De Novo Enzyme Design Using Rosetta3

By Florian Richter, Andrew Leaver-Fay, Sagar D. Khare, S. Bjelic, David Baker. PLoS ONE, 2011

337 citations recorded by the source corpus.

The Rosetta de novo enzyme design protocol has been used to design enzyme catalysts for a variety of chemical reactions, and in principle can be applied to any arbitrary chemical reaction of interest, The process has four stages: 1) choice of a catalytic mechanism and corresponding minimal model active site, 2) identification of sites in a set of scaffold proteins where this minimal active site can be realized, 3) optimization of the identities of the surrounding residues for stabilizing interactions with the transition state and primary catalytic residues, and 4) evaluation and ranking the resulting designed sequences. Stages two through four of this process can be carried out with the Rosetta package, while stage one needs to be done externally. Here, we demonstrate how to carry out the Rosetta enzyme design protocol from start to end in detail using for illustration the triosephosphate isomerase reaction.

Subfields: Materials Chemistry, Molecular Biology, Physiology

View Paper: https://www.semanticscholar.org/paper/3734574a7010f878fb4b2526e08f66a9b4768d42

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Citations in the corpus

Sampling and energy evaluation challenges in ligand binding protein design (2017, neutral): “Once a set of hydrogen bonds that satisfy the buried polar atoms of the ligand was found, Rosetta geometric constraint files were used to keep the hydrogen bonding patterns.(22) The other pocket residues were optimized based on the calculated interface energy.”
Efficient Flexible Backbone Protein-Protein Docking for Challenging Targets (2017, background): “Once all motifs have been analyzed, the populated bins are assigned a hash value and, to minimize its memory footprint, only the hashed bins are stored (32, 55).”
Computational Design of Ligand Binding Proteins with High Affinity and Selectivity (2013, neutral): “The purpose ofthe firstwastomaximizebindingaffinityfortheligand 33 .”
RosettaRemodel: A Generalized Framework for Flexible Backbone Protein Design (2011, methodology): “RosettaRemodel uses the constraint file format from the Rosetta enzyme design protocol [41], allowing constraints to be specified in up to six degrees of freedom.”
Emerging themes in the computational design of novel enzymes and protein–protein interfaces (2013, background): “The sequence of residues in the putative catalytic pocket is then optimized to both favor maintenance of catalytic geometry and to provide additional stabilization to the transition state(s) [25].”
Computational protein design enables a novel one-carbon assimilation pathway (2015, methodology): “This model was used as input for RosettaDesign and Foldit-based optimization using previously described methods (31, 32).”
Single-sequence protein structure prediction using language models and deep learning (2022, background): “Fast predictions for large numbers of long proteins would enable many practical applications in enzymology, therapeutics and chemical engineering, including designing new functions 14–16 , raising thermostability 17 , altering pH sensitivity 18 and increasing compatibility with organic solvents 19 .”
A computational method for the design of nested proteins by loop‐directed domain insertion (2018, methodology): “In order to simultaneously optimize decoys for structure and function, the matcher application 41,53 within Rosetta was also used as a filter for decoy structures after the LooDo placement algorithm and before loop closure in order to focus the structure optimization steps on Hits capable of…”
A Pareto-Optimal Refinement Method for Protein Design Scaffolds (2013, methodology): “The relax protocol was run on each protein in the set and 50 independent sequence design runs were carried out on each structure (using the ‘‘enzyme design’’ procedure in Rosetta [13]).”
Single-sequence protein structure prediction using language models from deep learning (2021, background): “Reducing this cost would enable many practical applications in enzymology and therapeutics including designing new functions 14–16 , raising thermostability 17 , altering pH sensitivity 18 , and increasing compatibility with organic solvents 19 .”
AlphaDesign: A de novo protein design framework based on AlphaFold (2021, background): “These applications range from the ﬁrst de novo designed proteins [48] to synthetic vaccines [49, 50], complex assemblies [51, 52] and enzymes [53, 54].”
RosettaAntibodyDesign (RAbD): A general framework for computational antibody design (2017, methodology): “Rosetta has been developed and used for a variety of modeling and design tasks, such as loop modeling [57,58], protein–protein docking [45,59], structure refinement [60–63], de novo protein design [64], enzyme design [65–67], and interface design [68–70].”
Rosetta FunFolDes – A general framework for the computational design of functional proteins (2018, background): “This is a main distinctive point from other approaches to design functional proteins that rely on a mostly rigid scaffold (Azoitei et al., 2011; Correia et al., 2010; Fallas et al., 2017; Hill et al., 2000; Joh et al., 2014; Richter et al., 2011).”
Automating human intuition for protein design (2014, methodology): “16 The final greedy optimization Rosetta protocol (in RosettaScripts format18 with an example run in Supporting Information Appendix C) for recapitulation of human design intervention is available in the standard Rosetta package: Rosetta/main/ source/src/apps/public/enzdes/ES10_broad2.xml”