Global analysis of protein folding using massively parallel design, synthesis, and testing
By G. Rocklin, T. M. Chidyausiku, I. Goreshnik, Alex Ford, S. Houliston, A. Lemak, Lauren P. Carter, Rashmi Ravichandran, Vikram Khipple Mulligan, A. Chevalier and 2 more. 2017
497 citations recorded by the source corpus.
Subfields: Biotechnology, Molecular Biology, Computational Biology & Bioinformatics
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In Biotechnology
In Molecular Biology
- David Baker
- Tanja Kortemme
- Yang Zhang
- Xingjie Pan
- Yaoqi Zhou
- Torsten Schwede
- Jinbo Xu
- Kevin Kaichuang Yang
In Computational Biology & Bioinformatics
- David Baker
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- Tanja Kortemme
- Torsten Schwede
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- David E. Kim
- Alexander Rives
Citations in the corpus
- Generating functional protein variants with variational autoencoders (2020, neutral): “When knowledge 37 of the protein structure is available, computer aided rational design can help identify interesting 38 modifications [3].”
- Evaluating Protein Transfer Learning with TAPE (2019, methodology): “(Labeling) This approach for observing stability is powerful because of its throughput, allowing the authors to find the most stable proteins ever observed for certain classes [45].”
- Learning inverse folding from millions of predicted structures (2022, background): “De novo mini-proteins Rocklin et al. (2017) performed deep mutational scans across a set of de novo designed mini-proteins with 10 different folds measuring the stability in response to point mutations.”
- Protein sequence design by conformational landscape optimization (2021, methodology): “Second, compared with Rosetta-based designs and native proteins, designs made with trRosetta have more ideal local sequence–structure relationships [an important determinant of design success (20, 23)] (Fig.”
- Mega-scale experimental analysis of protein folding stability in biology and design (2023, methodology): “We developed cDNA display proteolysis to efficiently measure folding stability using next-generation sequencing, following similar principles to the yeast display proteolysis method 18 .”
- Continuous Automated Model EvaluatiOn (CAMEO) complementing the critical assessment of structure prediction in CASP12 (2018, background): “Recent examples range from data driven protein design[1] to combining genetic diversity studies with comparative structural analyses[2] to elucidating the structure and function of the nucleopore complex protein Nup82 in a hybrid modeling approach[3] and contributions to the structure elucidation…”