Tools for comparative protein structure modeling and analysis

By Narayanan Eswar, Bino John, Nebojsa Mirkovic, András Fiser, V. Ilyin, Ursula Pieper, A. C. Stuart, Marc A. Martí-Renom, Mallur Srivatsan Madhusudhan, Bozidar Yerkovich and 1 more. 2003

510 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. Andrej Sali
2. Ben M. Webb
3. Min-Yi Shen
4. David Eramian
5. Narayanan Eswar
6. Jinbo Xu
7. Ursula Pieper
8. Mallur Srivatsan Madhusudhan

In Molecular Biology

1. Andrej Sali
2. Ben M. Webb
3. Min-Yi Shen
4. Jinbo Xu
5. David Eramian
6. Narayanan Eswar
7. Ursula Pieper
8. Mallur Srivatsan Madhusudhan

Citations in the corpus

• Distance-based protein folding powered by deep learning (2018, methodology): “CNFpred integrates sequence profile, secondary structure, and solvent accessibility via a machine learning approach to build sequence–template alignments, which are then fed into MODELER (38) to build 3D models.”
• Comparative Protein Structure Modeling Using Modeller (2006, methodology): “The second way of improving the alignment is to iteratively modify those regions in the alignment that correspond to predicted errors in the model (Sanchez and Sali, 1997a; Sanchez and Sali, 1997b; John and Sali, 2003).”
• Comparative Protein Structure Modeling Using MODELLER (2007, background): “Side-chain errors are critical if they occur in regions that are involved in protein function, such as active sites and ligand-binding sites.”
• Interactome3D: adding structural details to protein networks (2013, unknown): “58).”
• Strategies and molecular tools to fight antimicrobial resistance: resistome, transcriptome, and antimicrobial peptides (2013, methodology): “The information modeler generated is also combined with the statistical calculation preferences of constraints derived from the sequence template homology (Eswar et al., 2003; Vyas et al., 2012).”
• Comparative Protein Structure Modeling Using MODELLER (2016, methodology): “If a sequence does not yet have a model in ModBase, the ModWeb (http://salilab.org/ modweb/; Eswar et al., 2003) Web server can be used to model it.”
• Alignment of protein sequences by their profiles (2004, methodology): “Currently, SALIGN is used as a module in ModPipe, our software pipeline for large-scale modeling of all available protein sequences (Bairoch and Apweiler 2000) that are detectably related to at least one known protein structure (Eswar et al. 2003).”
• MODBASE: a database of annotated comparative protein structure models and associated resources (2005, methodology): “ModPipe uses sequence–sequence (22), sequence–profile (19,23) and profile–profile (5,24) methods for fold assignment and target–template alignment, using a promiscuous E-value threshold of 1.0 to increase the likelihood of identifying the best available template structure.”
• ModBase, a database of annotated comparative protein structure models and associated resources (2013, methodology): “Models in MODBASE are calculated using MODPIPE, our entirely automated software pipeline for comparative modeling ( 16 ).”
• How well can the accuracy of comparative protein structure models be predicted? (2008, methodology): “These three scores were developed and implemented as described elsewhere (Melo et al. 2002; Eswar et al. 2003).”
• Comparative Protein Structure Modeling Using MODELLER (2016, methodology): “These scores include a normalized (z-score) version of DOPE as above; the GA341 score (Melo and Sali, 2007); ModPipe’s own quality score (MPQS; Pieper et al., 2011), which is a linear combination of DOPE, GA341, and other scores; and a prediction from TSVMod (Eramian et al., 2008) of the C α root-me”