TOUCHSTONE II: a new approach to ab initio protein structure prediction.

By Yang Zhang, Andrzej Kolinski, Jeffrey Skolnick. 2003

273 citations recorded by the source corpus.

Subfields: Materials Chemistry, Molecular Biology

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. Yang Zhang
2. Robin Pearce
3. Jianyi Yang
4. Chengxin Zhang
5. Dong Xu
6. Ambrish Roy
7. Sitao Wu
8. Yang Li

In Molecular Biology

1. Yang Zhang
2. Robin Pearce
3. Jianyi Yang
4. Chengxin Zhang
5. Yang Li
6. Wei Zheng
7. Eric W. Bell
8. Ambrish Roy

Citations in the corpus

• I-TASSER-MR: automated molecular replacement for distant-homology proteins using iterative fragment assembly and progressive sequence truncation (2017, methodology): “Continuous fragments are then excised from the threading aligned regions of the top-ranked templates, which are used to reassemble full-length models with the threading unaligned regions built by an on-and-off lattice-based folding procedure (16).”
• I-TASSER server: new development for protein structure and function predictions (2015, methodology): “The topology of the full-length models is then constructed by reassembling the continuously-aligned fragment structures excised from the templates, where the structures of the un-aligned regions are built from scratch by ab initio folding based on replica-exchange Monte Carlo simulations (21).”
• Toward optimal fragment generations for ab initio protein structure assembly (2012, background): “11,38 For the ab initio targets, however, there are generally no long-range distance/contact predictions due to the lack of homologous global templates.”
• TM-align: a protein structure alignment algorithm based on the TM-score (2005, neutral): “a structure different from that actually adopted by the sequence of interest) folds (34).”
• LOMETS: A local meta-threading-server for protein structure prediction (2007, background): “As demonstrated in our previous study (35), an accuracy of side-chain contact constraints of 4 22% has a positive effect on ab initio protein structure modeling.”
• MUSTER: Improving protein sequence profile–profile alignments by using multiple sources of structure information (2008, methodology): “Although we excluded the homologous templates using a sequence identity cutoff > 30% to the target in the above testing as many previous studies did,26,49,50,63 there are still some relatively ‘‘trivial’’ targets where homologous templates can be detected by PSI-BLAST.”
• De novo Protein Structure Prediction by Coupling Contact With Distance Profile (2020, background): “They are turned by maximizing the correlation between the energy of the conformations and the RMSD to the native structure over the training set of 37 proteins that are a 30 percent sequence identity cutoff to the test proteins [14], [61].”
• I-TASSER: a unified platform for automated protein structure and function prediction (2010, methodology): “Although both CM and threading methods use sequence profile and profile–profile alignments for identifying the templates, many ab initio modeling algorithms use evolutionary or knowledge-based information for collecting spatial restraint”
• 3DRobot: automated generation of diverse and well-packed protein structure decoys (2016, background): “The new energy terms introduced in 3DRobot improve the hydrogen-bonding network and compactness of decoys, which eliminates the possibility of native structure recognition by trivial potentials.”
• Protein Structure and Function Prediction Using I-TASSER (2015, methodology): “…reassembling the continuously aligned fragment structures excised from the LOMETS templates and super-secondary structure segments, whereby the structures of the unaligned regions are created from scratch by ab initio folding based on replica-exchange Monte Carlo simulations (Zhang et al., 2003).”
• Folding non-homologous proteins by coupling deep-learning contact maps with I-TASSER assembly simulations (2021, background): “The last six energy groups are classic I-TASSER force fields (Yang et al., 2015; Zhang et al., 2003) and the first energy group is the newly added deep learning-based contact energy potentials.”
• Automated protein structure modeling in CASP9 by I-TASSER pipeline combined with QUARK-based ab initio folding and FG-MD-based structure refinement (2011, unknown): “The I-TASSER force field includes four components: (1) general knowledge-based statistical terms from the PDB (C a /side-chain correlations, 37 hydrogen-bond, 38 and hydrophobicity39); (2) spatial restraints collected from threading templates7; (3) sequence-based C a /C b /side-chain center contact…”
• Using iterative fragment assembly and progressive sequence truncation to facilitate phasing and crystal structure determination of distantly related proteins. (2016, methodology): “…gaps in the query–template alignments) are then excised from the top-ranked templates and used to reassemble full-length models, whereby the structural regions whose sequences were not aligned by LOMETS (mainly loops) are built using a lattice-based ab initio folding procedure (Zhang et al., 2003).”