Critical assessment of methods of protein structure prediction—Round VIII

By John Moult, Krzysztof Fidelis, Andriy Kryshtafovych, Burkhard Rost, Anna Tramontano. 2009

168 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. Jianlin Cheng
3. Robin Pearce
4. Ben M. Webb
5. Björn Wallner
6. Chengxin Zhang
7. Jinbo Xu
8. Andrej Sali

In Molecular Biology

1. Yang Zhang
2. Jianlin Cheng
3. Robin Pearce
4. Jinbo Xu
5. David T. Jones
6. Sheng Wang
7. Debora Marks
8. Arne Elofsson

Citations in the corpus

• Protein 3D Structure Computed from Evolutionary Sequence Variation (2011, methodology): “The TM scores customary in CASP reflect these differences and it is plausible that the top-ranked predictions for 11 out of the 15 test proteins would be considered excellent for de novo modeled structures of this size (Table S1) [27,61,63].”
• NNcon: improved protein contact map prediction using 2D-recursive neural networks (2009, positive): “To fill the gap, we describe a fast, state-of-the-art neural network-based contact map predictor NNcon that was ranked among the best methods in the Eighth Critical Assessment of Techniques for Protein Structure Prediction (CASP8), 2008 (35).”
• Ab Initio Protein Structure Assembly Using Continuous Structure Fragments and Optimized Knowledge-based Force Field (2012, background): “5,14 The importance and difficulty of ab initio benchmarking were also reflected in the community-wide blind CASP experiments,19,20 where the portion of FM targets has been consistently decreasing owing to the difficulties in collecting individual protein domains which have different folds from the…”
• MEDELLER: homology-based coordinate generation for membrane proteins (2010, background): “Received on July 1, 2010; revised on September 21, 2010; accepted on September 25, 2010”
• Comparative Protein Structure Modeling Using MODELLER (2016, methodology): “The accuracy of the models calculated by many of these servers is evaluated by CAMEO (Haas et al., 2013) and the biannual CASP (Critical Assessment of Techniques for Proteins Structure Prediction; Moult, 2005; Moult et al., 2009) experiment.”
• Comparative Protein Structure Modeling Using MODELLER (2016, background): “Possible future improvements of modeling by rigid-body assembly include incorporation of rigid body shifts, such as the relative shifts in the packing of a helices and β-sheets (Nagarajaram et al., 1999).”
• The Protein Structure Initiative: achievements and visions for the future (2012, methodology): “…that can be used for testing new methods for protein structure prediction and automated data analysis, including data used in such projects as the Critical Assessment of Structure Prediction [19,20] and the Critical Assessment of Automated Structure Determination of Proteins from NMR data [21].”
• Improved model quality assessment using ProQ2 (2012, methodology): “For the development of ProQ2 we used data from CASP7 [31] for training and data from CASP8 [6] and CASP9 [7] for testing and benchmarking.”
• SELECTpro: effective protein model selection using a structure-based energy function resistant to BLUNDERs (2008, unknown): “We analyze the CASP7 quality assessment category predictions with a focus on the quality of the model ranked first by each predictor and the recovery of the most native-like model in the set.”
• 3D Protein Structure Predicted from Sequence (2011, unknown): “The accuracy of overall topography of the highest-­‐ranked structures is remarkable (Figure 3) and, as far as we know, currently not achievable for proteins of this size by any de novo structure predictio”
• SP5: Improving Protein Fold Recognition by Using Torsion Angle Profiles and Profile-Based Gap Penalty Model (2008, methodology): “We use CASP 7 targets [50] as an additional test set for SP5”
• Folding non-homologous proteins by coupling deep-learning contact maps with I-TASSER assembly simulations (2021, methodology): “The benchmark dataset consists of single-domain proteins collected from the SCOPe 2.06 database (Chandonia et al., 2018) and the FM and FM/TBM targets from CASP 8–12 (Moult et al., 2009, 2011, 2014, 2016, 2018).”