Protein structure prediction and analysis using the Robetta server

By David E. Kim, Dylan Chivian, David Baker. 2004

2,037 citations recorded by the source corpus.

Subfields: 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 Molecular Biology

1. Yang Zhang
2. David Baker
3. Jinbo Xu
4. Jeffrey J. Gray
5. Sheng Wang
6. Tanja Kortemme
7. David T. Jones
8. Jens Meiler

In Computational Biology & Bioinformatics

1. Jinbo Xu
2. Yang Zhang
3. Sheng Wang
4. David Baker
5. David T. Jones
6. Sergey Ovchinnikov
7. Joe G. Greener
8. Badri Adhikari

Citations in the corpus

• Distance-based protein folding powered by deep learning (2018, neutral): “Table 1 summarizes folding accuracy of various methods on the 37 CASP12 hard targets, including our distance-based ab initio folding method, three contact-based ab initio folding methods (i.e., our own method, CCMpred and MetaPSICOV) and 4 top CASP12 groups (Baker-server 37 , Baker-human 38 , Zhang-”
• Accurate De Novo Prediction of Protein Contact Map by Ultra-Deep Learning Model (2016, methodology): “17), all the other CAMEO-participating servers, including Robetta, HHpred, RaptorX, SPARKS-X, and RBO Aleph (template-based and ab initio folding) only submitted models with TMscore≤0.48 and RMSD>43.82Å.”
• Folding membrane proteins by deep transfer learning (2017, methodology): “Meanwhile Robetta (Server19) employ both contact-assisted and fragment assembly-based ab initio folding and template-based modeling while the latter four mainly template-based modeling.”
• The RosettaDock server for local protein–protein docking (2008, methodology): “…were predicted correctly using techniques available on the web server [one in combination with the RosettaInterface mode available on Robetta (19)]; other targets pushed the boundaries of RosettaDock’s applicability in backbone flexibility and serve as precautions for server users to…”
• Automatic structure prediction of oligomeric assemblies using Robetta in CASP12 (2017, methodology): “For targets that our pipeline predicts as difficult ( target_difficulty 0.5), 13 or that our pipe-line predicts should be modeled as multi-domain targets, 10,15 the symmetry detection and modeling step is omitted.”
• HADDOCK versus HADDOCK: New features and performance of HADDOCK2.0 on the CAPRI targets (2007, background): “In particular, targets 19 and 22 illustrate the strength of our scoring function.”
• Changing the Apoptosis Pathway through Evolutionary Protein Design (2019, methodology): “For further confirmation, we also submitted the designed sequences to four other state-of-the-art structure prediction programs, including QUARK [27], Rosetta [28], RaptorX [29], and Phyre2 [30], which are built on ab initio and template-based modeling, respectively.”
• Improved Technologies Now Routinely Provide Protein NMR Structures Useful for Molecular Replacement (2011, methodology): “jsp) (Chivian et al., 2003; Kim et al., 2004) was used to generate fragment library, based on sequence and chemical shift data of each target protein.”