Progress and challenges in protein structure prediction.

By Yang Zhang. 2008

534 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. Jianlin Cheng
4. Jinbo Xu
5. Chengxin Zhang
6. Ben M. Webb
7. Renzhi Cao
8. Debswapna Bhattacharya

In Molecular Biology

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

Citations in the corpus

• Enhancing Evolutionary Couplings with Deep Convolutional Neural Networks (2017, background): “…methods are often not applicable to structure prediction tasks of interest, including orphan proteins, and thus for many novel proteins the field has turned to template-free, or de novo, folding approaches that predict 3D structures using the query sequence alone (Zhang, 2008; Xu and Zhang, 2012).”
• CASP10 results compared to those of previous CASP experiments (2013, neutral): “17 2.”
• Template-based structure modeling of protein-protein interactions (2013, methodology): “The first and probably the earliest strategy is the dimeric threading method [24], which is an extension of the single-chain threading (or fold-recognition) approach widely-used in tertiary protein structure prediction [14,16].”
• Deep learning techniques have significantly impacted protein structure prediction and protein design. (2021, background): “Since there are many more ways for a threading program to get incorrect alignments than to get a correct alignment [5], the consensus template selected from the meta-threading templates often has a higher accuracy on average than any of the individual threading programs.”
• Deep Spatio-Temporal Architectures and Learning for Protein Structure Prediction (2012, methodology): “To date, the more accurate and reliable computational methods for protein structure prediction are based on homology modeling [27].”
• Recognizing protein substructure similarity using segmental threading. (2010, background): “858 Structure 18, 858–867, July 14, 2010 a2010 Elsevier Ltd All right tion when close homologous templates are not available (Kopp et al., 2007; Wang et al., 2005; Zhang, 2008), i.”
• FragBag, an accurate representation of protein structure, retrieves structural neighbors from the entire PDB quickly and accurately (2010, background): “Recent Critical Assessment of Techniques for Protein Structure Prediction (CASP) experiments show that the most successful structure prediction methods construct their predictions from substructures that are not, in general, from proteins classified in the same fold [e.g. (3, 32, 33)].”
• ANGLOR: A Composite Machine-Learning Algorithm for Protein Backbone Torsion Angle Prediction (2008, background): “In the field of structural bioinformatics, the torsion angle prediction data have found their usefulness in aiding secondary protein structure prediction [4,5], sequence alignment [6], fold recognition [7,8] and protein structure modeling [9,10].”
• Toward the solution of the protein structure prediction problem (2021, background): “Since there are many more ways for threading to get incorrect alignments than to get a correct alignment, it is much easier to get a consensus correct alignment than multiple consistent but incorrect alignments (101).”
• 3DRobot: automated generation of diverse and well-packed protein structure decoys (2016, background): “…is the difficulty in designing accurate force fields that can recognize the native state as the lowest energy, and meanwhile possess an energy funnel with a medium-range energy-RMSD correlation that can guide the folding simulation towards the native state (Bradley et al. , 2005; Zhang, 2008).”
• Generating Triangulated Macromolecular Surfaces by Euclidean Distance Transform (2009, background): “Consequently, calculations of the macromolecular surfaces from given 3D structures have found extensive uses in modern molecular biology studies, including protein folding and structure prediction [2], protein-ligand docking [3,4], DNA-protein interactions [5], and new drug screening [6].”
• Large-scale model quality assessment for improving protein tertiary structure prediction (2015, methodology): “S: single-model method; M: multi-model method.”
• Amino acid coevolution reveals three-dimensional structure and functional domains of insect odorant receptors (2015, unknown): “Furthermore, because insect ORs have no significant similarity to sequences of known three-dimensional structure, homology modelling 22, 23 is not possible.”