Ab initio protein structure prediction of CASP III targets using ROSETTA
By K. Simons, Richard Bonneau, Ingo Ruczinski, David Baker. 1999
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The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
- Yang Zhang
- David Baker
- Robin Pearce
- Tanja Kortemme
- Hahnbeom Park
- Ben M. Webb
- Julia Koehler Leman
- Brian D. Weitzner
Citations in the corpus
- Protein 3D Structure Computed from Evolutionary Sequence Variation (2011, background): “libraries [22,23] or other strategies to search conformational space [24,25], followed by sophisticated energy optimization or”
- Improved Contact Predictions Using the Recognition of Protein Like Contact Patterns (2014, methodology): “In PconsFold, Rosetta [2] is used together with predicted contacts from PconsC (blue) or PconsC2 (red).”
- Scaffolding protein functional sites using deep learning (2022, background): “RF and AF have different architectures and were trained independently, and hence AF predictions can be regarded as a partially orthogonal in silico test of whether RF-designed sequences fold into the intended structures, analogous to traditional ab initio folding (13, 24).”
- Designing Peptides on a Quantum Computer (2019, background): “The latter class of algorithms, which include Rosetta’s AbinitoRelax ( 46 – 49 ) and simple_cycpep_predict applications ( 16 , 17 ) , sample tens of thousands to millions of alternative backbone conformations given a fixed sequence, relaxing and computing energies of each conformation sampled.”
- Constraint Logic Programming approach to protein structure prediction (2004, result): “We conclude the section comparing some results of our prediction with those returned by the well-known HMMSTR/Rosetta Prediction System [20].”
- Cyclic coordinate descent: A robotics algorithm for protein loop closure (2003, background): “Ab initio folding simulations have also made gains in recent years as the necessary computational resources have become cheaper and more plentiful (Simons et al. 1999).”
- Deep learning methods for designing proteins scaffolding functional sites (2021, methodology): “was inspired by AF, the two models were developed and trained independently, and hence AF predictions can be regarded as an orthogonal in silico test of whether RF designed sequences fold into the intended structures, analogous to traditional ab initio folding benchmarks (13, 18).”
- Better together: Elements of successful scientific software development in a distributed collaborative community (2020, methodology): “The protocol was successful in the blind prediction challenges, CASP 3[16] and 4[17].”
- Can correct protein models be identified? (2003, background): “These focus either on the identification of native, or native-like, protein models from a large set of decoys (Sippl 1990; Park and Levitt 1996; Park et al. 1997; Lazaridis and Karplus 1999; Gatchell et al. 2000; Petrey and Honig 2000; Vendruscolo et al. 2000; Vorobjev and Hermans 2001; Dominy and B”
- CONSTANS and the CCAAT Box Binding Complex Share a Functionally Important Domain and Interact to Regulate Flowering of Arabidopsis[W][OA] (2006, methodology): “Protein Structure Predictions Protein structures were ab initio predicted using the Rosetta program (Simons et al., 1997, 1999; Bonneau et al., 2001, 2002) and the resulting pdb-files analyzed using PYMOL.”
- Reconstruction of Protein Backbones from the BriX Collection of Canonical Protein Fragments (2008, methodology): “structure prediction algorithm Rosetta of Baker and co-workers [29–31], which also proved to be successful in accurately designing new folds [32].”
- Ab initio protein structure prediction: progress and prospects. (2001, methodology): “The majority of methods proving successful at CASP3 used secondary structure predictions in one way or another.”
- 3D Protein Structure Predicted from Sequence (2011, unknown): “to-‐structure problem remains unsolved, methods that use fragment libraries [20,21] or other strategies to search conformational space [22,23], followed by sophisticated energy optimization or molecular d”