De novo design of potent and selective mimics of IL-2 and IL-15

Silva, Daniel‐Adriano; Yu, Shawn; Ulge, Umut Y.; Spangler, Jamie B.; Jude, Kevin; Labão-Almeida, Carlos; Ali, Lestat; Quijano‐Rubio, Alfredo; Ruterbusch, Mikel; Leung, Isabel; Biary, Tamara; Crowley, Stephanie J; Marcos, Enrique Sánchez; Walkey, Carl; Weitzner, Brian D.; Pardo‐Ávila, Fátima; Castellanos, Javier; Carter, Lauren; Stewart, Lance; Riddell, Stanley R.; Pepper, Marion; Bernardes, Goncalo Lopes; Dougan, Michael; García, K. Christopher; Baker, David

doi:10.1038/s41586-018-0830-7

Cited by 427 publications

(356 citation statements)

References 85 publications

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“…Protein structures from this increasingly rich repertoire, determined either experimentally or computationally, may be used as templates or scaffolds for designing artificial proteins with many useful medical applications . Designing large artificial multiprotein assemblies has also been attempted, but remains considerably more challenging .…”

Section: Introductionmentioning

confidence: 99%

“…22 Protein structures from this increasingly rich repertoire, determined either experimentally or computationally, may be used as templates or scaffolds for designing artificial proteins with many useful medical applications. [23][24][25] Designing large artificial multiprotein assemblies has also been attempted, but remains considerably more challenging. 26 Modeling natural higher order protein assemblies is likewise difficult and involves sophisticated hybrid modeling techniques, 27,28 which integrate sequence and structural information on individual proteins with various other types of data.…”

Section: Introductionmentioning

confidence: 99%

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Blind prediction of homo‐ and hetero‐protein complexes: The CASP13‐CAPRI experiment

et al. 2019

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We present the results for CAPRI Round 46, the third joint CASP‐CAPRI protein assembly prediction challenge. The Round comprised a total of 20 targets including 14 homo‐oligomers and 6 heterocomplexes. Eight of the homo‐oligomer targets and one heterodimer comprised proteins that could be readily modeled using templates from the Protein Data Bank, often available for the full assembly. The remaining 11 targets comprised 5 homodimers, 3 heterodimers, and two higher‐order assemblies. These were more difficult to model, as their prediction mainly involved “ab‐initio” docking of subunit models derived from distantly related templates. A total of ~30 CAPRI groups, including 9 automatic servers, submitted on average ~2000 models per target. About 17 groups participated in the CAPRI scoring rounds, offered for most targets, submitting ~170 models per target. The prediction performance, measured by the fraction of models of acceptable quality or higher submitted across all predictors groups, was very good to excellent for the nine easy targets. Poorer performance was achieved by predictors for the 11 difficult targets, with medium and high quality models submitted for only 3 of these targets. A similar performance “gap” was displayed by scorer groups, highlighting yet again the unmet challenge of modeling the conformational changes of the protein components that occur upon binding or that must be accounted for in template‐based modeling. Our analysis also indicates that residues in binding interfaces were less well predicted in this set of targets than in previous Rounds, providing useful insights for directions of future improvements.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Blind prediction of homo‐ and hetero‐protein complexes: The CASP13‐CAPRI experiment

et al. 2019

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“…These steps are iterated through cycles of simulated annealing, during which the weight of the repulsive component of the Lennard‐Jones potential is increased stepwise to enable amino acid changes that may introduce unfavorable clashes but can be subsequently relaxed in the minimization step. FastDesign has been used in a variety of application to design new functions …”

Section: Resultsmentioning

confidence: 99%

“…Highly stable, idealized folds can be designed de novo, but design of proteins with new functions remains challenging. In most cases where new functions have been designed computationally, the designed protein is modeled on natural “scaffold” proteins with minimal changes in backbone conformation, although there are recent notable exceptions of functions designed into de novo proteins . Moreover, attaining sufficiently high activities typically requires optimization of the desired function by directed evolution .…”

Section: Introductionmentioning

confidence: 99%

“…In most cases where new functions have been designed computationally, the designed protein is modeled on natural "scaffold" proteins with minimal changes in backbone conformation, [11][12][13][14][15] although there are recent notable exceptions of functions designed into de novo proteins. 16,17 Moreover, attaining sufficiently high activities typically requires optimization of the desired function by directed evolution. 14,15,18 Function often depends on hydrogen bonds, which require precise backbone and side chain geometry, which remains difficult to design 19 especially when a novel function requires "reshaping" of an existing protein conformation.…”

Section: Introductionmentioning

confidence: 99%

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Comparison of Rosetta flexible‐backbone computational protein design methods on binding interactions

Loshbaugh

Kortemme

2019

Proteins

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Computational design of binding sites in proteins remains difficult, in part due to limitations in our current ability to sample backbone conformations that enable precise and accurate geometric positioning of side chains during sequence design. Here we present a benchmark framework for comparison between flexible‐backbone design methods applied to binding interactions. We quantify the ability of different flexible backbone design methods in the widely used protein design software Rosetta to recapitulate observed protein sequence profiles assumed to represent functional protein/protein and protein/small molecule binding interactions. The CoupledMoves method, which combines backbone flexibility and sequence exploration into a single acceptance step during the sampling trajectory, better recapitulates observed sequence profiles than the BackrubEnsemble and FastDesign methods, which separate backbone flexibility and sequence design into separate acceptance steps during the sampling trajectory. Flexible‐backbone design with the CoupledMoves method is a powerful strategy for reducing sequence space to generate targeted libraries for experimental screening and selection.

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Wet Lab Goes Virtual: In Silico Tools, ELN s, and Big Data Help Scientists Generate and Analyze Wet‐lab Data

Lee

Choi

2021

Digital Transformation of the Laboratory

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De novo design of potent and selective mimics of IL-2 and IL-15

Cited by 427 publications

References 85 publications

Blind prediction of homo‐ and hetero‐protein complexes: The CASP13‐CAPRI experiment

Blind prediction of homo‐ and hetero‐protein complexes: The CASP13‐CAPRI experiment

Comparison of Rosetta flexible‐backbone computational protein design methods on binding interactions

Wet Lab Goes Virtual: In Silico Tools, ELN s, and Big Data Help Scientists Generate and Analyze Wet‐lab Data

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