The DOI matches its Crossref record; 4 of the 4 references it cites were found in a registry or an index.
Template-based protein structure modeling using the RaptorX web server
Open accessAbstract
A key challenge of modern biology is to uncover the functional role of the protein entities that compose cellular proteomes. To this end, the availability of reliable three-dimensional atomic models of proteins is often crucial. This protocol presents a community-wide web-based method using RaptorX (http://raptorx.uchicago.edu/) for protein secondary structure prediction, template-based tertiary structure modeling, alignment quality assessment and sophisticated probabilistic alignment sampling. RaptorX distinguishes itself from other servers by the quality of the alignment between a target sequence and one or multiple distantly related template proteins (especially those with sparse sequence profiles) and by a novel nonlinear scoring function and a probabilistic-consistency algorithm. Consequently, RaptorX delivers high-quality structural models for many targets with only remote templates. At present, it takes RaptorX ∼35 min to finish processing a sequence of 200 amino acids. Since its official release in August 2011, RaptorX has processed ∼6,000 sequences submitted by ∼1,600 users from around the world.
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Cites
Mass spectrometry-based proteomics
Aebersold, R., & Mann, M. (2003) · Nature · doi:10.1038/nature01511 · Cited by 6,955
Comparative Protein Structure Modeling of Genes and Genomes
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The Pfam protein families database
Finn, R. D., Mistry, J., Tate, J., Coggill, P., Heger, A., Pollington, J. E., Gavin, O. L., Gunasekaran, P., Ceric, G., Forslund, K., et al. (2010) · Nucleic Acids Research · doi:10.1093/nar/gkp985 · Cited by 2,694
A new approach to protein fold recognition
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Cited by
The Phyre2 web portal for protein modeling, prediction and analysis
Kelley, L. A., Mezulis, S., Yates, C. M., Wass, M. N., & Sternberg, M. J. E. (2015) · Nature Protocols · doi:10.1038/nprot.2015.053 · Cited by 9,652
Accurate De Novo Prediction of Protein Contact Map by Ultra-Deep Learning Model
Wang, S., Sun, S., Li, Z., Zhang, R., & Xu, J. (2017) · PLOS Computational Biology · doi:10.1371/journal.pcbi.1005324 · Cited by 931
Self-citationComparative protein structure modeling using MODELLER
Webb, B., & Sali, A. (2016) · Current Protocols in Protein Science · doi:10.1002/cpps.20 · Cited by 730
Widespread macromolecular interaction perturbations in human genetic disorders
Sahni, N., Yi, S., Taipale, M., Fuxman Bass, J. I., Coulombe-Huntington, J., Yang, F., Peng, J., Weile, J., Karras, G. I., Wang, Y., et al. (2015) · Cell · doi:10.1016/j.cell.2015.04.013 · Cited by 594
Self-citationZanoni, P., Khetarpal, S. A., Larach, D. B., Hancock-Cerutti, W. F., Millar, J. S., Cuchel, M., DerOhannessian, S., Kontush, A., Surendran, P., Saleheen, D., et al. (2016) · Science · doi:10.1126/science.aad3517 · Cited by 545
Metadata
- Authors
- Källberg, M., Wang, H., Wang, S., Peng, J., Wang, Z., Lu, H., & Xu, J.
- Venue
- Nature Protocols
- Published
- 2012-07-19
- Volume
- 7
- Issue
- 8
- Pages
- 1511-1522
- ISSN
- 1750-2799 (ISSN-L)
- 1754-2189
- OpenAlex
- W1981264193
- Cited by
- 1,597
- References
- 4
- FWCI
- 20.45
- Type
- journal article
- Language
- en
- Open access
- green
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