Protein-protein docking with simultaneous optimization of rigid-body displacement and side-chain conformations.
By Jeffrey J. Gray, Stewart E. Moughon, Chu Wang, O. Schueler‐Furman, Brian Kuhlman, Carol A. Rohl, David Baker. Journal of Molecular Biology, 2003
1,114 citations recorded by the source corpus.
Subfields: Computational Theory and Mathematics, Materials Chemistry, Molecular Biology
View Paper: https://www.semanticscholar.org/paper/f4e176d8682a625b65abfc226cfd58fb623a822d
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Computational Theory and Mathematics
- Shengyou Huang
- Alexandre M. J. J. Bonvin
- Xiaoqin Zou
- David Baker
- Jeffrey J. Gray
- Juan Fernández-Recio
- Brian G. Pierce
- Rhiju Das
In Materials Chemistry
- David Baker
- Jeffrey J. Gray
- Yang Zhang
- Tanja Kortemme
- Jens Meiler
- Hahnbeom Park
- Frank DiMaio
- Alexandre M. J. J. Bonvin
In Molecular Biology
- David Baker
- Jeffrey J. Gray
- Tanja Kortemme
- Yang Zhang
- Jens Meiler
- Rhiju Das
- Brian D. Weitzner
- Frank DiMaio
Citations in the corpus
- Flexible backbone assembly and refinement of symmetrical homomeric complexes (2018, background): “For the centroid score function environment and interacting residue pair terms, only the distance between backbone C α atoms of two interacting residues across the interface is considered (Gray et al., 2003).”
- Flexible protein-protein docking. (2006, positive): “In RosettaDock [33], after the initial low-resolution search, the sidechains are repacked and further optimized in an MC search that includes rigid-body displacements.”
- Dockground: A comprehensive data resource for modeling of protein complexes (2018, background): “Thus, an important part in developing intermolecular potentials and scoring functions is docking decoy sets (scoring benchmarks), where near-native matches are paired with incorrect docking predictions (decoys).(19,24-26) Comparative docking relies on target/template relationships based on sequence(”
- Practically Useful: What the Rosetta Protein Modeling Suite Can Do for You (2010, background): “for predicting the interaction of two proteins (49).”
- Protein Docking Model Evaluation by Graph Neural Networks (2020, background): “optimization (Moal and Bates, 2010), as well as considering protein flexibility (Gray et al., 2003; Oliwa and Shen, 2015).”
- Rosetta3: An Object-Oriented Software Suite for the Simulation and Design of Macromolecules (2011, positive): “…new sequence for an RNA molecule (Das et al. , 2010) that would have been virtually impossible in ROSETTA 2, including Python bindings for all ROSETTA classes (Chaudhury et al. , 2010) and an interactive game, FOLDIT , which challenges users to predict a protein’s structure (Cooper et al. , 2010).”
- Protein–protein interaction and quaternary structure (2008, methodology): “RosettaDock in CAPRI rounds 6–12.”
- Benchmarking and Analysis of Protein Docking Performance in Rosetta v3.2 (2011, methodology): “We first prepared each docking partner in isolation, optimizing their side-chain conformations prior to docking (‘‘pre-packing’’),[13] and then carried out local docking perturbations [13] using both RosettaDock v2.”
- PIPER: An FFT‐based protein docking program with pairwise potentials (2006, neutral): “6,7 Other approaches, primarily Monte Carlo, also perform well if the search can be restricted to regions of the conformational space, 8,9 but become computationally expensive if no such constraints are available.”
- De novo protein structure prediction by dynamic fragment assembly and conformational space annealing (2011, background): “33–36 Through-out this study, the weights of physics-based energy terms are kept fixed at 1.”