Protein structure modeling with MODELLER.

By Ben M. Webb, Andrej Sali. 2014

1,751 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. Jinbo Xu
2. Jeffrey J. Gray
3. Sheng Wang
4. Yang Zhang
5. Jianlin Cheng
6. David Baker
7. Shengyou Huang
8. Yumeng Yan

In Molecular Biology

1. Yang Zhang
2. Jinbo Xu
3. Sheng Wang
4. Jeffrey J. Gray
5. Badri Adhikari
6. Ivan V. Anishchenko
7. David Baker
8. Shengyou Huang

Citations in the corpus

• DeepSF: deep convolutional neural network for mapping protein sequences to folds (2017, background): “Each alignment and its corresponding template structure are fed into Modeller (Webb and Sali, 2014) to build the tertiary structures.”
• Protein threading using residue co-variation and deep learning (2018, methodology): “We align each query protein to the template database PDB40 and select top sequence-template alignments by alignment score and build corresponding 3D models by MODELLER (Webb and Sali, 2014).”
• Enhancing structure prediction and design of soluble and membrane proteins with explicit solvent-protein interactions (2017, methodology): “Modeller predictions: Modeller 9.17 is a premier protein modeling software for performing homology or comparative modeling to produce atomic-resolution model of target proteins from their amino acid sequences and template protein structures.”
• How Pairwise Coevolutionary Models Capture the Collective Residue Variability in Proteins? (2018, background): “-Marie-Curie Paris 06, CNRS, Biologie Computationnelle et Quantitative – Institut de Biologie Paris Seine, 75005 Paris, France 1 arXiv:1801.04184v1 [q-bio.QM] 12 Jan 2018 protein-structure prediction [2, 3]. However, beyond the transfer of information, the variability of sequences across homologs it”
• Lysine Glutarylation Is a Protein Post-Translational Modification Regulated by SIRT5 (2014, methodology): “A human CPS1 crystal structure has not been solved, and therefore we built a homology model of CPS1 with the MODELER protocol (Eswar et al., 2008) using the crystal structure of E. coli carbamoylphosphate synthetase (Thodenetal., 1999) (Figure6H).”
• Protein tertiary structure modeling driven by deep learning and contact distance prediction in CASP13 (2019, methodology): “40 The alignments and the structures of templates were fed into Modeller 41 to build the structural models for the target.”
• A structural model of the active ribosome-bound membrane protein insertase YidC (2014, methodology): “Next, we used MODELLER (Eswar et al., 2008) to create full length models based on the TM core, secondary structure prediction and the 50 residue–residue contacts with the highest coupling coefficients (39 excluding intrahelical contacts, indels and topology violations).”
• In silico methods and tools for drug discovery (2021, methodology): “Diverse tools and online resources for homology modeling are available; these tools include MODELER [136], SwissModel [137], Mod web [138], and Phyre2 [139].”
• GPCRM: a homology modeling web service with triple membrane-fitted quality assessment of GPCR models (2018, methodology): “SA in the PIR file format is subjected to Modeller (28).”
• NMRbox: A Resource for Biomolecular NMR Computation. (2017, background): “2), a metapackage for determining protein structures from unassigned 13C chemical shifts, found that new installations of their complex software (uti- lizing NMRPipe (17), ROSETTA (18), MODELER (19), SPARKY (20), and SHIFTX2 (21)) to be difficult, even within their own laboratory.”
• Prediction of protein assemblies, the next frontier: The CASP14-CAPRI experiment (2021, methodology): “A number or groups successfully exploited homology models generated by the best performing CASP14 servers, made available during the prediction Rounds, or used publicly available tools such as Modeler.(51) Successful approaches involved searching a database of known structures, clustered on the basi”
• Prediction of homo- and hetero-protein complexes by protein docking and template-based modeling: a CASP-CAPRI experiment (2016, neutral): “…of native and f(non-nat) of non-native contacts in the predicted interface; the root mean square displacement (rmsd) of the backbone atoms of the ligand ( L-rms ), the mis-orientation angle h L and the residual displacement d L of the ligand center of mass, after the receptor in the model and…”