Comparative Protein Structure Modeling Using MODELLER

By Ben M. Webb, Andrej Sali. 2016

5,498 citations recorded by the source corpus.

Comparative protein structure modeling predicts the three‐dimensional structure of a given protein sequence (target) based primarily on its alignment to one or more proteins of known structure (templates). The prediction process consists of fold assignment, target‐template alignment, model building, and model evaluation. This unit describes how to calculate comparative models using the program MODELLER and how to use the ModBase database of such models, and discusses all four steps of comparative modeling, frequently observed errors, and some applications. Modeling lactate dehydrogenase from Trichomonas vaginalis (TvLDH) is described as an example. The download and installation of the MODELLER software is also described. © 2016 by John Wiley & Sons, Inc.

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Citations in the corpus

Improving homology modeling from low-sequence identity templates in Rosetta: A case study in GPCRs (2020, neutral): “Modeller [18], except for GPCR-I-Tasser [11].”
Folding membrane proteins by deep transfer learning (2017, methodology): “To generate template-based models (TBMs) for test protein, we first run HHblits (with the UniProt20_2016 library) to generate an HMM file for the test protein, then run HHsearch with this HMM file to search for the best templates among the training proteins of our deep learning model, and finally ru”
UCSF ChimeraX: Tools for structure building and analysis (2023, methodology): “The Modeller Comparative tool in ChimeraX requires an atomic structure of a similar protein to use as a template, as well as a target-template sequence alignment, where the target is the sequence of the structure to predict.”
The Immune Epitope Database and Analysis Resource in Epitope Discovery and Synthetic Vaccine Design (2017, background): “Among them MODELLER is one of the prominent homology modeling suites that has gained vast popularity over the years (71).”
AI-based structure prediction empowers integrative structural analysis of human nuclear pores (2022, methodology): “The conformation of the linkers was further optimized usingModeller (90) and Isolde (91).”
A practical guide to large-scale docking (2021, methodology): “Common programs used for homology modeling include Modeller 52 , Rosetta 53 , ICM 27 and I-Tasser 54 .”
Deep learning extends de novo protein modelling coverage of genomes using iteratively predicted structural constraints (2018, methodology): “Webb B, Sali A. Comparative Protein Structure Modeling Using MODELLER.”
SE(3)-Equivariant Energy-based Models for End-to-End Protein Folding (2021, background): “This is almost sufﬁcient for protein folding, as there are several off-the-shelf tools [14, 20, 40] for determining side-chain conformations from backbone or C α -trace structures.”
Automatic structure prediction of oligomeric assemblies using Robetta in CASP12 (2017, methodology): “trimer where Robetta had a more accurate prediction than any other server, exceeding the next best GDT-TS by 2 (82 vs 80); (2) T0867, a relatively easy trimer comparative modeling case where 6 servers including Robetta had the top model with GDT-TS 98; (3) T0873, a tetramer, where the Robetta model ”
Predicting membrane protein contacts from non-membrane proteins by deep transfer learning (2017, methodology): “For each test MP, we run HHpred [42] to search for the 5 best templates among our training proteins and then construct 5 TBMs by MODELLER [43], which generates 3D structural models by mainly copying from templates.”
Modeling T cell receptor recognition of CD1-lipid and MR1-metabolite complexes (2014, unknown): “Prior to docking, missing unbound TCR CDR loop residues were added to PDB files 4LFH (test case 4LHU), 4MNH (test case 4MNG), 4G8E, and 4G8F using Modeller [33], keeping the remainder of the TCR fixed.”