The protein structure prediction problem could be solved using the current PDB library.

By Yang Zhang, Jeffrey Skolnick. Proceedings of the National Academy of Sciences of the United States of America, 2005

331 citations recorded by the source corpus.

Subfields: Materials Chemistry, Molecular Biology

View Paper: https://www.semanticscholar.org/paper/bb5efa1999d415880c87532b2748223b18d5c2d7

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. Jinbo Xu
4. Sheng Wang
5. Michael Feig
6. Chengxin Zhang
7. Sitao Wu
8. Yang Li

In Molecular Biology

1. Yang Zhang
2. Robin Pearce
3. Chengxin Zhang
4. Jinbo Xu
5. Sheng Wang
6. Yang Li
7. Wei Zheng
8. Michael Feig

Citations in the corpus

• Detecting distant-homology protein structures by aligning deep neural-network based contact maps (2019, background): “Thus, although there is evidence that the PDB library is complete and sufficient to solve the protein structure prediction problem [16], modeling of distantly-homologous proteins using TBM remains a major challenge in the field.”
• Protein threading using residue co-variation and deep learning (2018, background): “TBM is based upon the observation that many proteins share similar structures even if their sequences diverge (Kinch and Grishin, 2002; Zhang and Skolnick, 2005).”
• Deep-learning contact-map guided protein structure prediction in CASP13 (2019, result): “detect them, which is consistent with the former findings on the completeness of the PDB library.(55) One reason could be the poor quality”
• Physics Based Protein Structure Refinement through Multiple Molecular Dynamics Trajectories and Structure Averaging (2013, background): “6,9,12,13 The resulting models are often overall correct, but deviate from experimental structures in detail with typical root-mean-square deviations (RMSDs) of 2–6 A˚ due to intrinsic errors when constructing models based on template structures.”
• Automated Protein Structure Modeling with SWISS-MODEL Workspace and the Protein Model Portal (2012, background): “Regions of the protein, for which no template information is available, typically insertions and deletions in loop regions, are built by using libraries of backbone fragments (48) or by constraint space de novo reconstruction of these backbone segments (49).”
• TASSER: An automated method for the prediction of protein tertiary structures in CASP6 (2005, background): “19 For weakly/nonhomologous proteins, about two thirds of single-domain proteins could be folded.”
• All-atom ab initio folding of a diverse set of proteins. (2006, methodology): “The hydrogen-bonding of a donor-acceptor pair is properly taken into account by adapting the residue-based hydrogen-bonding model of Zhang and Skolnick (Zhang and Skolnick, 2004; Zhang and Skolnick, 2005) to the detailed all-atom model.”
• TM-align: a protein structure alignment algorithm based on the TM-score (2005, neutral): “…the 300 target proteins are equally taken from three categories of Easy, Medium, Hard sets, defined according to the score significance of template alignments by our fold-recognition program PROSPECTOR_3 (30) and approximately reflecting the difficulties of structure modeling of the targets.”
• Driven to Near-Experimental Accuracy by Refinement via Molecular Dynamics Simulations (2019, background): “it has become possible to predict protein structures with high accuracy.(2) Increased coverage of the protein fold space means that it is more likely that close homolog structures suitable for template-based”
• Protein Structure Prediction: Is It Useful? (2009, background): “Zhang and Skolnick [33] recently showed that using the best possible templates, almost all the single-domain proteins can be folded with an overall average RMSD of 2.”
• Ab initio modeling of small proteins by iterative TASSER simulations (2007, positive): “The force field was developed from TOUCHSTONE-II, with new potentials including the sequence-specified pair-wise potential [23], Cα distance correlations from sequencespecific fragments [20], and the more accurate secondary structure-specified hydrogen bonding [24].”
• Toward the solution of the protein structure prediction problem (2021, methodology): “%) if algorithms were able to identify the best templates from the PDB library (75).”
• Template-based structure modeling of protein-protein interactions (2013, result): “Recent data have shown that structural analogs can be found among the solved complex structures in the PDB for all monomer structures, a finding analogous to an earlier claim stating that the PDB library is nearly complete in the tertiary structure space [81,82]; this seems to suggest that the curre”
• Prediction of protein structure from ideal forms (2008, neutral): “3chy 128 8 16 (5) 3 16 128 1214 10 (8) 2trx 108 18 31 (4) 8 29 214 7319 36 (9) 1f4p 147 12 49 (18) 14 41 356 6656 50 (10) 1coz 126 9 39 (5) 26 49 272 3097 16 (5) 1di0 148 8 33 (3) 11 25 224 4411 32 (2)”