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Principles of Protein Stability and Their Application in Computational Design

Annual review of biochemistry, 2018-06, Vol.87 (1), p.105-129

2018

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Automated structure refinement of macromolecular assemblies from cryo-EM maps using Rosetta

eLife, 2016-09, Vol.5

2016

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Engineering an improved light-induced dimer (iLID) for controlling the localization and activity of signaling proteins

Proceedings of the National Academy of Sciences - PNAS, 2015-01, Vol.112 (1), p.112-117

2015

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Perturbing the energy landscape for improved packing during computational protein design

Proteins, structure, function, and bioinformatics, 2021-04, Vol.89 (4), p.436-449

2021

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Computational Design of Structured and Functional Peptide Macrocycles

Methods in molecular biology (Clifton, N.J.), 2022, Vol.2371, p.63

2022

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Protein structure prediction using Rosetta in CASP12

Proteins, structure, function, and bioinformatics, 2018-03, Vol.86 (S1), p.113-121

2018

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Identifying the amylome, proteins capable of forming amyloid-like fibrils

Proceedings of the National Academy of Sciences - PNAS, 2010-02, Vol.107 (8), p.3487-3492

2010

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A comparison of successful and failed protein interface designs highlights the challenges of designing buried hydrogen bonds

Protein science, 2013-01, Vol.22 (1), p.74-82

2013

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Consistent blind protein structure generation from NMR chemical shift data

Proceedings of the National Academy of Sciences - PNAS, 2008-03, Vol.105 (12), p.4685-4690

2008

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Modeling membrane geometries implicitly in Rosetta

Protein science, 2024-03, Vol.33 (3), p.e4908-n/a

2024

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Assessing and enhancing foldability in designed proteins

Protein science, 2022-09, Vol.31 (9), p.e4400-n/a

2022

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Principles for computational design of binding antibodies

Proceedings of the National Academy of Sciences - PNAS, 2017-10, Vol.114 (41), p.10900-10905

2017

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RosettaDDGPrediction for high‐throughput mutational scans: From stability to binding

Protein science, 2023-01, Vol.32 (1), p.e4527-n/a

2023

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Normal‐mode driven exploration of protein domain motions

Journal of computational chemistry, 2021-12, Vol.42 (31), p.2250-2257

Sanejouand, Yves‐Henri

2021

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Accurate positioning of functional residues with robotics-inspired computational protein design

Proceedings of the National Academy of Sciences - PNAS, 2022-03, Vol.119 (11), p.e2115480119-e2115480119

2022

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Enzyme stabilization via computationally guided protein stapling

Proceedings of the National Academy of Sciences - PNAS, 2017-11, Vol.114 (47), p.12472-12477

2017

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Automated de novo prediction of native-like RNA tertiary structures

Proceedings of the National Academy of Sciences - PNAS, 2007-09, Vol.104 (37), p.14664-14669

2007

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A flagellate-to-amoeboid switch in the closest living relatives of animals

eLife, 2021-01, Vol.10

2021

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Stabilizing proteins, simplified: A Rosetta‐based webtool for predicting favorable mutations

Protein science, 2022-10, Vol.31 (10), p.e4428-n/a

2022

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De novo design of stable proteins that efficaciously inhibit oncogenic G proteins

Protein science, 2023-08, Vol.32 (8), p.e4713-n/a

2023

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Expression and purification of soluble and functional fusion protein DAB389IL‐2 into the E. coli strain Rosetta‐gami (DE3)

Biotechnology and applied biochemistry, 2020-03, Vol.67 (2), p.206-212

2020

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Integrating comparative modeling and accelerated simulations reveals conformational and energetic basis of actomyosin force generation

Proceedings of the National Academy of Sciences - PNAS, 2023-02, Vol.120 (9), p.e2215836120-e2215836120

2023

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Advances in the Computational Design of Small-Molecule-Controlled Protein-Based Circuits for Synthetic Biology

Proceedings of the IEEE, 2022-05, Vol.110 (5), p.659-674

2022

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Scaffold Matcher: A CMA-ES based algorithm for identifying hotspot aligned peptidomimetic scaffolds

Proteins, structure, function, and bioinformatics, 2024-03, Vol.92 (3), p.343-355

2024

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AlphaFold accurately predicts distinct conformations based on the oligomeric state of a de novo designed protein

Protein science, 2022-07, Vol.31 (7), p.e4368-n/a

2022

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Principles of Protein Stability and Their Application in Computational Design

Automated structure refinement of macromolecular assemblies from cryo-EM maps using Rosetta

Engineering an improved light-induced dimer (iLID) for controlling the localization and activity of signaling proteins

Perturbing the energy landscape for improved packing during computational protein design

Computational Design of Structured and Functional Peptide Macrocycles

Protein structure prediction using Rosetta in CASP12

Identifying the amylome, proteins capable of forming amyloid-like fibrils

A comparison of successful and failed protein interface designs highlights the challenges of designing buried hydrogen bonds

Consistent blind protein structure generation from NMR chemical shift data

Modeling membrane geometries implicitly in Rosetta

Assessing and enhancing foldability in designed proteins

Principles for computational design of binding antibodies

RosettaDDGPrediction for high‐throughput mutational scans: From stability to binding

Normal‐mode driven exploration of protein domain motions

Accurate positioning of functional residues with robotics-inspired computational protein design

Enzyme stabilization via computationally guided protein stapling

Automated de novo prediction of native-like RNA tertiary structures

A flagellate-to-amoeboid switch in the closest living relatives of animals

Stabilizing proteins, simplified: A Rosetta‐based webtool for predicting favorable mutations

De novo design of stable proteins that efficaciously inhibit oncogenic G proteins

Expression and purification of soluble and functional fusion protein DAB389IL‐2 into the E. coli strain Rosetta‐gami (DE3)

Integrating comparative modeling and accelerated simulations reveals conformational and energetic basis of actomyosin force generation

Advances in the Computational Design of Small-Molecule-Controlled Protein-Based Circuits for Synthetic Biology

Scaffold Matcher: A CMA-ES based algorithm for identifying hotspot aligned peptidomimetic scaffolds

AlphaFold accurately predicts distinct conformations based on the oligomeric state of a de novo designed protein

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