Abstract
Assigning functional properties to a newly discovered protein is a key challenge in modern biology. To this end, computational modeling of the three-dimensional atomic arrangement of the amino acid chain is often crucial in determining the role of the protein in biological processes. We present a community-wide web-based protocol, RaptorX server (http://raptorx.uchicago.edu), for automated protein secondary structure prediction, template-based tertiary structure modeling, and probabilistic alignment sampling.
Given a target sequence, RaptorX server is able to detect even remotely related template sequences by means of a novel nonlinear context-specific alignment potential and probabilistic consistency algorithm. Using the protocol presented here it is thus possible to obtain high-quality structural models for many target protein sequences when only distantly related protein domains have experimentally solved structures. At present, RaptorX server can perform secondary and tertiary structure prediction of a 200 amino acid target sequence in approximately 30 min.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Aebersold R, Mann M (2003) Mass spectrometry-based proteomics. Nature 422(6928):198–207
Källberg M, Lu H (2010) An improved machine learning protocol for the identification of correct Sequest search results. BMC Bioinformatics 11:591
Bairoch A (2000) The ENZYME database in 2000. Nucleic Acids Res 28(1):304–305
Hannum G et al (2009) Genome-wide association data reveal a global map of genetic interactions among protein complexes. PLoS Genet 5(12):e1000782
Berman HM et al (2000) The protein data bank. Nucleic Acids Res 28(1):235–242
Baker D, Sali A (2001) Protein structure prediction and structural genomics. Science 294(5540):93–96
Peng J, Xu J (2011) RaptorX: exploiting structure information for protein alignment by statistical inference. Proteins 79(Suppl 10):161–171
Kallberg M et al (2012) Template-based protein structure modeling using the RaptorX web server. Nat Protoc 7(8):1511–1522
Peng J, Xu J (2010) Low-homology protein threading. Bioinformatics 26(12):i294–i300
Peng J, Xu J (2009) Boosting protein threading accuracy. Lect Notes Comput Sci 5541:31
Peng J, Xu J (2011) A multiple-template approach to protein threading. Proteins 79(6):1930–1939
Roy A, Kucukural A, Zhang Y (2010) I-TASSER: a unified platform for automated protein structure and function prediction. Nat Protoc 5(4):725–738
Peng J, Bo L, Xu J (2009) Conditional neural fields. In: Bengio Y, Schuurmans D, Lafferty J, Williams CKI, Culotta A (eds) Advances in neural information processing systems, vol 22. p 1419–1427
Singh R et al (2010) Struct2Net: a web service to predict protein–protein interactions using a structure-based approach. Nucleic Acids Res 38:W508–W515
Singh R, Xu J, Berger B (2006) Struct2net: integrating structure into protein–protein interaction prediction. Pac Symp Biocomput 2006:403–414
Acknowledgments
This work is supported by the National Institute of Health grant R01GM0897532, National Science Foundation DBI-0960390 and CAREER award, Alfred P. Sloan Fellowship, and TTIC summer intern program. The authors are grateful to the University of Chicago Beagle team, TeraGrid, and Canadian SHARCNet for their support of computational resources.
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2014 Springer Science+Business Media New York
About this protocol
Cite this protocol
Källberg, M., Margaryan, G., Wang, S., Ma, J., Xu, J. (2014). RaptorX server: A Resource for Template-Based Protein Structure Modeling. In: Kihara, D. (eds) Protein Structure Prediction. Methods in Molecular Biology, vol 1137. Humana Press, New York, NY. https://doi.org/10.1007/978-1-4939-0366-5_2
Download citation
DOI: https://doi.org/10.1007/978-1-4939-0366-5_2
Published:
Publisher Name: Humana Press, New York, NY
Print ISBN: 978-1-4939-0365-8
Online ISBN: 978-1-4939-0366-5
eBook Packages: Springer Protocols