Template-based protein structure modeling using the RaptorX web server

By Morten Källberg, Haipeng Wang, Sheng Wang, Jian Peng, Zhiyong Wang, Hui Lu, Jinbo Xu. 2012

1,602 citations recorded by the source corpus.

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.

Subfields: Materials Chemistry, Molecular Biology, Computational Biology & Bioinformatics

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Materials Chemistry

1. Sheng Wang
2. Jinbo Xu
3. Siqi Sun
4. Yizhou Yu
5. Z. Li
6. Renyu Zhang
7. Zhen Li
8. Shiwang Liu

In Molecular Biology

1. Jinbo Xu
2. Sheng Wang
3. Siqi Sun
4. Yizhou Yu
5. Z. Li
6. Shiwang Liu
7. Xin Gao
8. Renyu Zhang

In Computational Biology & Bioinformatics

1. Jinbo Xu
2. Sheng Wang
3. Siqi Sun
4. Yizhou Yu
5. Shiwang Liu
6. Zhen Li
7. Renyu Zhang
8. Z. Li

Citations in the corpus

• Accurate De Novo Prediction of Protein Contact Map by Ultra-Deep Learning Model (2016, methodology): “Nevertheless, the CAMEO participants include some well-known servers such as Robetta[34], Phyre[35], RaptorX[36], Swiss-Model[37] and HHpred[38].”
• Folding membrane proteins by deep transfer learning (2017, methodology): “However, the CAMEO participants include some well-known servers such as Robetta(Kim et al., 2004) and its test version Server19(Ovchinnikov et al., 2017), Phyre(Kelley et al., 2015), RaptorX(Källberg et al., 2012), Swiss-Model(Biasini et al., 2014) and HHpred(Remmert et al., 2012).”
• RaptorX-Property: a web server for protein structure property prediction (2016, background): “The underlying reason why we develop an independent structure property prediction server instead of merging it with our 3D structure prediction server (17,18) is that struc-”
• COACH-D: improved protein–ligand binding sites prediction with refined ligand-binding poses through molecular docking (2018, neutral): “2% higher than the second best method RaptorXBinding (22).”
• The Road to Metagenomics: From Microbiology to DNA Sequencing Technologies and Bioinformatics (2015, methodology): “To confirm a candidate for a novel gene, the appropriate strategy should include a de novo secondary and tertiary structure predictions using tools like I-TASSER (Yang et al., 2015), QUARK (Xu and Zhang, 2012), or RaptorX (Källberg et al., 2012) and perform a protein structure comparison using tools ”
• CoinFold: a web server for protein contact prediction and contact-assisted protein folding (2016, background): “The reason why we focus on hard targets for evaluating 3D model is due to the fact that template-based modeling may be better for easy and medium-level targets (27,28).”
• The Phyre2 web portal for protein modelling, prediction and analysis (2015, neutral): “However, this accuracy is only reached if there is a substantial number of diverse sequence homologs detectable in the sequence database (Steps 13–16).”