De novo protein design by citizen scientists

By B. Koepnick, Jeff Flatten, Tamir Husain, Alex Ford, Daniel‐Adriano Silva, M. J. Bick, Aaron Bauer, Gaohua Liu, Y. Ishida, A. Boykov and 11 more. 2019

131 citations recorded by the source corpus.

Online citizen science projects such as GalaxyZoo1, Eyewire2 and Phylo3 have proven very successful for data collection, annotation and processing, but for the most part have harnessed human pattern-recognition skills rather than human creativity. An exception is the game EteRNA4, in which game players learn to build new RNA structures by exploring the discrete two-dimensional space of Watson–Crick base pairing possibilities. Building new proteins, however, is a more challenging task to present in a game, as both the representation and evaluation of a protein structure are intrinsically three-dimensional. We posed the challenge of de novo protein design in the online protein-folding game Foldit5. Players were presented with a fully extended peptide chain and challenged to craft a folded protein structure and an amino acid sequence encoding that structure. After many iterations of player design, analysis of the top-scoring solutions and subsequent game improvement, Foldit players can now—starting from an extended polypeptide chain—generate a diversity of protein structures and sequences that encode them in silico. One hundred forty-six Foldit player designs with sequences unrelated to naturally occurring proteins were encoded in synthetic genes; 56 were found to be expressed and soluble in Escherichia coli, and to adopt stable monomeric folded structures in solution. The diversity of these structures is unprecedented in de novo protein design, representing 20 different folds—including a new fold not observed in natural proteins. High-resolution structures were determined for four of the designs, and are nearly identical to the player models. This work makes explicit the considerable implicit knowledge that contributes to success in de novo protein design, and shows that citizen scientists can discover creative new solutions to outstanding scientific challenges such as the protein design problem. Proteins designed de novo by players of the online protein-folding game Foldit can be expressed in Escherichia coli and adopt the designed structure in solution.

Subfields: Biotechnology, Ecology, Molecular Biology

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Citations in the corpus

Improved protein structure prediction using predicted interresidue orientations (2019, positive): “We collected a set of 18 de novo designed proteins of various topologies (24-26) (α, β and α/β) with coordinates in the PDB and a set of 79 natural proteins of similar size selected from the CAMEO set and ran trRosetta protocol to predict inter-residue geometries (Fig.”
Critical Assessment of Methods of Protein Structure Prediction (CASP) – Round XIII (2019, methodology): “So far one experimental structure has been obtained.(38) A new round of modeling is now beginning, using the new free modeling methods from CASP13.”
Protein sequence design by conformational landscape optimization (2021, positive): “To test this, we collected a set of 4,204 monomeric proteins designed by Foldit Players, who compete to optimize the Rosetta energy of designed structures without the aid of energy landscape calculations (16).”
Protein sequence design with a learned potential (2020, background): “Computational protein design has emerged as a powerful tool to expand the space of known protein folds [1, 2, 3, 4, 5, 6], create variations on existing topologies [7, 8, 9, 10, 11], and access the vast space of sequences yet to be traversed by evolution [12].”
Deep Learning in Protein Structural Modeling and Design (2020, background): “One suitable protein modeling problem for an RL algorithm would be training an artificial intelligence (AI) agent to make a series of ‘‘moves’’ to fold a protein, similar to the Foldit game.(234,235) Such studies are still rare and previous attempts have focused on folding the 2D hydrophobic-polar m”
Recent advances in de novo protein design: Principles, methods, and applications (2021, methodology): “Automatic computational tools have enabled nonexperts to accurately design well-folded de novo proteins (71).”
Reconfigurable asymmetric protein assemblies through implicit negative design (2021, unknown): “De novo designed protein scaffolds created by Foldit players ( 22 ) were expanded with C-terminal polyvaline helices using blueprint-based backbone generation ( 24 , 25 ).”