Structure prediction for CASP7 targets using extensive all‐atom refinement with Rosetta@home

By Rhiju Das, Bin Qian, Srivatsan Raman, Robert M. Vernon, James M. Thompson, Philip Bradley, Sagar D. Khare, Michael D. Tyka, Divya Bhat, Dylan Chivian and 7 more. 2007

222 citations recorded by the source corpus.

Subfields: Materials Chemistry, Molecular Biology, Radiology, Nuclear Medicine and Imaging

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. David Baker
2. Jeffrey J. Gray
3. Yang Zhang
4. Tanja Kortemme
5. Jens Meiler
6. Frank DiMaio
7. David E. Kim
8. Rhiju Das

In Molecular Biology

1. David Baker
2. Jeffrey J. Gray
3. Tanja Kortemme
4. Yang Zhang
5. Jens Meiler
6. Rhiju Das
7. Jinbo Xu
8. Frank DiMaio

In Radiology, Nuclear Medicine and Imaging

1. Brian D. Weitzner
2. Jeffrey J. Gray
3. Sarel Jacob Fleishman
4. Joël Janin
5. Tanja Kortemme
6. David Baker
7. Sergey Lyskov
8. Philip Bradley

Citations in the corpus

• Protein 3D Structure Computed from Evolutionary Sequence Variation (2011, methodology): “A series of ingenious methods have led to significant progress as reported in CASP since then, including threading, molecular dynamics, fragment-based assembly, contact prediction, machine learning, as well as methods combining several techniques [16,63,72,73].”
• Rosetta3: An Object-Oriented Software Suite for the Simulation and Design of Macromolecules (2011, positive): “Predict the structure of a protein from its sequence (Bonneau et al. , 2001, 2002; Bradley et al. , 2005; Das et al. , 2007; Raman et al. , 2009; Simons et al. , 1997) enzdes Design a protein active site to catalyze a chemical reaction ( Jiang et al. , 2008; Rothlisberger et al. , 2008; Zanghellini…”
• I-TASSER: a unified platform for automated protein structure and function prediction (2010, unknown): “have shown significant advantages of composite approaches in protein structure prediction, which combine various techniques such as threading, ab initio modeling and atomic-level structure refinement approache”
• I-TASSER: Fully automated protein structure prediction in CASP8 (2009, methodology): “The structure assembly simulations (for both the threading-aligned and the ab initio modeled regions) are guided by a unified knowledge-based force field, which includes three components: (1) general knowledge-based statistics terms from the PDB (Ca/side-chain correlations,37 H-bonds38 and hydrophob”
• 3D Protein Structure Predicted from Sequence (2011, unknown): “A series of ingenious methods have led to significant progress as reported in CASP since then, including threading, molecular dynamics, fragment-­‐based assembly, contact prediction, machine learning, as well as met”
• Practically Useful: What the Rosetta Protein Modeling Suite Can Do for You (2010, methodology): “The resulting models can undergo atomic-detail refinement, or if computational expense is an issue, clustering based on the CR root-mean-square deviation (rmsd) (16, 17) can reduce the number of models before performing refinement.”
• Structure prediction for CASP8 with all-atom refinement using Rosetta (2009, methodology): “Iterative rebuild-and-refine has been found to be particularly effective on targets with medium-to-low sequence similarity templates,21 since a single round of rebuild-and-refine is not likely to satisfactorily converge for such targets.”
• Homology Modeling a Fast Tool for Drug Discovery: Current Perspectives (2012, methodology): “The model refinement process will usually begin with an energy minimization step using one of the molecular mechanics force fields[55,56] and for further refinement, techniques such as molecular dynamics, Monte Carlo and genetic algorithm-based sampling can be applied[57,58].”
• CASP9 Assessment of Free Modeling Target Predictions (2011, methodology): “With these definitions, QCS simplifies the target and the model, respectively, into a set of SSE vectors and several key contacts, which allows direct comparison between the target and the model for the following four global features: (1) the correct prediction of SSE boundaries measured by the leng”
• Protein–protein interaction and quaternary structure (2008, background): “Energy refinement with an all-atom force field is computationally expensive, albeit well suited for distributed computing (Das et al. 2007).”
• Better together: Elements of successful scientific software development in a distributed collaborative community (2020, background): “through which volunteers can donate idle computer cycles for protein structure prediction, docking, and design[47,48].”
• Selectivity by Small-Molecule Inhibitors of Protein Interactions Can Be Driven by Protein Surface Fluctuations (2015, unknown): “66 Using pocket optimization to build ensembles of pocket-containing conformations To generate ensembles of pocket containing conformations, we use a Monte Carlo sampling technique implemented in the Rosetta software suite (56); this local move set was originally designed for refining templates in c”