grid of more than 1.5 million CPUs worldwide (World Community Grid). We generated significant numbers of new confident fold annotations (9% of domains that are otherwise unannotated in these genomes). We demonstrate that predicted structures can be combined with annotations from the Gene Ontology database to predict new and more specific molecular functions.