Algorithm discovery by protein folding game players

By Firas Khatib, Seth Cooper, Michael D. Tyka, Kefan Xu, I. Makedon, Zoran Popovic, David Baker, Foldit players. 2011

554 citations recorded by the source corpus.

Subfields: Computer Science Applications, Computer Vision and Pattern Recognition, Sociology and Political Science

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Tal Yarkoni

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Citations in the corpus

Scientific Benchmarks for Guiding Macromolecular Energy Function Improvement (2013, methodology): “Then, for each candidate energy function, we relaxed each protein chain with the FastRelax protocol in Rosetta (Khatib et al., 2011).”
An Introduction to Social Media for Scientists (2013, background): “, Foldit for protein structure [4]) assist scientists by allowing members of the general public to make unique and meaningful contributions to ongoing research projects.”
Repertoire Analysis of Antibody CDR-H3 Loops Suggests Affinity Maturation Does Not Typically Result in Rigidification (2017, methodology): “Antibody structural ensembles with 10 members were generated using either the Rosetta FastRelax 187 (47, 48) or Rosetta KIC protocol (49).”
A Recurrent Mutation in Anaplastic Lymphoma Kinase with Distinct Neoepitope Conformations (2018, methodology): “(2) We performed local refinement of the MHC-binding groove and the peptide using Rosetta’s relax protocol (81), which allows the region of focus to be in the local optimum of the Rosetta force field.”
From brain maps to cognitive ontologies: informatics and the search for mental structure (2015, methodology): “One largely unexplored approach in this area is the kind of “gamification” successfully achieved in other domains using platforms such as Foldit (Khatib et al. 2011) and EyeWire (http://www.eyewire.org).”
Better together: Elements of successful scientific software development in a distributed collaborative community (2020, background): “Foldit is widely used by the general public to solve real scientific puzzles[31], as a teaching tool for education levels from K-12 to graduate school[32], and by academic labs to gain insights and inspire new scientific discoveries.”
Computationally Designed Bispecific Antibodies Using Negative State Repertoires (2016, methodology): “We modeled these mutations in Rosetta using the FastRelax protocol (Khatib et al., 2011) and found that B_Q347R could escape its unfavorable interaction with a_K360 by forming a salt bridge with the neighboring b_E345; Rosetta predicted that B_E345R would prevent B_Q347R’s escape.”
Protein sequence design with a learned potential (2020, methodology): “Backbone relaxes for designs were done with the RosettaRelax application [107, 108, 109, 110], with the ex1 and ex2 options for extra χ 1 and χ 2 rotamer sampling.”
Reconfigurable asymmetric protein assemblies through implicit negative design (2021, methodology): “After removal after the aligned clashing strand, the docked strand was trimmed at N and/or C terminus in order to remove potential clashes and subsequently minimized using Rosetta FastRelax (34) to optimize backbone to backbone hydrogen bonds.”