RosettaRemodel: A Generalized Framework for Flexible Backbone Protein Design

By Po-Ssu Huang, Y. Ban, Florian Richter, Ingemar André, Robert M. Vernon, William R. Schief, David Baker. 2011

376 citations recorded by the source corpus.

We describe RosettaRemodel, a generalized framework for flexible protein design that provides a versatile and convenient interface to the Rosetta modeling suite. RosettaRemodel employs a unified interface, called a blueprint, which allows detailed control over many aspects of flexible backbone protein design calculations. RosettaRemodel allows the construction and elaboration of customized protocols for a wide range of design problems ranging from loop insertion and deletion, disulfide engineering, domain assembly, loop remodeling, motif grafting, symmetrical units, to de novo structure modeling.

Subfields: Molecular Biology

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Citations in the corpus

Protein sequence design with a learned potential (2022, methodology): “The Rosetta-FixBB baseline uses the Rosetta packer 55 , invoked via the RosettaRemodel 57 application, to perform sequence design on ﬁ xed backbones.”
Protein sequence design by conformational landscape optimization (2021, methodology): “Backbones were constructed using blueprints (29) with distance and angle constraints to guide the formation of β-sheet backbone hydrogen bonds (30), followed by sequence design with Rosetta [FastDesign (28, 31) in conjunction with LayerDesign (21) and using the Rosetta all-atom energy function (32) ”
A Recurrent Mutation in Anaplastic Lymphoma Kinase with Distinct Neoepitope Conformations (2018, methodology): “RosettaRemodel: a generalized framework for flexible backbone protein design.”
Generative modeling for protein structures (2018, methodology): “We also baseline the GAN inpainting method with RosettaRemodel [37], which uses fragment sampling to do loop closure, followed by a sequence design process, guided by a heuristic energy score.”
De novo protein design, a retrospective (2020, methodology): “This was realized in RosettaRemodel (Huang et al., 2011), which provided the framework for a wide range of design problems including: the insertion, deletion, and remodeling of loops; design of disulfides; input of symmetry operators, and various other aspects of de novo design.”
The coming of age of de novo protein design (2016, background): “First, an overall topology ‘blueprint’ 29 that is consistent with the backbone design principles is created to specify the lengths, packing arrangement and order of the constituent α-helices and β-strands, as well as the lengths of the connecting loops.”
Perturbing the energy landscape for improved packing during computational protein design (2020, positive): “For seven of the eight pools, we performed step (a) using a “blueprint”-based approach.(24,25) A blueprint encodes several aspects of the desired protein structure, including: the length, secondary structure at each position, and pairings or register shifts between secondary structures.”
Fully differentiable full-atom protein backbone generation (2019, background): “Current state-of-the-art methods for backbone generation rely on either sampling or assembling native backbone fragments (15; 16).”
Deep learning techniques have significantly impacted protein structure prediction and protein design. (2021, methodology): “Following sequence design, local structure optimization is performed and the sequence design/back-bone optimization is iteratively repeated [41].”
Design of a hyperstable 60-subunit protein icosahedron (2016, positive): “RosettaRemodel 41 was used to model I3-01(ctGFP) and to generate ideal linker lengths for I3-01(HB).”