RaptorX: exploiting structure information for protein alignment by statistical inference
By Jian Peng, Jinbo Xu. Proteins: Structure, Function, and Bioinformatics, 2011
369 citations recorded by the source corpus.
This work presents RaptorX, a statistical method for template‐based protein modeling that improves alignment accuracy by exploiting structural information in a single or multiple templates. RaptorX consists of three major components: single‐template threading, alignment quality prediction, and multiple‐template threading. This work summarizes the methods used by RaptorX and presents its CASP9 result analysis, aiming to identify major bottlenecks with RaptorX and template‐based modeling and hopefully directions for further study. Our results show that template structural information helps a lot with both single‐template and multiple‐template protein threading especially when closely‐related templates are unavailable, and there is still large room for improvement in both alignment and template selection. The RaptorX web server is available at http://raptorx.uchicago.edu. Proteins 2011; © 2011 Wiley‐Liss, Inc.
Subfields: Materials Chemistry, Molecular Biology
View Paper: https://www.semanticscholar.org/paper/74d435ba702a0668b03399dab4d98e675bc0131f
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In Materials Chemistry
- Jinbo Xu
- Sheng Wang
- David Baker
- Hahnbeom Park
- Sergey Ovchinnikov
- David E. Kim
- Frank DiMaio
- Yang Zhang
In Molecular Biology
- Jinbo Xu
- Sheng Wang
- Yang Zhang
- David Baker
- Sergey Ovchinnikov
- Hahnbeom Park
- Torsten Schwede
- David E. Kim
Citations in the corpus
- In silico SARS-CoV-2 vaccine development for Omicron strain using reverse vaccinology (2022, background): “ 2022; Peng and Xu 2011).”
- Protein threading using residue co-variation and deep learning (2018, background): “…and correct template recognition, both of which are challenging when only distantly-related templates are available for a protein sequence under prediction (Cozzetto and Tramontano, 2004; Hou, et al., 2018; Jo, et al., 2015; Jones, 1997; Peng and Xu, 2011a; Peng and Xu, 2011b; Zhu, et al., 2017).”
- Automatic Prediction of Protein 3D Structures by Probabilistic Multi-template Homology Modeling (2015, methodology): “Current methods are therefore based on heuristics [23–25].”
- Continuous Automated Model EvaluatiOn (CAMEO) complementing the critical assessment of structure prediction in CASP12 (2018, methodology): “The top 3 servers participating both in CAMEO and CASP12 ranked by average lDDT are in identical order Robetta (Baker)[36] in the lead closely followed by RaptorX (Xu)[37–39] and IntFOLD4-TS (McGuffin)[40].”
- Automatic structure prediction of oligomeric assemblies using Robetta in CASP12 (2017, methodology): “server model by a GDT-TS of 4 (84 vs 80); (4) T0881, a trimer where FIGURE 2 Symmetric modeling of T0860 enables accurate modeling of both monomeric and multimeric configuration.”